using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

Tutorial for working with the KITTI odometry dataset in Python with OpenCV. Includes a review of Computer Vision fundamentals.

import pyttsx3 import speech_recognition as sr import datetime from datetime import date import calendar import time import math import wikipedia import webbrowser import os import smtplib import winsound import pyautogui import cv2 from pygame import mixer from tkinter import * import tkinter.messagebox as message from sqlite3 import * conn = connect("voice_assistant_asked_questions.db") conn.execute("CREATE TABLE IF NOT EXISTS `voicedata`(id INTEGER PRIMARY KEY AUTOINCREMENT,command VARCHAR(201))") conn.execute("CREATE TABLE IF NOT EXISTS `review`(id INTEGER PRIMARY KEY AUTOINCREMENT, review VARCHAR(50), type_of_review VARCHAR(50))") conn.execute("CREATE TABLE IF NOT EXISTS `emoji`(id INTEGER PRIMARY KEY AUTOINCREMENT,emoji VARCHAR(201))") global query engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) def speak(audio): engine.say(audio) engine.runAndWait() def wishMe(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour<12: speak("Good Morning!") elif hour >= 12 and hour < 18: speak("Good Afternoon!") else: speak("Good Evening!") speak("I am voice assistant Akshu2020 Sir. Please tell me how may I help you.") def takeCommand(): global query r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 0.9 audio = r.listen(source) try: print("Recognizing...") query = r.recognize_google(audio,language='en-in') print(f"User said: {query}\n") except Exception as e: #print(e) print("Say that again please...") #speak('Say that again please...') return "None" return query def calculator(): global query try: if 'add' in query or 'edi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to add') b = float(input("Enter another number to add:")) c = a+b print(f"{a} + {b} = {c}") speak(f'The addition of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sub' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to subtract') b = float(input("Enter another number to subtract:")) c = a-b print(f"{a} - {b} = {c}") speak(f'The subtraction of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'mod' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number') b = float(input("Enter another number:")) c = a%b print(f"{a} % {b} = {c}") speak(f'The modular division of {a} and {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'div' in query: speak('Enter a number as dividend') a = float(input("Enter a number:")) speak('Enter another number as divisor') b = float(input("Enter another number as divisor:")) c = a/b print(f"{a} / {b} = {c}") speak(f'{a} divided by {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'multi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to multiply') b = float(input("Enter another number to multiply:")) c = a*b print(f"{a} x {b} = {c}") speak(f'The multiplication of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square root' in query: speak('Enter a number to find its sqare root') a = float(input("Enter a number:")) c = a**(1/2) print(f"Square root of {a} = {c}") speak(f'Square root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**2 print(f"{a} x {a} = {c}") speak(f'Square of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube root' in query: speak('Enter a number to find its cube root') a = float(input("Enter a number:")) c = a**(1/3) print(f"Cube root of {a} = {c}") speak(f'Cube root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**3 print(f"{a} x {a} x {a} = {c}") speak(f'Cube of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'fact' in query: try: n = int(input('Enter the number whose factorial you want to find:')) fact = 1 for i in range(1,n+1): fact = fact*i print(f"{n}! = {fact}") speak(f'{n} factorial is equal to {fact}. Your answer is {fact}.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: #print(e) speak('I unable to calculate its factorial.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'power' in query or 'raise' in query: speak('Enter a number whose power you want to raised') a = float(input("Enter a number whose power to be raised :")) speak(f'Enter a raised power to {a}') b = float(input(f"Enter a raised power to {a}:")) c = a**b print(f"{a} ^ {b} = {c}") speak(f'{a} raise to the power {b} = {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'percent' in query: speak('Enter a number whose percentage you want to calculate') a = float(input("Enter a number whose percentage you want to calculate :")) speak(f'How many percent of {a} you want to calculate?') b = float(input(f"Enter how many percentage of {a} you want to calculate:")) c = (a*b)/100 print(f"{b} % of {a} is {c}") speak(f'{b} percent of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'interest' in query: speak('Enter the principal value or amount') p = float(input("Enter the principal value (P):")) speak('Enter the rate of interest per year') r = float(input("Enter the rate of interest per year (%):")) speak('Enter the time in months') t = int(input("Enter the time (in months):")) interest = (p*r*t)/1200 sint = round(interest) fv = round(p + interest) print(f"Interest = {interest}") print(f"The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {p + interest}.") speak(f'interest is {sint}. The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {fv}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'si' in query: speak('Enter the angle in degree to find its sine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.sin(b) speak('Here is your answer.') print(f"sin({a}) = {c}") speak(f'sin({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cos' in query: speak('Enter the angle in degree to find its cosine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.cos(b) speak('Here is your answer.') print(f"cos({a}) = {c}") speak(f'cos({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cot' in query or 'court' in query: try: speak('Enter the angle in degree to find its cotangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.tan(b) speak('Here is your answer.') print(f"cot({a}) = {c}") speak(f'cot({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print("infinity") speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'tan' in query or '10' in query: speak('Enter the angle in degree to find its tangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.tan(b) speak('Here is your answer.') print(f"tan({a}) = {c}") speak(f'tan({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cosec' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'caus' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sec' in query: try: speak('Enter the angle in degree to find its secant value') a = int(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.cos(b) speak('Here is your answer.') print(f"sec({a}) = {c}") speak(f'sec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: speak('I unable to do this calculation.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') def callback(r,c): global player if player == 'X' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='X',fg='blue', bg='white') states[r][c] = 'X' player = 'O' if player == 'O' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='O',fg='red', bg='yellow') states[r][c] = 'O' player = 'X' check_for_winner() def check_for_winner(): global stop_game global root for i in range(3): if states[i][0] == states[i][1]== states[i][2]!=0: b[i][0].config(bg='grey') b[i][1].config(bg='grey') b[i][2].config(bg='grey') stop_game = True root.destroy() for i in range(3): if states[0][i] == states[1][i] == states[2][i]!= 0: b[0][i].config(bg='grey') b[1][i].config(bg='grey') b[2][i].config(bg='grey') stop_game = True root.destroy() if states[0][0] == states[1][1]== states[2][2]!= 0: b[0][0].config(bg='grey') b[1][1].config(bg='grey') b[2][2].config(bg='grey') stop_game = True root.destroy() if states[2][0] == states[1][1] == states[0][2]!= 0: b[2][0].config(bg='grey') b[1][1].config(bg='grey') b[0][2].config(bg='grey') stop_game = True root.destroy() def sendEmail(to,content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('xyz123@gmail.com','password') server.sendmail('xyz123@gmail.com',to,content) server.close() def brightness(): try: query = takeCommand().lower() if '25' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1610,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '50' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1684,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '75' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1758,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '100' in query or 'full' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1835,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') else: speak('Please select 25, 50, 75 or 100....... Say again.') brightness() except exception as e: #print(e) speak('Something went wrong') def close_window(): try: if 'y' in query: pyautogui.moveTo(1885,10) pyautogui.click() else: speak('ok') pyautogui.moveTo(1000,500) except exception as e: #print(e) speak('error') def whatsapp(): query = takeCommand().lower() if 'y' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') time.sleep(2) pyautogui.press('enter') time.sleep(2) pyautogui.moveTo(100,140) pyautogui.click() speak('To whom you want to send message,.....just write the name here in 5 seconds') time.sleep(7) pyautogui.moveTo(120,300) pyautogui.click() time.sleep(1) pyautogui.moveTo(800,990) pyautogui.click() speak('Say the message,....or if you want to send anything else,...say send document, or say send emoji') query = takeCommand() if ('sent' in query or 'send' in query) and 'document' in query: pyautogui.moveTo(660,990) pyautogui.click() time.sleep(1) pyautogui.moveTo(660,740) pyautogui.click() speak('please select the document within 10 seconds') time.sleep(12) speak('Should I send this document?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the document......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('document' in query or 'message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') elif ('sent' in query or 'send' in query) and 'emoji' in query: pyautogui.moveTo(620,990) pyautogui.click() pyautogui.moveTo(670,990) pyautogui.click() pyautogui.moveTo(650,580) pyautogui.click() speak('please select the emoji within 10 seconds') time.sleep(11) speak('Should I send this emoji?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('Sending the emoji......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doublClick(x=800, y=990) speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: pyautogui.write(f'{query}') speak('Should I send this message?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the message......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: speak('ok') def alarm(): root = Tk() root.title('Akshu2020 Alarm-Clock') speak('Please enter the time in the format hour, minutes and seconds. When the alarm should rang?') speak('Please enter the time greater than the current time') def setalarm(): alarmtime = f"{hrs.get()}:{mins.get()}:{secs.get()}" print(alarmtime) if(alarmtime!="::"): alarmclock(alarmtime) else: speak('You have not entered the time.') def alarmclock(alarmtime): while True: time.sleep(1) time_now=datetime.datetime.now().strftime("%H:%M:%S") print(time_now) if time_now == alarmtime: Wakeup=Label(root, font = ('arial', 20, 'bold'), text="Wake up! Wake up! Wake up",bg="DodgerBlue2",fg="white").grid(row=6,columnspan=3) speak("Wake up, Wake up") print("Wake up!") mixer.init() mixer.music.load(r'C:\Users\Admin\Music\Playlists\wake-up-will-you-446.mp3') mixer.music.play() break speak('you can click on close icon to close the alarm window.') hrs=StringVar() mins=StringVar() secs=StringVar() greet=Label(root, font = ('arial', 20, 'bold'),text="Take a short nap!").grid(row=1,columnspan=3) hrbtn=Entry(root,textvariable=hrs,width=5,font =('arial', 20, 'bold')) hrbtn.grid(row=2,column=1) minbtn=Entry(root,textvariable=mins, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=2) secbtn=Entry(root,textvariable=secs, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=3) setbtn=Button(root,text="set alarm",command=setalarm,bg="DodgerBlue2", fg="white",font = ('arial', 20, 'bold')).grid(row=4,columnspan=3) timeleft = Label(root,font=('arial', 20, 'bold')) timeleft.grid() mainloop() def select1(): global vs global root3 global type_of_review if vs.get() == 1: message.showinfo(" ","Thank you for your review!!") review = "Very Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 2: message.showinfo(" ","Thank you for your review!!") review = "Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 3: message.showinfo(" ","Thank you for your review!!!!") review = "Neither Satisfied Nor Dissatisfied" type_of_review = "Neutral" root3.destroy() elif vs.get() == 4: message.showinfo(" ","Thank you for your review!!") review = "Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 5: message.showinfo(" ","Thank you for your review!!") review = "Very Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 6: message.showinfo(" "," Ok ") review = "I do not want to give review" type_of_review = "No review" root3.destroy() try: conn.execute(f"INSERT INTO `review`(review,type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass def select_review(): global root3 global vs global type_of_review root3 = Tk() root3.title("Select an option") vs = IntVar() string = "Are you satisfied with my performance?" msgbox = Message(root3,text=string) msgbox.config(bg="lightgreen",font = "(20)") msgbox.grid(row=0,column=0) rs1=Radiobutton(root3,text="Very Satisfied",font="(20)",value=1,variable=vs).grid(row=1,column=0,sticky=W) rs2=Radiobutton(root3,text="Satisfied",font="(20)",value=2,variable=vs).grid(row=2,column=0,sticky=W) rs3=Radiobutton(root3,text="Neither Satisfied Nor Dissatisfied",font="(20)",value=3,variable=vs).grid(row=3,column=0,sticky=W) rs4=Radiobutton(root3,text="Dissatisfied",font="(20)",value=4,variable=vs).grid(row=4,column=0,sticky=W) rs5=Radiobutton(root3,text="Very Dissatisfied",font="(20)",value=5,variable=vs).grid(row=5,column=0,sticky=W) rs6=Radiobutton(root3,text="I don't want to give review",font="(20)",value=6,variable=vs).grid(row=6,column=0,sticky=W) bs = Button(root3,text="Submit",font="(20)",activebackground="yellow",activeforeground="green",command=select1) bs.grid(row=7,columnspan=2) root3.mainloop() while True : query = takeCommand().lower() # logic for executing tasks based on query if 'wikipedia' in query: speak('Searching wikipedia...') query = query.replace("wikipedia","") results = wikipedia.summary(query, sentences=2) speak("According to Wikipedia") print(results) speak(results) elif 'translat' in query or ('let' in query and 'translat' in query and 'open' in query): webbrowser.open('https://translate.google.co.in') time.sleep(10) elif 'open map' in query or ('let' in query and 'map' in query and 'open' in query): webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'youtube' in query) or ('let' in query and 'youtube' in query and 'open' in query): webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'chrome' in query: webbrowser.open('https://www.chrome.com') time.sleep(10) elif 'weather' in query: webbrowser.open('https://www.yahoo.com/news/weather') time.sleep(3) speak('Click on, change location, and enter the city , whose whether conditions you want to know.') time.sleep(10) elif 'google map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'google' in query) or ('let' in query and 'google' in query and 'open' in query): webbrowser.open('google.com') time.sleep(10) elif ('open' in query and 'stack' in query and 'overflow' in query) or ('let' in query and 'stack' in query and 'overflow' in query and 'open' in query): webbrowser.open('stackoverflow.com') time.sleep(10) elif 'open v i' in query or 'open vi' in query or 'open vierp' in query or ('open' in query and ('r p' in query or 'rp' in query)): webbrowser.open('https://www.vierp.in/login/erplogin') time.sleep(10) elif 'news' in query: webbrowser.open('https://www.bbc.com/news/world') time.sleep(10) elif 'online shop' in query or (('can' in query or 'want' in query or 'do' in query or 'could' in query) and 'shop' in query) or('let' in query and 'shop' in query): speak('From which online shopping website, you want to shop? Amazon, flipkart, snapdeal or naaptol?') query = takeCommand().lower() if 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'flip' in query: webbrowser.open('https://www.flipkart.com') time.sleep(10) elif 'snap' in query: webbrowser.open('https://www.snapdeal.com') time.sleep(10) elif 'na' in query: webbrowser.open('https://www.naaptol.com') time.sleep(10) else: speak('Sorry sir, you have to search in browser as his shopping website is not reachable for me.') elif ('online' in query and ('game' in query or 'gaming' in query)): webbrowser.open('https://www.agame.com/games') time.sleep(10) elif 'dictionary' in query: webbrowser.open('https://www.dictionary.com') time.sleep(3) speak('Enter the word, in the search bar of the dictionary, whose defination or synonyms you want to know') time.sleep(3) elif ('identif' in query and 'emoji' in query) or ('sentiment' in query and ('analysis' in query or 'identif' in query)): speak('Please enter only one emoji at a time.') emoji = input('enter emoji here: ') if '😀' in emoji or '😃' in emoji or '😄' in emoji or '😁' in emoji or '🙂' in emoji or '😊' in emoji or '☺️' in emoji or '😇' in emoji or '🥲' in emoji: speak('happy') print('Happy') elif '😝' in emoji or '😆' in emoji or '😂' in emoji or '🤣' in emoji: speak('Laughing') print('Laughing') elif '😡' in emoji or '😠' in emoji or '🤬' in emoji: speak('Angry') print('Angry') elif '🤫' in emoji: speak('Keep quite') print('Keep quite') elif '😷' in emoji: speak('face with mask') print('Face with mask') elif '🥳' in emoji: speak('party') print('party') elif '😢' in emoji or '😥' in emoji or '😓' in emoji or '😰' in emoji or '☹️' in emoji or '🙁' in emoji or '😟' in emoji or '😔' in emoji or '😞️' in emoji: speak('Sad') print('Sad') elif '😭' in emoji: speak('Crying') print('Crying') elif '😋' in emoji: speak('Tasty') print('Tasty') elif '🤨' in emoji: speak('Doubt') print('Doubt') elif '😴' in emoji: speak('Sleeping') print('Sleeping') elif '🥱' in emoji: speak('feeling sleepy') print('feeling sleepy') elif '😍' in emoji or '🥰' in emoji or '😘' in emoji: speak('Lovely') print('Lovely') elif '😱' in emoji: speak('Horrible') print('Horrible') elif '🎂' in emoji: speak('Cake') print('Cake') elif '🍫' in emoji: speak('Cadbury') print('Cadbury') elif '🇮🇳' in emoji: speak('Indian national flag,.....Teeranga') print('Indian national flag - Tiranga') elif '💐' in emoji: speak('Bouquet') print('Bouquet') elif '🥺' in emoji: speak('Emotional') print('Emotional') elif ' ' in emoji or '' in emoji: speak(f'{emoji}') else: speak("I don't know about this emoji") print("I don't know about this emoji") try: conn.execute(f"INSERT INTO `emoji`(emoji) VALUES('{emoji}')") conn.commit() except Exception as e: #print('Error in storing emoji in database') pass elif 'time' in query: strTime = datetime.datetime.now().strftime("%H:%M:%S") print(strTime) speak(f"Sir, the time is {strTime}") elif 'open' in query and 'sublime' in query: path = "C:\Program Files\Sublime Text 3\sublime_text.exe" os.startfile(path) elif 'image' in query: path = "C:\Program Files\Internet Explorer\images" os.startfile(path) elif 'quit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'exit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'stop' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'shutdown' in query or 'shut down' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'close you' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() try: conn.execute(f"INSERT INTO `voice_assistant_review`(review, type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass elif 'bye' in query: speak('Bye Sir') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'wait' in query or 'hold' in query: speak('for how many seconds or minutes I have to wait?') query = takeCommand().lower() if 'second' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("seconds","") query = query.replace("second","") query = query.replace("on","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('Ok sir') query = int(query) time.sleep(query) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'minute' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("on","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("and","") query = query.replace("half","") query = query.replace("minutes","") query = query.replace("minute","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('ok sir') query = int(query) time.sleep(query*60) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'play' in query and 'game' in query: speak('I have 3 games, tic tac toe game for two players,....mario, and dyno games for single player. Which one of these 3 games you want to play?') query = takeCommand().lower() if ('you' in query and 'play' in query and 'with' in query) and ('you' in query and 'play' in query and 'me' in query): speak('Sorry sir, I cannot play this game with you.') speak('Do you want to continue it?') query = takeCommand().lower() try: if 'y' in query or 'sure' in query: root = Tk() root.title("TIC TAC TOE (By Akshay Khare)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() else: speak('ok sir') except Exception as e: #print(e) time.sleep(3) print('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'tic' in query or 'tac' in query: try: root = Tk() root.title("TIC TAC TOE (Rayen Kallel)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() except Exception as e: #print(e) time.sleep(3) speak('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'mar' in query or 'mer' in query or 'my' in query: webbrowser.open('https://chromedino.com/mario/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) elif 'di' in query or 'dy' in query: webbrowser.open('https://chromedino.com/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) else: speak('ok sir') elif 'change' in query and 'you' in query and 'voice' in query: engine.setProperty('voice', voices[1].id) speak("Here's an example of one of my voices. Would you like to use this one?") query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('Great. I will keep using this voice.') elif 'n' in query: speak('Ok. I am back to my other voice.') engine.setProperty('voice', voices[0].id) else: speak('Sorry, I am having trouble understanding. I am back to my other voice.') engine.setProperty('voice', voices[0].id) elif 'www.' in query and ('.com' in query or '.in' in query): webbrowser.open(query) time.sleep(10) elif '.com' in query or '.in' in query: webbrowser.open(query) time.sleep(10) elif 'getting bore' in query: speak('then speak with me for sometime') elif 'i bore' in query: speak('Then speak with me for sometime.') elif 'i am bore' in query: speak('Then speak with me for sometime.') elif 'calculat' in query: speak('Yes. Which kind of calculation you want to do? add, substract, divide, multiply or anything else.') query = takeCommand().lower() calculator() elif 'add' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '+' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'plus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'subtrac' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'minus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'multipl' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif ' x ' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'slash' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif '/' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'divi' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'trigonometr' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'percent' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '%' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'raise to ' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'simple interest' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'akshay' in query: speak('Mr. Rayen Kallel is my inventor. He is 14 years old and he is A STUDENT AT THE COLLEGE PILOTEE SFAX') elif 'your inventor' in query: speak('Mr. Rayen Kallel is my inventor') elif 'your creator' in query: speak('Mr. Rayen Kallel is my creator') elif 'invent you' in query: speak('Mr. Rayen Kallel invented me') elif 'create you' in query: speak('Mr. Rayen Kallel created me') elif 'how are you' in query: speak('I am fine Sir') elif 'write' in query and 'your' in query and 'name' in query: print('Akshu2020') pyautogui.write('Akshu2020') elif 'write' in query and ('I' in query or 'whatever' in query) and 'say' in query: speak('Ok sir I will write whatever you will say. Please put your cursor where I have to write.......Please Start speaking now sir.') query = takeCommand().lower() pyautogui.write(query) elif 'your name' in query: speak('My name is akshu2020') elif 'who are you' in query: speak('I am akshu2020') elif ('repeat' in query and ('word' in query or 'sentence' in query or 'line' in query) and ('say' in query or 'tell' in query)) or ('repeat' in query and 'after' in query and ('me' in query or 'my' in query)): speak('yes sir, I will repeat your words starting from now') query = takeCommand().lower() speak(query) time.sleep(1) speak("If you again want me to repeat something else, try saying, 'repeat after me' ") elif ('send' in query or 'sent' in query) and ('mail' in query or 'email' in query or 'gmail' in query): try: speak('Please enter the email id of receiver.') to = input("Enter the email id of reciever: ") speak(f'what should I say to {to}') content = takeCommand() sendEmail(to, content) speak("Email has been sent") except Exception as e: #print(e) speak("sorry sir. I am not able to send this email") elif 'currency' in query and 'conver' in query: speak('I can convert, US dollar into dinar, and dinar into US dollar. Do you want to continue it?') query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('which conversion you want to do? US dollar to dinar, or dinar to US dollar?') query = takeCommand().lower() if ('dollar' in query or 'US' in query) and ('dinar' in query): speak('Enter US Dollar') USD = float(input("Enter United States Dollar (USD):")) DT = USD * 0.33 dt = "{:.4f}".format(DT) print(f"{USD} US Dollar is equal to {dt} dniar.") speak(f'{USD} US Dollar is equal to {dt} dinar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) elif ('dinar' in query) and ('to US' in query or 'to dollar' in query or 'to US dollar'): speak('Enter dinar') DT = float(input("Enter dinar (DT):")) USD = DT/0.33 usd = "{:.3f}".format(USD) print(f"{DT} dinar is equal to {usd} US Dollar.") speak(f'{DT} dinar rupee is equal to {usd} US Dollar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) else: speak("I cannot understand what did you say. If you want to convert currency just say 'convert currency'") else: print('ok sir') elif 'about you' in query: speak('My name is akshu2020. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device. I am also able to send email') elif 'your intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your short intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your quick intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your brief intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you work' in query: speak('run the program and say what do you want. so that I can help you. In this way I work') elif 'your job' in query: speak('My job is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your work' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'work you' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your information' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'yourself' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'introduce you' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'description' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your birth' in query: speak('My birthdate is 6 August two thousand twenty') elif 'your use' in query: speak('I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you eat' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'your food' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'you live' in query: speak('I live in sfax, in laptop of Mr. Rayen Khare') elif 'where from you' in query: speak('I am from sfax, I live in laptop of Mr. Rayen Khare') elif 'you sleep' in query: speak('Yes, when someone close this program or stop to run this program then I sleep and again wake up when someone again run me.') elif 'what are you doing' in query: speak('Talking with you.') elif 'you communicate' in query: speak('Yes, I can communicate with you.') elif 'hear me' in query: speak('Yes sir, I can hear you.') elif 'you' in query and 'dance' in query: speak('No, I cannot dance.') elif 'tell' in query and 'joke' in query: speak("Ok, here's a joke") speak("'Write an essay on cricket', the teacher told the class. Chintu finishes his work in five minutes. The teacher is impressed, she asks chintu to read his essay aloud for everyone. Chintu reads,'The match is cancelled because of rain', hehehehe,haahaahaa,hehehehe,haahaahaa") elif 'your' in query and 'favourite' in query: if 'actor' in query: speak('sofyen chaari, is my favourite actor.') elif 'food' in query: speak('I can always go for some food for thought. Like facts, jokes, or interesting searches, we could look something up now') elif 'country' in query: speak('tunisia') elif 'city' in query: speak('sfax') elif 'dancer' in query: speak('Michael jackson') elif 'singer' in query: speak('tamino, is my favourite singer.') elif 'movie' in query: speak('baywatch, such a treat') elif 'sing a song' in query: speak('I cannot sing a song. But I know the 7 sur in indian music, saaareeegaaamaaapaaadaaanisaa') elif 'day after tomorrow' in query or 'date after tomorrow' in query: td = datetime.date.today() + datetime.timedelta(days=2) print(td) speak(td) elif 'day before today' in query or 'date before today' in query or 'yesterday' in query or 'previous day' in query: td = datetime.date.today() + datetime.timedelta(days= -1) print(td) speak(td) elif ('tomorrow' in query and 'date' in query) or 'what is tomorrow' in query or (('day' in query or 'date' in query) and 'after today' in query): td = datetime.date.today() + datetime.timedelta(days=1) print(td) speak(td) elif 'month' in query or ('current' in query and 'month' in query): current_date = date.today() m = current_date.month month = calendar.month_name[m] print(f'Current month is {month}') speak(f'Current month is {month}') elif 'date' in query or ('today' in query and 'date' in query) or 'what is today' in query or ('current' in query and 'date' in query): current_date = date.today() print(f"Today's date is {current_date}") speak(f'Todays date is {current_date}') elif 'year' in query or ('current' in query and 'year' in query): current_date = date.today() m = current_date.year print(f'Current year is {m}') speak(f'Current year is {m}') elif 'sorry' in query: speak("It's ok sir") elif 'thank you' in query: speak('my pleasure') elif 'proud of you' in query: speak('Thank you sir') elif 'about human' in query: speak('I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings.') elif 'you have feeling' in query: speak('No. I do not have feelings. I have not been programmed like this.') elif 'you have emotions' in query: speak('No. I do not have emotions. I have not been programmed like this.') elif 'you are code' in query: speak('I am coded in python programming language.') elif 'your code' in query: speak('I am coded in python programming language.') elif 'you code' in query: speak('I am coded in python programming language.') elif 'your coding' in query: speak('I am coded in python programming language.') elif 'dream' in query: speak('I wish that I should be able to answer all the questions which will ask to me.') elif 'sanskrit' in query: speak('yadaa yadaa he dharmasyaa ....... glaanirbhaavati bhaaaraata. abhyuthaanaam adhaarmaasyaa tadaa tmaanama sruujaamiyaahama') elif 'answer is wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'wrong answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incorrect answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incomplete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incomplete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is improper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not correct' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not yet complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not proper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'facebook' in query: webbrowser.open('https://www.facebook.com') time.sleep(10) elif 'youtube' in query: webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'shapeyou' in query: webbrowser.open('https://www.shapeyou.com') time.sleep(10) elif 'information about ' in query or 'informtion of ' in query: try: #speak('Searching wikipedia...') query = query.replace("information about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'information' in query: try: speak('Information about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("information","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'something about ' in query: try: #speak('Searching wikipedia...') query = query.replace("something about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'tell me about ' in query: try: #speak('Searching wikipedia...') query = query.replace("tell me about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'tell me ' in query: try: query = query.replace("tell me ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'tell me' in query: try: speak('about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'meaning of ' in query: try: #speak('Searching wikipedia...') query = query.replace("meaning of ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'meaning' in query: try: speak('meaning of what?') query = takeCommand().lower() query = query.replace("meaning of","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'means' in query: try: #speak('Searching wikipedia...') query = query.replace("it means","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'want to know ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to know that","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') status = 'Not answered' elif 'want to ask ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to ask you ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'you know ' in query: try: #speak('Searching wikipedia...') query = query.replace("you know","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'alarm' in query: alarm() elif 'bharat mata ki' in query: speak('jay') elif 'kem chhe' in query: speak('majaama') elif 'namaskar' in query: speak('Namaskaar') elif 'jo bole so nihal' in query: speak('sat shri akaal') elif 'jay hind' in query: speak('jay bhaarat') elif 'jai hind' in query: speak('jay bhaarat') elif 'how is the josh' in query: speak('high high sir') elif 'hip hip' in query: speak('Hurreh') elif 'help' in query: speak('I will try my best to help you if I have solution of your problem.') elif 'follow' in query: speak('Ok sir') elif 'having illness' in query: speak('Take care and get well soon') elif 'today is my birthday' in query: speak('many many happy returns of the day. Happy birthday.') print("🎂🎂 Happy Birthday 🎂🎂") elif 'you are awesome' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'you are great' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'tu kaun hai' in query: speak('Meraa naam akshu2020 haai.') elif 'you speak' in query: speak('Yes, I can speak with you.') elif 'speak with ' in query: speak('Yes, I can speak with you.') elif 'hare ram' in query or 'hare krishna' in query: speak('Haare raama , haare krishnaa, krishnaa krishnaa , haare haare') elif 'ganpati' in query: speak('Ganpati baappa moryaa!') elif 'laugh' in query: speak('hehehehe,haahaahaa,hehehehe,haahaahaa,hehehehe,haahaahaa') print('😂🤣') elif 'genius answer' in query: speak('No problem') elif 'you' in query and 'intelligent' in query: speak('Thank you sir') elif ' into' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif ' power' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'whatsapp' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') pyautogui.press('enter') speak('Do you want to send message to anyone through whatsapp, .....please answer in yes or no') whatsapp() elif 'wh' in query or 'how' in query: url = "https://www.google.co.in/search?q=" +(str(query))+ "&oq="+(str(query))+"&gs_l=serp.12..0i71l8.0.0.0.6391.0.0.0.0.0.0.0.0..0.0....0...1c..64.serp..0.0.0.UiQhpfaBsuU" webbrowser.open_new(url) time.sleep(2) speak('Here is your answer') time.sleep(5) elif 'piano' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query and 'instru' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query or 'turn on' in query and ('music' in query or 'song' in query) : try: music_dir = 'C:\\Users\\Admin\\Music\\Playlists' songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir, songs[0])) except Exception as e: #print(e) speak('Sorry sir, I am not able to play music') elif (('open' in query or 'turn on' in query) and 'camera' in query) or (('click' in query or 'take' in query) and ('photo' in query or 'pic' in query)): speak("Opening camera") cam = cv2.VideoCapture(0) cv2.namedWindow("test") img_counter = 0 speak('say click, to click photo.....and if you want to turn off the camera, say turn off the camera') while True: ret, frame = cam.read() if not ret: print("failed to grab frame") speak('failed to grab frame') break cv2.imshow("test", frame) query = takeCommand().lower() k = cv2.waitKey(1) if 'click' in query or ('take' in query and 'photo' in query): speak('Be ready!...... 3.....2........1..........') pyautogui.press('space') img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'escape' in query or 'off' in query or 'close' in query: pyautogui.press('esc') print("Escape hit, closing...") speak('Turning off the camera') break elif k%256 == 27: # ESC pressed print("Escape hit, closing...") break elif k%256 == 32: # SPACE pressed img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'exit' in query or 'stop' in query or 'bye' in query: speak('Please say, turn off the camera or press escape button before giving any other command') else: speak('I did not understand what did you say or you entered a wrong key.') cam.release() cv2.destroyAllWindows() elif 'screenshot' in query: speak('Please go on the screen whose screenshot you want to take, after 5 seconds I will take screenshot') time.sleep(4) speak('Taking screenshot....3........2.........1.......') pyautogui.screenshot('screenshot_by_rayen2020.png') speak('The screenshot is saved as screenshot_by_rayen2020.png') elif 'click' in query and 'start' in query: pyautogui.moveTo(10,1200) pyautogui.click() elif ('open' in query or 'click' in query) and 'calendar' in query: pyautogui.moveTo(1800,1200) pyautogui.click() elif 'minimise' in query and 'screen' in query: pyautogui.moveTo(1770,0) pyautogui.click() elif 'increase' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumeup') elif 'decrease' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumedown') elif 'capslock' in query or ('caps' in query and 'lock' in query): pyautogui.press('capslock') elif 'mute' in query: pyautogui.press('volumemute') elif 'search' in query and ('bottom' in query or 'pc' in query or 'laptop' in query or 'app' in query): pyautogui.moveTo(250,1200) pyautogui.click() speak('What do you want to search?') query = takeCommand().lower() pyautogui.write(f'{query}') pyautogui.press('enter') elif ('check' in query or 'tell' in query or 'let me know' in query) and 'website' in query and (('up' in query or 'working' in query) or 'down' in query): speak('Paste the website in input to know it is up or down') check_website_status = input("Paste the website here: ") try: status = urllib.request.urlopen(f"{check_website_status}").getcode() if status == 200: print('Website is up, you can open it.') speak('Website is up, you can open it.') else: print('Website is down, or no any website is available of this name.') speak('Website is down, or no any website is available of this name.') except: speak('URL not found') elif ('go' in query or 'open' in query) and 'settings' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('settings') pyautogui.press('enter') elif 'close' in query and ('click' in query or 'window' in query): pyautogui.moveTo(1885,10) speak('Should I close this window?') query = takeCommand().lower() close_window() elif 'night light' in query and ('on' in query or 'off' in query or 'close' in query): pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1840,620) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() elif 'notification' in query and ('show' in query or 'click' in query or 'open' in query or 'close' in query or 'on' in query or 'off' in query or 'icon' in query or 'pc' in query or 'laptop' in query): pyautogui.moveTo(1880,1050) pyautogui.click() elif ('increase' in query or 'decrease' in query or 'change' in query or 'minimize' in query or 'maximize' in query) and 'brightness' in query: speak('At what percent should I kept the brightness, 25, 50, 75 or 100?') brightness() elif '-' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'open' in query: if 'gallery' in query or 'photo' in query or 'image' in query or 'pic' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('photo') pyautogui.press('enter') elif 'proteus' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('proteus') pyautogui.press('enter') elif 'word' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('word') pyautogui.press('enter') elif ('power' in query and 'point' in query) or 'presntation' in query or 'ppt' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('ppt') pyautogui.press('enter') elif 'file' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('file') pyautogui.press('enter') elif 'edge' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('microsoft edge') pyautogui.press('enter') elif 'wps' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('wps office') pyautogui.press('enter') elif 'spyder' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('spyder') pyautogui.press('enter') elif 'snip' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('snip') pyautogui.press('enter') elif 'pycharm' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('pycharm') pyautogui.press('enter') elif 'this pc' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('this pc') pyautogui.press('enter') elif 'scilab' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('sciab') pyautogui.press('enter') elif 'autocad' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('autocad') pyautogui.press('enter') elif 'obs' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('OBS Studio') pyautogui.press('enter') elif 'android' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('android studio') pyautogui.press('enter') elif ('vs' in query or 'visual studio' in query) and 'code' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('visual studio code') pyautogui.press('enter') elif 'code' in query and 'block' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('codeblocks') pyautogui.press('enter') elif 'me the answer' in query: speak('Yes sir, I will try my best to answer you.') elif 'me answer' in query or ('answer' in query and 'question' in query): speak('Yes sir, I will try my best to answer you.') elif 'map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif 'can you' in query or 'could you' in query: speak('I will try my best if I can do that.') elif 'do you' in query: speak('I will try my best if I can do that.') elif 'truth' in query: speak('I always speak truth. I never lie.') elif 'true' in query: speak('I always speak truth. I never lie.') elif 'lying' in query: speak('I always speak truth. I never lie.') elif 'liar' in query: speak('I always speak truth. I never lie.') elif 'doubt' in query: speak('I will try my best if I can clear your doubt.') elif ' by' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'hii' in query: speak('hii sir') elif 'hey' in query: speak('hello sir') elif 'hai' in query: speak('hello sir') elif 'hay' in query: speak('hello sir') elif 'hi' in query: speak('hii Sir') elif 'hello' in query: speak('hello Sir!') elif 'kon' in query and 'aahe' in query: speak('Me eka robot aahee sir. Maazee naav akshu2020 aahee.') elif 'nonsense' in query: speak("I'm sorry sir") elif 'mad' in query: speak("I'm sorry sir") elif 'shut up' in query: speak("I'm sorry sir") elif 'nice' in query: speak('Thank you sir') elif 'good' in query or 'wonderful' in query or 'great' in query: speak('Thank you sir') elif 'excellent' in query: speak('Thank you sir') elif 'ok' in query: speak('Hmmmmmm') elif 'akshu 2020' in query: speak('yes sir') elif len(query) >= 200: speak('Your voice is pretty good!') elif ' ' in query: try: #query = query.replace("what is ","") results = wikipedia.summary(query, sentences=3) print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'a' in query or 'b' in query or 'c' in query or 'd' in query or 'e' in query or 'f' in query or 'g' in query or 'h' in query or 'i' in query or 'j' in query or 'k' in query or 'l' in query or 'm' in query or 'n' in query or 'o' in query or 'p' in query or 'q' in query or 'r' in query or 's' in query or 't' in query or 'u' in query or 'v' in query or 'w' in query or 'x' in query or 'y' in query or 'z' in query: try: results = wikipedia.summary(query, sentences = 2) print(results) speak(results) except Exception as e: speak('I unable to answer your question. ') else: speak('I unable to give answer of your question')

Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.

🚀 Real-time football video analysis using YOLOv11: ⚽🏃‍♂️🏃‍♀️ Detect players, ball, and referees with per-class heatmaps 🌡️🔥 and overlay videos 🎥. Built with Python 🐍, OpenCV 📷, Ultralytics YOLO 🧠, and Roboflow datasets 📊. Ideal for sports analytics 🏆 and automated performance review 📈.

Created a decision review system in python using tkinter library and Opencv module.

Click here to download the source code to this post In this tutorial, you will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning. Last month, I authored a blog post on detecting COVID-19 in X-ray images using deep learning. Readers really enjoyed learning from the timely, practical application of that tutorial, so today we are going to look at another COVID-related application of computer vision, this one on detecting face masks with OpenCV and Keras/TensorFlow. I was inspired to author this tutorial after: Receiving numerous requests from PyImageSearch readers asking that I write such a blog post Seeing others implement their own solutions (my favorite being Prajna Bhandary’s, which we are going to build from today) If deployed correctly, the COVID-19 mask detector we’re building here today could potentially be used to help ensure your safety and the safety of others (but I’ll leave that to the medical professionals to decide on, implement, and distribute in the wild). To learn how to create a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning, just keep reading! Looking for the source code to this post? JUMP RIGHT TO THE DOWNLOADS SECTION COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning In this tutorial, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. From there, we’ll review the dataset we’ll be using to train our custom face mask detector. I’ll then show you how to implement a Python script to train a face mask detector on our dataset using Keras and TensorFlow. We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to implement two more additional Python scripts used to: Detect COVID-19 face masks in images Detect face masks in real-time video streams We’ll wrap up the post by looking at the results of applying our face mask detector. I’ll also provide some additional suggestions for further improvement. Two-phase COVID-19 face mask detector Figure 1: Phases and individual steps for building a COVID-19 face mask detector with computer vision and deep learning using Python, OpenCV, and TensorFlow/Keras. In order to train a custom face mask detector, we need to break our project into two distinct phases, each with its own respective sub-steps (as shown by Figure 1 above): Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask We’ll review each of these phases and associated subsets in detail, but in the meantime, let’s take a look at the dataset we’ll be using to train our COVID-19 face mask detector.

miniproject for upskill knowledge & review & learn(Python & basic C++), (Opencv, Mediapipe), (basic Arduino)

A Python-based Third Umpire Decision Review System simulation using OpenCV and Tkinter. Watch cricket clips, navigate frame-by-frame, and give your own "Out" or "Not Out" decisions like a pro!

The next project is carried out using an HPE idea for the electrical engineering graduation project. The main objective is to develop an algorithm using Python and other tools such as OpenCV for the automated review of the LEDs of a switch using images or video entry.

Complete tutorial and review of python OpenCV based on "OpenCV 3.x with python by example" book, second edition.

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A Decision Review System build in Python using libraries : Tkinter, openCV

Decision Review System for Cricket game built using libraries like OpenCV, Tkinter in Python

A Third umpire Decision review system build using the python libraries Tkinter and opencv .

A review of python packages/modules such as: JSON, Logging, MQTT, SQL connector, FastAPI, numpy, opencv and, os/glob.

A Python-based cricket Decision Review System simulation using OpenCV, enabling replay analysis with frame control to mimic third umpire decisions.

in this Project the simulation of the actual Third Umpire decision review system is made using Python Modules such as OpenCV , Tkinter

DualCam Replay is a desktop video replay system built with Python, PySide6, and OpenCV, designed for multi-camera instant review and offline analysis.

Optical Character Recognition (OCR) is implemented using Python packages like Tesseract and OpenCV to scan the title of a book and open an URL for that book's review

Form OCR Automation – Built a Python + OpenCV + Tesseract system to read filled forms and sync data to Google Sheets; includes a Streamlit-based review tool for correcting OCR errors.

Dot Art Creator — A web app that transforms images into ASCII-style dot art using Python (FastAPI, OpenCV, Pillow) and allows users to save, review, and delete their creations securely.

A fun Python-based desktop GUI application that simulates the Third Umpire Decision Review System (DRS) using Tkinter, OpenCV, and Pillow. This project lets you play back a cricket video, review the footage, and give a decision — just like a real third umpire!

Developed a home security system using OpenCV for advanced face detection and tracking. Implemented automatic video recording of detected movements, saving footage for review. Utilized Python and OpenCV for robust and efficient video processing. Integrated local storage for easy access and playback of recorded videos.

Decision Review System made in Python is similar to what used in professional cricket matches. Created a user-friendly interface with Tkinter and along with Integrated OpenCV for video analysis and processing.

This Python script automates the process of detecting faces in images using OpenCV's Haar Cascades, highlighting each detected face with color-coded rectangles, and saving the processed images in a new directory for easy review and analysis.

A simple Python script for automated visual quality control that uses OpenCV to compare test images against a "golden reference" image. It automatically detects and classifies common manufacturing defects such as FLASH (excess material) and CUT (missing material), highlighting them on the image for review.

This project demonstrates the implementation of key computer vision and image processing algorithms using OpenCV. Each algorithm is implemented in Python and applied on sample image pairs to detect keypoints, match features, and analyze image structures. The results are visualized and saved for review.

This repository contains my solutions for the technical interview, covering multiple topics including Embedded C, C language, OpenCV, Python, Linux, and Arduino. The code is organized into relevant folders, with each folder corresponding to a specific topic. Please review the code and implementations based on the interview requirements.

ATLAS , ranked #48 out of 52,500+ teams in the Amazon HackOn S5, is a trust and safety prototype enhancing e-commerce integrity. Built with Python, it features image verification (OpenCV), anomaly detection (Isolation Forest), and review authenticity checks (Mistral-7B LLM via Ollama) using an agentic AI framework (LangChain with CrewAI).