8 examples of 'videocapture cv2' in Python

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def video(src = 0):
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    cap = cv2.VideoCapture(src)
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    if args['save']:
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        if os.path.isfile(args['save']+'.avi'):
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            os.remove(args['save']+'.avi')
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        out = cv2.VideoWriter(args['save']+'.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30,(int(cap.get(3)),int(cap.get(4))))
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    while(cap.isOpened):
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        _ , frame = cap.read()
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        output_frame, op_crop = Eyeliner(frame)
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        if args['save']:
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            out.write(output_frame)
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        cv2.imshow("Artificial Eyeliner", cv2.resize(output_frame, (600,600)))
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        cv2.imshow('Eye Region', cv2.resize(op_crop, (400, 200)))
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        if cv2.waitKey(1) & 0xFF == ord('q'):
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            break
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    if args['save']:
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        out.release()
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    cap.release()
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    cv2.destroyAllWindows()

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def detect():
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    face = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
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    eye = cv2.CascadeClassifier("data/haarcascade_eye.xml")
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    camera = cv2.VideoCapture(0) # 0表示使用第一个摄像头
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    while True:
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        ret, frame = camera.read() # ret:布尔值表示是否读取帧成功, frame为帧本身
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        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 检测人脸需要基于灰度图像
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        faces = face.detectMultiScale(gray, 1.3, 5) 
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        # faces = face.detectMultiScale(gray, scaleFactor, minNeighbors)
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        # scaleFactor: 每次迭代时图像的压缩率
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        # minNeighbors: 每个人脸矩形保留近似邻近数目的最小值
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        for x,y,w,h in faces:
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            img = cv2.rectangle(frame, (x,y), (x + w, y + h), (250, 0, 0), 2)
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            eye_area = gray[y : y + h, x : x + w]
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            eyes = eye.detectMultiScale(eye_area, 1.03, 5, 0, (40, 40))
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            # eye.detectMultiScale(eye_area, 1.03, 5, 0, (40, 40))中
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            # (40, 40)参数目的是为了消除假阳性(false positive)的影响， 将眼睛搜索的最小尺寸现实为40x40
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            for ex,ey,ew,eh in eyes:
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                cv2.rectangle(frame, (x + ex, y + ey),(x + ex + ew, y + ey + eh), (0, 255, 0), 2)
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        cv2.imshow("face", frame)
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        if cv2.waitKey(1000 // 12) & 0xff == ord("q"):
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            break
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    camera.release()
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    cv2.destroyAllWindows()

4
def main():
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    capture = cv2.VideoCapture(0)
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    if capture.isOpened():
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        flag, frame = capture.read()
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    else:
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        flag = False
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    while flag:
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        flag, frame = capture.read()
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        frame = abs(255 - frame)
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        cv2.imshow("Video Camera", frame)
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        if cv2.waitKey(1) & 0xFF == 27:
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            break
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    cv2.destroyAllWindows()

4
def main():
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    capture = cv2.VideoCapture(0)
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    eye_path = "../classifier/haarcascade_eye.xml"
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    face_path = "../classifier/haarcascade_frontalface_default.xml"
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    eye_cascade = cv2.CascadeClassifier(eye_path)
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    face_cascade = cv2.CascadeClassifier(face_path)
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    while (True):
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        _, frame = capture.read()
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        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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        eyes = eye_cascade.detectMultiScale(gray_frame, scaleFactor=1.05, minNeighbors=5, minSize=(10,10))
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        faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.05, minNeighbors=5, minSize=(40, 40))
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        print("Number of eyes : " + str(len(eyes)))
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        print("Number of faces : " + str(len(faces)))
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        for (x, y, w, h) in eyes:
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            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
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        for (x, y, w, h) in faces:
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            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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        cv2.imshow("Live Capture", frame)
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        if cv2.waitKey(1) == 27:
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            break
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    cv2.destroyAllWindows()
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    capture.release()

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def detection(video, stream, fps, scale, details):
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    manager_cv2 = ManagerCV2(cv2.VideoCapture(video), is_stream=stream, fps_limit=fps)
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    manager_cv2.add_keystroke(27, 1, exit=True)
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    manager_cv2.add_keystroke(ord('d'), 1, 'detect')
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    manager_cv2.key_manager.detect = True # I want to start detecting
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    selector = None
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    frames_tracking = 0
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    for frame in manager_cv2:
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        if manager_cv2.key_manager.detect:
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            # I'm using the frame counter from the manager
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            # So I will recalculate de detection each 20 frames
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            # The other ones, I will continue using the same selector (so it will not change)
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            #   NOTE: It is == 1 (and not == 0) because in the first iteration we
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            #    get the first frame, so count_frames = 1.
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            #    This is how I manage to ensure that in the first loop it is True
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            #    so selector exist and doesn't raise an error.
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            if manager_cv2.count_frames % 20 == 1:
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                new_selector = face_detector(frame, scale, details)
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                if not selector or frames_tracking >= 30 or len(new_selector.zones) >= len(selector.zones):
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                    selector = new_selector
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                    frames_tracking = 0
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                manager_cv2.set_tracking(selector, frame)
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            else:
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                # The other frames I wil get the tracking of the last detections
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                selector = manager_cv2.get_tracking(frame)
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                frames_tracking += 1
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            frame = selector.draw(frame)
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        cv2.imshow('Face detection example', frame)
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    print('FPS: {}'.format(manager_cv2.get_fps()))
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    cv2.destroyAllWindows()

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def load_video():
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    manager_cv2 = ManagerCV2(cv2.VideoCapture('walking.mp4'), is_stream=False, fps_limit=3)
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    manager_cv2.add_keystroke(27, 1, print, 'Pressed esc. Exiting', exit=True)
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    storage_cv2 = StorageCV2(path='salida.json')
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    for frame, selector in zip(manager_cv2,storage_cv2):
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        frame = cv2.flip(frame, 1)
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        frame = selector.draw(frame)
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        cv2.imshow('Example face_recognition', frame)
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    print('FPS: {}'.format(manager_cv2.get_fps()))
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    cv2.destroyAllWindows()

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def recognize_from_video(video, detector):
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    if video == '0':
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        print('[INFO] Webcam mode is activated')
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        capture = cv2.VideoCapture(0)
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        if not capture.isOpened():
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            print("[ERROR] webcamera not found")
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            sys.exit(1)
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    else:
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        if pathlib.Path(video).exists():
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            capture = cv2.VideoCapture(video)
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    while(True):
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        ret, img = capture.read()
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        if cv2.waitKey(1) & 0xFF == ord('q'):
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            break
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        if not ret:
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            continue
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        boxes, scores, cls_inds = detect_objects(img, detector)
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        img = draw_detection(img, boxes, scores, cls_inds)
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        cv2.imshow('frame', img)
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        # press q to end video capture
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        if cv2.waitKey(1)&0xFF == ord('q'):
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            break
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        if not ret:
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            continue
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    capture.release()
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    cv2.destroyAllWindows()
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    print('Script finished successfully.')

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def recognize_from_video():
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    # net initialize
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    env_id = ailia.get_gpu_environment_id()
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    print(f'env_id: {env_id}')
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    net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id)
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    if args.video == '0':
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        print('[INFO] Webcam mode is activated')
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        capture = cv2.VideoCapture(0)
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        if not capture.isOpened():
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            print("[ERROR] webcamera not found")
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            sys.exit(1)
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    else:
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        if check_file_existance(args.video):
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            capture = cv2.VideoCapture(args.video)        
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    while(True):
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        ret, frame = capture.read()
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        if cv2.waitKey(1) & 0xFF == ord('q'):
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            break
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        if not ret:
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            continue
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        input_image, input_data = preprocess_frame(
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            frame, IMAGE_HEIGHT, IMAGE_WIDTH, normalize_type='127.5'
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        )
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        # inference
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        input_blobs = net.get_input_blob_list()
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        net.set_input_blob_data(input_data, input_blobs[0])
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        net.update()
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        preds_ailia = net.get_results()
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        # postprocessing
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        detections = postprocess(preds_ailia)
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        show_result(input_image, detections)
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        cv2.imshow('frame', input_image)
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    capture.release()
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    cv2.destroyAllWindows()
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    print('Script finished successfully.')