PyCharm中的OpenCV代码：进程结束，退出代码为0

import yaml import numpy as np import cv2 # path references fn = "Khare_testvideo_01.mp4" #3 #fn = "datasets\parkinglot_1_720p.mp4" #fn = "datasets\street_high_360p.mp4" fn_yaml = "Khare_yml_01.yml" fn_out = "Khare_outputvideo_01.avi" cascade_src = 'Khare_classifier_02.xml' car_cascade = cv2.CascadeClassifier(cascade_src) global_str = "Last change at: " change_pos = 0.00 dict = { 'text_overlay': True, 'parking_overlay': True, 'parking_id_overlay': True, 'parking_detection': True, 'motion_detection': True, 'pedestrian_detection': False, # takes a lot of processing power 'min_area_motion_contour': 500, # area given to detect motion 'park_laplacian_th': 2.8, 'park_sec_to_wait': 1, # 4 wait time for changing the status of a region 'start_frame': 0, # begin frame from specific frame number 'show_ids': True, # shows id on each region 'classifier_used': True, 'save_video': True } # Set from video cap = cv2.VideoCapture(fn) print("video found") video_info = { 'fps': cap.get(cv2.CAP_PROP_FPS), 'width': int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)*0.6), 'height': int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)*0.6), 'fourcc': cap.get(cv2.CAP_PROP_FOURCC), 'num_of_frames': int(cap.get(cv2.CAP_PROP_FRAME_COUNT))} cap.set(cv2.CAP_PROP_POS_FRAMES, dict['start_frame']) # jump to frame number specified def run_classifier(img, id): # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cars = car_cascade.detectMultiScale(img, 1.1, 1) if cars == (): return False else: # parking_status[id] = False return True # Define the codec and create VideoWriter object if dict['save_video']: fourcc = cv2.VideoWriter_fourcc('X','V','I','D') # options: ('P','I','M','1'), ('D','I','V','X'), ('M','J','P','G'), ('X','V','I','D') out = cv2.VideoWriter(fn_out, -1, 25.0,(video_info['width'], video_info['height'])) print("save video -- out w * H") # initialize the HOG descriptor/person detector. Take a lot of processing power. if dict['pedestrian_detection']: hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # Use Background subtraction if dict['motion_detection']: fgbg = cv2.createBackgroundSubtractorMOG2(history=300, varThreshold=16, detectShadows=True) # Read YAML data (parking space polygons) with open(fn_yaml, 'r') as stream: parking_data = yaml.load(stream) parking_contours = [] parking_bounding_rects = [] parking_mask = [] parking_data_motion = [] if parking_data != None: for park in parking_data: points = np.array(park['points']) rect = cv2.boundingRect(points) points_shifted = points.copy() points_shifted[:, 0] = points[:, 0] - rect[0] # shift contour to region of interest points_shifted[:, 1] = points[:, 1] - rect[1] parking_contours.append(points) parking_bounding_rects.append(rect) mask = cv2.drawContours(np.zeros((rect[3], rect[2]), dtype=np.uint8), [points_shifted], contourIdx=-1, color = 255, thickness=-1, lineType=cv2.LINE_8) mask = mask == 255 parking_mask.append(mask) kernel_erode = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) # morphological kernel kernel_dilate = cv2.getStructuringElement(cv2.MORPH_RECT,(5,19)) if parking_data != None: parking_status = [False]*len(parking_data) parking_buffer = [None]*len(parking_data) # bw = () def print_parkIDs(park, coor_points, frame_rev): moments = cv2.moments(coor_points) centroid = (int(moments['m10']/moments['m00'])-3, int(moments['m01']/moments['m00'])+3) # putting numbers on marked regions cv2.putText(frame_rev, str(park['id']), (centroid[0]+1, centroid[1]+1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA) cv2.putText(frame_rev, str(park['id']), (centroid[0]-1, centroid[1]-1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA) cv2.putText(frame_rev, str(park['id']), (centroid[0]+1, centroid[1]-1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA) cv2.putText(frame_rev, str(park['id']), (centroid[0]-1, centroid[1]+1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA) cv2.putText(frame_rev, str(park['id']), centroid, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA) while(cap.isOpened()): video_cur_pos = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0 # Current position of the video file in seconds video_cur_frame = cap.get(cv2.CAP_PROP_POS_FRAMES) # Index of the frame to be decoded/captured next ret, frame_initial = cap.read() if ret == True: frame = cv2.resize(frame_initial, None, fx=0.6, fy=0.6) if ret == False: print("Video ended") break # Background Subtraction frame_blur = cv2.GaussianBlur(frame.copy(), (5,5), 3) # frame_blur = frame_blur[150:1000, 100:1800] frame_gray = cv2.cvtColor(frame_blur, cv2.COLOR_BGR2GRAY) frame_out = frame.copy() # Drawing the Overlay. Text overlay at the left corner of screen if dict['text_overlay']: str_on_frame = "%d/%d" % (video_cur_frame, video_info['num_of_frames']) cv2.putText(frame_out, str_on_frame, (5, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA) cv2.putText(frame_out,global_str + str(round(change_pos, 2)) + 'sec', (5, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2, cv2.LINE_AA) # motion detection for all objects if dict['motion_detection']: # frame_blur = frame_blur[380:420, 240:470] # cv2.imshow('dss', frame_blur) fgmask = fgbg.apply(frame_blur) bw = np.uint8(fgmask==255)*255 bw = cv2.erode(bw, kernel_erode, iterations=1) bw = cv2.dilate(bw, kernel_dilate, iterations=1) # cv2.imshow('dss',bw) # cv2.imwrite("frame%d.jpg" % co, bw) (_, cnts, _) = cv2.findContours(bw.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # loop over the contours for c in cnts: # print(cv2.contourArea(c)) # if the contour is too small, we ignore it if cv2.contourArea(c) < dict['min_area_motion_contour']: continue (x, y, w, h) = cv2.boundingRect(c) cv2.rectangle(frame_out, (x, y), (x + w, y + h), (255, 0, 0), 1) # detecting cars and vacant spaces if dict['parking_detection']: for ind, park in enumerate(parking_data): points = np.array(park['points']) rect = parking_bounding_rects[ind] roi_gray = frame_gray[rect[1]:(rect[1]+rect[3]), rect[0]:(rect[0]+rect[2])] # crop roi for faster calcluation laplacian = cv2.Laplacian(roi_gray, cv2.CV_64F) # cv2.imshow('oir', laplacian) points[:, 0] = points[:, 0] - rect[0] # shift contour to roi points[:, 1] = points[:, 1] - rect[1] delta = np.mean(np.abs(laplacian * parking_mask[ind])) # if(delta<2.5): # print("ind, del", ind, delta) status = delta < dict['park_laplacian_th'] # If detected a change in parking status, save the current time if status != parking_status[ind] and parking_buffer[ind]==None: parking_buffer[ind] = video_cur_pos change_pos = video_cur_pos # print("state ", ind,delta) # applying classifier in case a change is detected in the status of area # if dict['classifier_used']: # classifier_result = run_classifier(roi_gray) # if classifier_result: # print(classifier_result) # If status is still different than the one saved and counter is open elif status != parking_status[ind] and parking_buffer[ind] != None: if video_cur_pos - parking_buffer[ind] > dict['park_sec_to_wait']: parking_status[ind] = status parking_buffer[ind] = None # If status is still same and counter is open elif status == parking_status[ind] and parking_buffer[ind] != None: parking_buffer[ind] = None # changing the color on the basis on status change occured in the above section and putting numbers on areas if dict['parking_overlay']: for ind, park in enumerate(parking_data): points = np.array(park['points']) if parking_status[ind]: color = (0, 255, 0) rect = parking_bounding_rects[ind] roi_gray_ov = frame_gray[rect[1]:(rect[1] + rect[3]), rect[0]:(rect[0] + rect[2])] # crop roi for faster calcluation res = run_classifier(roi_gray_ov, ind) if res: parking_data_motion.append(parking_data[ind]) # del parking_data[ind] color = (0, 0, 255) else: color = (0, 0, 255) cv2.drawContours(frame_out, [points], contourIdx=-1, color=color, thickness=2, lineType=cv2.LINE_8) if dict['show_ids']: print_parkIDs(park, points, frame_out) if parking_data_motion != []: for index, park_coord in enumerate(parking_data_motion): points = np.array(park_coord['points']) color = (0, 0, 255) recta = parking_bounding_rects[ind] roi_gray1 = frame_gray[recta[1]:(recta[1] + recta[3]), recta[0]:(recta[0] + recta[2])] # crop roi for faster calcluation # laplacian = cv2.Laplacian(roi_gray, cv2.CV_64F) # delta2 = np.mean(np.abs(laplacian * parking_mask[ind])) # state = delta2<1 # classifier_result = run_classifier(roi_gray1, index) # cv2.imshow('dsd', roi_gray1) fgbg1 = cv2.createBackgroundSubtractorMOG2(history=300, varThreshold=16, detectShadows=True) roi_gray1_blur = cv2.GaussianBlur(roi_gray1.copy(), (5, 5), 3) # cv2.imshow('sd', roi_gray1_blur) fgmask1 = fgbg1.apply(roi_gray1_blur) bw1 = np.uint8(fgmask1 == 255) * 255 bw1 = cv2.erode(bw1, kernel_erode, iterations=1) bw1 = cv2.dilate(bw1, kernel_dilate, iterations=1) # cv2.imshow('sd', bw1) # cv2.imwrite("frame%d.jpg" % co, bw) (_, cnts1, _) = cv2.findContours(bw1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # loop over the contours for c in cnts1: print(cv2.contourArea(c)) # if the contour is too small, we ignore it if cv2.contourArea(c) < 4: continue (x, y, w, h) = cv2.boundingRect(c) classifier_result1 = run_classifier(roi_gray1, index) if classifier_result1: # print(classifier_result) color = (0, 0, 255) # Red again if car found by classifier else: color = (0,255, 0) classifier_result1 = run_classifier(roi_gray1, index) if classifier_result1: # print(classifier_result) color = (0, 0, 255) # Red again if car found by classifier else: color = (0, 255, 0) cv2.drawContours(frame_out, [points], contourIdx=-1, color=color, thickness=2, lineType=cv2.LINE_8) if dict['pedestrian_detection']: # detect people in the image. Slows down the program, requires high GPU speed (rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05) # draw the bounding boxes for (x, y, w, h) in rects: cv2.rectangle(frame_out, (x, y), (x + w, y + h), (255, 0, 0), 2) # write the output frames if dict['save_video']: #if video_cur_frame % 35 == 0: # take every 30 frames out.write(frame_out) # Display video cv2.imshow('frame', frame_out) # cv2.imshow('background mask', bw) k = cv2.waitKey(1) if k == ord('q'): break elif k == ord('c'): cv2.imwrite('frame%d.jpg' % video_cur_frame, frame_out) elif k == ord('j'): cap.set(cv2.CAP_PROP_POS_FRAMES, video_cur_frame+1000) # jump 1000 frames elif k == ord('u'): cap.set(cv2.CAP_PROP_POS_FRAMES, video_cur_frame + 500) # jump 500 frames if cv2.waitKey(33) == 27: break cv2.waitKey(0) cap.release() if dict['save_video']: out.release() cv2.destroyAllWindows()

1条回答

网友

1楼 · 发布于 2024-04-19 22:57:35

把这些线改一下

`if dict['save_video']:
    fourcc = cv2.VideoWriter_fourcc('X','V','I','D') # options: ('P','I','M','1'), ('D','I','V','X'), ('M','J','P','G'), ('X','V','I','D')
    out = cv2.VideoWriter(fn_out, -1, 25.0,(video_info['width'], video_info['height']))`

到

^{pr2}$

再试一次

同时将函数/方法定义放在代码的顶部。在

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