您好，感谢您的项目分享，我想做一个其他物体检测追踪的模型。我有了一个预训练的图像识别分类模型。然后我load视频做图像腐蚀的时候报错了。这是我在windows下运行的error-log： > D:\PythonSpace\Hand_Tracking>python track_finger.py --file car.flv > Using TensorFlow backend. > car.flv > D:\PythonSpace\Hand_Tracking > D:\PythonSpace\Hand_Tracking\car.flv > > 2019-03-22 11:51:22.024142: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 > C:\Python\Python37\lib\site-packages\keras\engine\saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually. > warnings.warn('No training configuration found in save file: ' > [[0 0 0 ... 0 0 0] > [0 0 0 ... 0 0 0] > [0 0 0 ... 0 0 0] > ... > [0 0 0 ... 0 0 0] > [0 0 0 ... 0 0 0] > [0 0 0 ... 0 0 0]] > Traceback (most recent call last): > File "track_finger.py", line 157, in > if __name__ == '__main__': > File "track_finger.py", line 44, in main > args = parser.parse_args() > File "track_finger.py", line 109, in track > dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2) > ValueError: not enough values to unpack (expected 3, got 2) 几乎没有怎么改变您的原始代码，主要是换了个分类模型： ``` Date : 7th Jan 2018 Author : xiaochus Email : xiaochus@live.cn Affiliation : School of Computer Science and Communication Engineering - Jiangsu University - China License : MIT Status : Under Active Development Description : OpenCV 3 & Keras implementation of the vehicle tracking. """ import sys import copy import argparse import cv2 import numpy as np from keras.models import load_model import os from utils.entity import Entity def main(argv): parser = argparse.ArgumentParser() # Required arguments. parser.add_argument( "--file", help="Input video file.", ) # Optional arguments. parser.add_argument( "--iou", default=0.2, help="threshold for tracking", ) args = parser.parse_args() track(args.file, args.iou) def overlap(box1, box2): """ Check the overlap of two boxes """ endx = max(box1[0] + box1[2], box2[0] + box2[2]) startx = min(box1[0], box2[0]) width = box1[2] + box2[2] - (endx - startx) endy = max(box1[1] + box1[3], box2[1] + box2[3]) starty = min(box1[1], box2[1]) height = box1[3] + box2[3] - (endy - starty) if (width <= 0 or height <= 0): return 0 else: Area = width * height Area1 = box1[2] * box1[3] Area2 = box2[2] * box2[3] ratio = Area / (Area1 + Area2 - Area) return ratio def track(video, iou): print(video) print(os.getcwd()) print(os.path.join(os.getcwd(), video)) camera = cv2.VideoCapture(video) print(type(camera)) res, frame = camera.read() y_size = frame.shape[0] x_size = frame.shape[1] # Load CNN classification model model = load_model('brightness3ch.h5') # Definition of MOG2 Background Subtraction bs = cv2.createBackgroundSubtractorMOG2(detectShadows=True) history = 20 frames = 0 counter = 0 track_list = [] cv2.namedWindow("detection", cv2.WINDOW_NORMAL) while True: res, frame = camera.read() y_size = frame.shape[0] x_size = frame.shape[1] cv2.imwrite("result.png", frame) if res is False: break # Train the MOG2 with first frames frame fg_mask = bs.apply(frame) if frames < history: frames += 1 continue # Expansion and denoising the original frame th = cv2.threshold(fg_mask.copy(), 244, 255, cv2.THRESH_BINARY)[1] print(th) th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2) dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2) image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Check the bouding boxs for c in contours: x, y, w, h = cv2.boundingRect(c) if cv2.contourArea(c) > 3000: # Extract roi img = frame[y: y + h, x: x + w, :] rimg = cv2.resize(img, (64, 64), interpolation=cv2.INTER_CUBIC) image_data = np.array(rimg, dtype='float32') image_data /= 255. roi = np.expand_dims(image_data, axis=0) flag = model.predict(roi) print(flag) if flag[0][0] >= 0 and flag[0][0] <=9: e = Entity(counter, (x, y, w, h), frame) # Exclude existing targets in the tracking list if track_list: count = 0 num = len(track_list) for p in track_list: if overlap((x, y, w, h), p.windows) < iou: count += 1 if count == num: track_list.append(e) else: track_list.append(e) counter += 1 # Check and update goals if track_list: tlist = copy.copy(track_list) for e in tlist: x, y = e.center if 10 < x < x_size - 10 and 10 < y < y_size - 10: e.update(frame) else: track_list.remove(e) frames += 1 cv2.imshow("detection", frame) if cv2.waitKey(110) & 0xff == 27: break camera.release() if __name__ == '__main__': main(sys.argv) ``` 在做膨胀腐蚀的时候出错了，用您的原始视频和我自己的视频都碰到了同样的error-log

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