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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.

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VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend D3D9 Overlay: Supported DXVA-HD: Not Supported DDraw Status: Enabled D3D Status: Enabled AGP Status: Enabled ------------- Sound Devices ------------- Description: Alto-falantes (Dispositivo de High Definition Audio) Default Sound Playback: Yes Default Voice Playback: Yes Hardware ID: HDAUDIO\FUNC_01&VEN_8384&DEV_7662&SUBSYS_104D2300&REV_1002 Manufacturer ID: 1 Product ID: 65535 Type: WDM Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 11/20/2010 02:44:24, 350208 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No --------------------- Sound Capture Devices --------------------- Description: Microfone (Dispositivo de High Definition Audio) Default Sound Capture: Yes Default Voice Capture: Yes Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail Date and Size: 11/20/2010 02:44:24, 350208 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: Microfone (Dispositivo de High Definition Audio) Default Sound Capture: No Default Voice Capture: No Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail Date and Size: 11/20/2010 02:44:24, 350208 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF ------------------- DirectInput Devices ------------------- Device Name: Mouse Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Teclado Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: USB Receiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x046D, 0xC51B FF Driver: n/a Poll w/ Interrupt: No ----------- USB Devices ----------- + USB Root Hub | Vendor/Product ID: 0x8086, 0x2830 | Matching Device ID: usb\root_hub | Service: usbhub ---------------- Gameport Devices ---------------- ------------ PS/2 Devices ------------ + Teclado Padrão PS/2 | Matching Device ID: *pnp0303 | Service: i8042prt | + Terminal Server Keyboard Driver | Matching Device ID: root\rdp_kbd | Upper Filters: kbdclass | Service: TermDD | + Mouse compatível com PS/2 | Matching Device ID: *pnp0f13 | Service: i8042prt | + Mouse compatível com HID | Vendor/Product ID: 0x046D, 0xC51B | Matching Device ID: hid_device_system_mouse | Service: mouhid | + Terminal Server Mouse Driver | Matching Device ID: root\rdp_mou | Upper Filters: mouclass | Service: TermDD ------------------------ Disk & DVD/CD-ROM Drives ------------------------ Drive: C: Free Space: 386.5 GB Total Space: 476.8 GB File System: NTFS Model: SAMSUNG HM500JI ATA Device Drive: D: Model: Optiarc DVD RW AD-7560A ATA Device Driver: c:\windows\system32\drivers\cdrom.sys, 6.01.7601.17514 (Portuguese), , 0 bytes -------------- System Devices -------------- Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A03&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&11 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 1 - 283F Device ID: PCI\VEN_8086&DEV_283F&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E0 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2830 Device ID: PCI\VEN_8086&DEV_2830&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E8 Driver: n/a Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A02&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&10 Driver: n/a Name: Intel(R) ICH8 Family SMBus Controller - 283E Device ID: PCI\VEN_8086&DEV_283E&SUBSYS_9005104D&REV_03\3&33FD14CA&0&FB Driver: n/a Name: Intel(R) ICH8M SATA AHCI Controller - 2829 Device ID: PCI\VEN_8086&DEV_2829&SUBSYS_9005104D&REV_03\3&33FD14CA&0&FA Driver: n/a Name: Mobile Intel(R) PM965/GM965/GL960/GS965 Express Processor to DRAM Controller - 2A00 Device ID: PCI\VEN_8086&DEV_2A00&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&00 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 283A Device ID: PCI\VEN_8086&DEV_283A&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D7 Driver: n/a Name: Intel(R) ICH8M LPC Interface Controller - 2815 Device ID: PCI\VEN_8086&DEV_2815&SUBSYS_9005104D&REV_03\3&33FD14CA&0&F8 Driver: n/a Name: Intel(R) ICH8M Ultra ATA Storage Controllers - 2850 Device ID: PCI\VEN_8086&DEV_2850&SUBSYS_9005104D&REV_03\3&33FD14CA&0&F9 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 2836 Device ID: PCI\VEN_8086&DEV_2836&SUBSYS_9005104D&REV_03\3&33FD14CA&0&EF Driver: n/a Name: Intel(R) 82801 PCI Bridge - 2448 Device ID: PCI\VEN_8086&DEV_2448&SUBSYS_9005104D&REV_F3\3&33FD14CA&0&F0 Driver: n/a Name: Controlador de High Definition Audio Device ID: PCI\VEN_8086&DEV_284B&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D8 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2835 Device ID: PCI\VEN_8086&DEV_2835&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D1 Driver: n/a Name: Marvell Yukon 88E8036 PCI-E Fast Ethernet Controller Device ID: PCI\VEN_11AB&DEV_4351&SUBSYS_9005104D&REV_16\4&16BEB6B9&0&00E4 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 5 - 2847 Device ID: PCI\VEN_8086&DEV_2847&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E4 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2834 Device ID: PCI\VEN_8086&DEV_2834&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D0 Driver: n/a Name: Controlador de armazenamento em massa Device ID: PCI\VEN_104C&DEV_803B&SUBSYS_9005104D&REV_00\4&3A867C58&0&1AF0 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 3 - 2843 Device ID: PCI\VEN_8086&DEV_2843&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E2 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2832 Device ID: PCI\VEN_8086&DEV_2832&SUBSYS_9005104D&REV_03\3&33FD14CA&0&EA Driver: n/a Name: Texas Instruments 1394 OHCI Compliant Host Controller Device ID: PCI\VEN_104C&DEV_803A&SUBSYS_9005104D&REV_00\4&3A867C58&0&19F0 Driver: n/a Name: Intel(R) Wireless WiFi Link 4965AGN Device ID: PCI\VEN_8086&DEV_4229&SUBSYS_11008086&REV_61\4&17422A72&0&00E2 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 2 - 2841 Device ID: PCI\VEN_8086&DEV_2841&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E1 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2831 Device ID: PCI\VEN_8086&DEV_2831&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E9 Driver: n/a Name: Texas Instruments PCI-8x12/7x12/6x12 CardBus Controller Device ID: PCI\VEN_104C&DEV_8039&SUBSYS_9005104D&REV_00\4&3A867C58&0&18F0 Driver: n/a ------------------ DirectShow Filters ------------------ DirectShow Filters: WMAudio Decoder DMO,0x00800800,1,1,WMADMOD.DLL,6.01.7601.17514 WMAPro over S/PDIF DMO,0x00600800,1,1,WMADMOD.DLL,6.01.7601.17514 WMSpeech Decoder DMO,0x00600800,1,1,WMSPDMOD.DLL,6.01.7601.17514 MP3 Decoder DMO,0x00600800,1,1,mp3dmod.dll,6.01.7600.16385 Mpeg4s Decoder DMO,0x00800001,1,1,mp4sdecd.dll,6.01.7600.16385 WMV Screen decoder DMO,0x00600800,1,1,wmvsdecd.dll,6.01.7601.17514 WMVideo Decoder DMO,0x00800001,1,1,wmvdecod.dll,6.01.7601.17514 Mpeg43 Decoder DMO,0x00800001,1,1,mp43decd.dll,6.01.7600.16385 Mpeg4 Decoder DMO,0x00800001,1,1,mpg4decd.dll,6.01.7600.16385 Nero Audible Decoder,0x00200000,1,1,NeAudible.ax,4.11.0003.0007 Nero Subpicture Decoder,0x00400000,1,1,NeSubpicture.ax,4.11.0003.0007 ArcSoft TimeShift2.0 Client Filter,0x00400000,0,1,TimeShift2.ax,1.00.0000.0015 Nero Scene Detector 2,0x00200000,2,0,NeSceneDetector.ax,4.11.0003.0007 Nero Stream Buffer Sink,0x00200000,0,0,NeSBE.ax,4.11.0003.0007 Nero Subtitle,0x00200000,1,1,NeSubtitle.ax,4.11.0003.0007 DV Muxer,0x00400000,0,0,qdv.dll,6.06.7601.17514 DV Scenes,0x00200000,1,1,NVDV.dll,3.00.0004.0000 Color Space Converter,0x00400001,1,1,quartz.dll,6.06.7601.17514 WM ASF Reader,0x00400000,0,0,qasf.dll,12.00.7601.17514 Screen Capture filter,0x00200000,0,1,wmpsrcwp.dll,12.00.7601.17514 AVI Splitter,0x00600000,1,1,quartz.dll,6.06.7601.17514 VGA 16 Color Ditherer,0x00400000,1,1,quartz.dll,6.06.7601.17514 SBE2MediaTypeProfile,0x00200000,0,0,sbe.dll,6.06.7601.17514 Arcsoft PutDataSample Filter 1.0,0x00200000,1,1,ArcPutDataSample.ax,1.00.0000.0005 CyberLink AudioCD Filter (PDVD7),0x00600000,0,1,CLAudioCD.ax,5.00.0000.4417 Nero FTC,0x00200000,1,1,NeFTC.ax,1.00.0000.0000 Microsoft DTV-DVD Video Decoder,0x005fffff,2,4,msmpeg2vdec.dll,6.01.7140.0000 AC3 Parser Filter,0x00600000,1,1,mpg2splt.ax,6.06.7601.17514 CyberLink Audio Decoder (PDVD7),0x00201000,1,1,CLAud.ax,6.01.0000.4227 StreamBufferSink,0x00200000,0,0,sbe.dll,6.06.7601.17514 Nero Resize,0x00400000,1,1,NeResize.ax,4.11.0003.0007 MJPEG Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.17514 CyberLink Audio Effect (PDVD7),0x00200000,1,1,CLAudFx.ax,6.00.0000.4111 MPEG-I Stream Splitter,0x00600000,1,2,quartz.dll,6.06.7601.17514 ArcSoft Mpeg Encoder Filter,0x00200000,2,0,ArcMpegCodec.ax,2.05.0000.0013 MPEG-2 PSI Reader Filter,0x00200000,0,0,Mpeg2PsiReader.ax,1.00.0000.0004 SAMI (CC) Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 Nero AV Synchronizer,0x00200000,1,1,NeAVSync.ax,4.11.0003.0007 VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 Nero Audio Stream Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 MPEG-2 Splitter,0x005fffff,1,0,mpg2splt.ax,6.06.7601.17514 Closed Captions Analysis Filter,0x00200000,2,5,cca.dll,6.06.7601.17514 SBE2FileScan,0x00200000,0,0,sbe.dll,6.06.7601.17514 Microsoft MPEG-2 Video Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 Nero Digital AVC Audio Encoder,0x00200000,1,2,NeNDAud.ax,4.11.0003.0007 Nero Digital AVC File Writer,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Digital AVC Video Enc,0x00200000,1,2,NeNDVid.ax,4.11.0003.0007 Nero Digital AVC Null Renderer,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Digital AVC Muxer,0x00200000,2,1,NeNDMux.ax,4.11.0003.0007 CyberLink Video/SP Decoder(PDVD7 HomeNetwork),0x00200000,2,3,CLVSD.ax,6.00.0000.3313 Arcsoft GetDataSample Filter 1.0,0x00200000,1,1,ArcGetDataSample.ax,1.00.0000.0012 ArcSoft MPEG Audio Decoder,0x00600000,1,1,mpgaudio.ax,2.04.0002.0016 Nero QuickTime(tm) Video Decoder,0x00400000,1,1,NeQTDec.ax,4.11.0003.0007 Internal Script Command Renderer,0x00800001,1,0,quartz.dll,6.06.7601.17514 MPEG Audio Decoder,0x03680001,1,1,quartz.dll,6.06.7601.17514 Nero Digital AVC Subpicture Enc,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Format Converter,0x00200000,1,1,NeroFormatConv.ax,4.11.0003.0007 Nero Overlay Mixer,0x00200000,1,1,NeOverlayMixer.ax,4.11.0003.0007 Nero MP4 Splitter,0x00600000,1,1,NeMP4Splitter.ax,4.11.0003.0007 DV Splitter,0x00600000,1,2,qdv.dll,6.06.7601.17514 HighMAT and MPV Navigator Filter,0x00200000,0,3,HMNavigator.ax,4.11.0003.0007 Video Mixing Renderer 9,0x00200000,1,0,quartz.dll,6.06.7601.17514 Nero Photo Source,0x00200000,0,1,NePhotoSource.ax,4.11.0003.0007 CyberLink Demux (PDVD7),0x00602000,1,0,CLDemuxer.ax,1.00.0000.4528 CyberLink MPEG Splitter,0x00200000,1,2,CLSplter.ax,3.01.0000.3022 ArcSoft TimeShift2.0 Server Filter,0x00200000,1,0,TimeShift2.ax,1.00.0000.0015 Nero Video Analyzer,0x00200000,2,0,NeVideoAnalyzer.ax,4.11.0003.0007 Nero ES Video Reader,0x00600000,0,1,NDParser.ax,4.11.0003.0007 CyberLink Line21 Decoder (PDVD7),0x00200000,0,2,CLLine21.ax,4.00.0000.7602 Microsoft MPEG-2 Encoder,0x00200000,2,1,msmpeg2enc.dll,6.01.7601.17514 DV Source Filter,0x00400000,0,1,NVDV.dll,3.00.0004.0000 MPEG-2 Stream Reader Filter,0x00200000,0,0,Mpeg2StreamReader.ax,1.04.0000.0000 Nero Audio CD Filter,0x00200000,0,1,NeAudCD.ax,4.11.0003.0007 Nero Video Renderer,0x00200000,1,0,NeVideoRenderer.ax,4.11.0003.0007 Nero PresentationGraphics Decoder,0x00600000,2,1,NeBDGraphic.ax,4.11.0003.0007 ACM Wrapper,0x00600000,1,1,quartz.dll,6.06.7601.17514 Video Renderer,0x00800001,1,0,quartz.dll,6.06.7601.17514 ArcSoft File Dump,0x00200000,1,0,FileDump.ax,2.00.0000.0008 MPEG-2 Video Stream Analyzer,0x00200000,0,0,sbe.dll,6.06.7601.17514 Line 21 Decoder,0x00600000,1,1,qdvd.dll,6.06.7601.17514 Nero InteractiveGraphics Decoder,0x00600000,1,1,NeBDGraphic.ax,4.11.0003.0007 Video Port Manager,0x00600000,2,1,quartz.dll,6.06.7601.17514 CyberLink Push-Mode CLStream (PDVD7),0x00200000,0,1,CLStream(PushMode).ax,1.00.0000.1627 CyberLink Audio Decoder (PDVD7 UPnP),0x00200000,1,1,CLAud.ax,6.01.0000.3816 Video Renderer,0x00400000,1,0,quartz.dll,6.06.7601.17514 Nero Sound Processor,0x00200000,1,1,NeSoundProc.ax,4.11.0003.0007 Nero Audio Sample Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 CyberLink Audio Spectrum Analyzer (PDVD7),0x00200000,1,1,CLAudSpa.ax,1.00.0000.0924 Nero Vcd Navigator,0x00600000,0,2,NeVCD.ax,4.11.0003.0007 ArcSoft VideoEffect Filter,0x00200000,1,1,ArcVideoEffect.ax,1.00.0000.0010 VPS Decoder,0x00200000,0,0,WSTPager.ax,6.06.7601.17514 WM ASF Writer,0x00400000,0,0,qasf.dll,12.00.7601.17514 Nero Mpeg2 Encoder,0x00200000,2,1,NeVCR.ax,4.11.0003.0007 VBI Surface Allocator,0x00600000,1,1,vbisurf.ax,6.01.7601.17514 ArcSoft Null Render,0x00200000,1,0,ArcNullRender.ax,1.00.0000.0001 Nero Video Stream Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 File writer,0x00200000,1,0,qcap.dll,6.06.7601.17514 iTV Data Sink,0x00600000,1,0,itvdata.dll,6.06.7601.17514 Nero FLV Splitter,0x00600000,1,1,NeFLVSplitter.ax,4.11.0003.0007 iTV Data Capture filter,0x00600000,1,1,itvdata.dll,6.06.7601.17514 CyberLink Video/SP Decoder (PDVD7),0x00602000,2,3,CLVsd.ax,8.00.0000.1918 Nero Stream Buffer Source,0x00200000,0,0,NeSBE.ax,4.11.0003.0007 Nero PS Muxer,0x00200000,1,1,NePSMuxer.ax,4.11.0003.0007 CyberLink Audio Wizard,0x00201010,1,1,CLAudWizard.ax,1.00.0000.1730 DVD Navigator,0x00200000,0,3,qdvd.dll,6.06.7601.17514 CyberLink DVD Navigator (PDVD7),0x00600000,0,3,CLNavX.ax,7.00.0000.3112 CyberLink TimeStretch Filter (PDVD7),0x00200000,1,1,clauts.ax,1.00.0000.5423 Overlay Mixer2,0x00200000,1,1,qdvd.dll,6.06.7601.17514 Cyberlink SubTitle Importor (PDVD7),0x00200000,1,1,CLSubTitle.ax,1.00.0000.1604 Nero Splitter,0x00600000,1,3,NeSplitter.ax,4.11.0003.0007 Nero Deinterlace,0x00200000,1,1,NeDeinterlace.ax,4.11.0003.0007 AVI Draw,0x00600064,9,1,quartz.dll,6.06.7601.17514 RDP DShow Redirection Filter,0xffffffff,1,0,DShowRdpFilter.dll, Nero File Source / Splitter,0x00600000,0,3,NeFSource.ax,4.11.0003.0007 Microsoft MPEG-2 Audio Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 WST Pager,0x00200000,1,1,WSTPager.ax,6.06.7601.17514 MPEG-2 Demultiplexer,0x00600000,1,1,mpg2splt.ax,6.06.7601.17514 DV Video Decoder,0x00800000,1,1,qdv.dll,6.06.7601.17514 CyberLink MPEG-4 Splitter (PDVD7),0x00600000,1,2,clm4splt.ax,1.00.0000.3229 ArcSoft Realtime Capture Encoder Filter,0x00200000,2,0,ArcCaptureEncoder.ax,2.05.0000.0032 Nero Video Processor,0x00200000,1,1,NeroVideoProc.ax,4.11.0003.0007 SampleGrabber,0x00200000,1,1,qedit.dll,6.06.7601.17514 Null Renderer,0x00200000,1,0,qedit.dll,6.06.7601.17514 Nero Sound Switcher,0x00200000,1,1,NeSoundSwitch.ax,4.11.0003.0007 Arcsoft WMV/ASF Splitter,0x00200000,1,0,ArcWmvSpl.ax,1.00.0000.0012 MPEG-2 Sections and Tables,0x005fffff,1,0,Mpeg2Data.ax,6.06.7601.17514 Microsoft AC3 Encoder,0x00200000,1,1,msac3enc.dll,6.01.7601.17514 Nero Audio CD Navigator,0x00200000,0,1,NeAudCD.ax,4.11.0003.0007 StreamBufferSource,0x00200000,0,0,sbe.dll,6.06.7601.17514 Video MotionDetect,0x00200000,1,1,motiondetect.ax,1.00.0000.0005 Smart Tee,0x00200000,1,2,qcap.dll,6.06.7601.17514 Nero Thumbnail Decoder,0x00600000,1,1,NeBDThumbnail.ax,4.11.0003.0007 Overlay Mixer,0x00200000,0,0,qdvd.dll,6.06.7601.17514 Nero Scene Detector,0x00200000,1,0,NeSceneDetector.ax,4.11.0003.0007 Nero Stream Control,0x00200000,1,1,NeStreamControl.ax,1.00.0000.0000 AVI Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.17514 Nero Sample Queue,0x00200000,1,1,NeSampleQueue.ax,1.00.0000.0000 AVI/WAV File Source,0x00400000,0,2,quartz.dll,6.06.7601.17514 Arcsoft Snapshot Filter 1.0,0x00200000,1,1,ArcSnap.ax,1.00.0000.0024 Wave Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 MIDI Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 Multi-file Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 File stream renderer,0x00400000,1,1,quartz.dll,6.06.7601.17514 ArcSoft MPEG Splitter,0x00400000,1,2,ArcSpl.ax,2.04.0002.0045 Nero File Source,0x00200000,0,1,NeFileSrc.ax,4.11.0003.0007 Nero QuickTime(tm) Audio Decoder,0x00400000,1,1,NeQTDec.ax,4.11.0003.0007 Nero File Source (Async.),0x00400000,0,1,NeFileSourceAsync.ax,4.11.0003.0007 Nero Ogg Splitter,0x00400000,1,1,NeOggSplitter.ax,4.11.0003.0007 Microsoft DTV-DVD Audio Decoder,0x005fffff,1,1,msmpeg2adec.dll,6.01.7140.0000 Nero Digital Parser,0x00600000,0,3,NDParser.ax,4.11.0003.0007 StreamBufferSink2,0x00200000,0,0,sbe.dll,6.06.7601.17514 AVI Mux,0x00200000,1,0,qcap.dll,6.06.7601.17514 Line 21 Decoder 2,0x00600002,1,1,quartz.dll,6.06.7601.17514 File Source (Async.),0x00400000,0,1,quartz.dll,6.06.7601.17514 File Source (URL),0x00400000,0,1,quartz.dll,6.06.7601.17514 Nero MP3 Encoder,0x00200000,1,1,NeMp3Encoder.ax,4.11.0003.0007 ArcSoft Time Stamp,0x00200000,1,1,ArcTimeStamp.ax,1.00.0000.0003 CyberLink Demux (PDVD7 UPnP),0x00200000,1,0,CLDemuxer.ax,1.00.0000.4513 Nero Frame Capture,0x00200000,1,1,NeCapture.ax,4.11.0003.0007 Nero Video Sample Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 ArcSoft MPEG Video Decoder,0x00600000,1,1,mpgvideo.ax,2.04.0000.0048 HighMAT/MPV Navigator Client Filter,0x00200000,0,0,HMNavigator.ax,4.11.0003.0007 Infinite Pin Tee Filter,0x00200000,1,1,qcap.dll,6.06.7601.17514 Nero DV Splitter,0x00200000,1,2,NeDVSplitter.ax,4.11.0003.0007 Enhanced Video Renderer,0x00200000,1,0,evr.dll,6.01.7601.17514 CyberLink Streamming Filter (PDVD7),0x00200000,0,1,CLStream.ax,1.01.0000.2902 BDA MPEG2 Transport Information Filter,0x00200000,2,0,psisrndr.ax,6.06.7601.17514 MPEG Video Decoder,0x40000001,1,1,quartz.dll,6.06.7601.17514 WDM Streaming Tee/Splitter Devices: Conversor em T entre Coletores,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Video Compressors: WMVideo8 Encoder DMO,0x00600800,1,1,wmvxencd.dll,6.01.7600.16385 WMVideo9 Encoder DMO,0x00600800,1,1,wmvencod.dll,6.01.7600.16385 MSScreen 9 encoder DMO,0x00600800,1,1,wmvsencd.dll,6.01.7600.16385 ArcSoft Mpeg Encode Filter,0x00200000,0,0,ArcMpegCodec.ax,2.05.0000.0013 ArcSoft Realtime Capture Encoder Filter,0x00200000,0,0,ArcCaptureEncoder.ax,2.05.0000.0032 DV Video Encoder,0x00200000,0,0,qdv.dll,6.06.7601.17514 MJPEG Compressor,0x00200000,0,0,quartz.dll,6.06.7601.17514 Cinepak Codec by Radius,0x00200000,1,1,qcap.dll,6.06.7601.17514 Codec IYUV Intel,0x00200000,1,1,qcap.dll,6.06.7601.17514 Codec IYUV Intel,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft RLE,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft Video 1,0x00200000,1,1,qcap.dll,6.06.7601.17514 Audio Compressors: WM Speech Encoder DMO,0x00600800,1,1,WMSPDMOE.DLL,6.01.7600.16385 WMAudio Encoder DMO,0x00600800,1,1,WMADMOE.DLL,6.01.7600.16385 IMA ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 PCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 Microsoft ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 GSM 6.10,0x00200000,1,1,quartz.dll,6.06.7601.17514 CCITT A-Law,0x00200000,1,1,quartz.dll,6.06.7601.17514 CCITT u-Law,0x00200000,1,1,quartz.dll,6.06.7601.17514 MPEG Layer-3,0x00200000,1,1,quartz.dll,6.06.7601.17514 Audio Capture Sources: Microfone (Dispositivo de High ,0x00200000,0,0,qcap.dll,6.06.7601.17514 PBDA CP Filters: PBDA DTFilter,0x00600000,1,1,CPFilters.dll,6.06.7601.17514 PBDA ETFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17514 PBDA PTFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17514 Midi Renderers: Default MidiOut Device,0x00800000,1,0,quartz.dll,6.06.7601.17514 Microsoft GS Wavetable Synth,0x00200000,1,0,quartz.dll,6.06.7601.17514 WDM Streaming Capture Devices: Captura Mista de HD Audio,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Dispositivo de vídeo USB,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 WDM Streaming Rendering Devices: Alto-falante de HD Audio,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 BDA Network Providers: Microsoft ATSC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBS Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBT Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft Network Provider,0x00200000,0,1,MSNP.ax,6.06.7601.17514 Video Capture Sources: Dispositivo de vídeo USB,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Multi-Instance Capable VBI Codecs: VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 BDA Transport Information Renderers: BDA MPEG2 Transport Information Filter,0x00600000,2,0,psisrndr.ax,6.06.7601.17514 MPEG-2 Sections and Tables,0x00600000,1,0,Mpeg2Data.ax,6.06.7601.17514 BDA CP/CA Filters: Decrypt/Tag,0x00600000,1,1,EncDec.dll,6.06.7601.17514 Encrypt/Tag,0x00200000,0,0,EncDec.dll,6.06.7601.17514 PTFilter,0x00200000,0,0,EncDec.dll,6.06.7601.17514 XDS Codec,0x00200000,0,0,EncDec.dll,6.06.7601.17514 WDM Streaming Communication Transforms: Conversor em T entre Coletores,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Audio Renderers: Alto-falantes (Dispositivo de H,0x00200000,1,0,quartz.dll,6.06.7601.17514 CyberLink Audio Renderer (PDVD7),0x00200000,1,0,cladr.ax,6.00.0000.3916 Default DirectSound Device,0x00800000,1,0,quartz.dll,6.06.7601.17514 Default WaveOut Device,0x00200000,1,0,quartz.dll,6.06.7601.17514 DirectSound: Alto-falantes (Dispositivo de High Definition Audio),0x00200000,1,0,quartz.dll,6.06.7601.17514 --------------- EVR Power Information --------------- Current Setting: {5C67A112-A4C9-483F-B4A7-1D473BECAFDC} (Quality) Quality Flags: 2576 Enabled: Force throttling Allow half deinterlace Allow scaling Decode Power Usage: 100 Balanced Flags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 50 PowerFlags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 0

Send the system info (cpu usage, temperature, kernel version, etc..) from any linux based system like Raspberry Pi, ubuntu... to firebase. Realtime nodejs script

Realtime pseudo 3D raycaster on the CPU using 2D ray marching

FaceSwap, Realtime using cpu, 3D, c++