如何使用 openCV 在 Python 内提高视频播放速度

Question

我正在编写一个程序，在遇到栏杆的第一个像素的视频上画一条线，我的问题是视频播放缓慢。

屏幕截图以供参考视频的外观。在视频中，相机被移近了，但由于速度较慢，我必须等待几分钟才能看到变化，但拍摄时，它每隔几秒钟就移动一次。

我假设问题是 for 循环在视频的每一帧上运行，但我不确定。

我可以实施什么解决方案来加速我的程序？

import cv2

cap = cv2.VideoCapture('video.mp4')

while(cap.isOpened()):

    ret, frame = cap.read()
    canny = cv2.Canny(frame, 85, 255)
    height, width = canny.shape

    first_black_array = []

    for x in range(width):
        first_black_pixel_found = 0
        for y in range(height):
            if first_black_pixel_found == 0:
                if canny[y,x] == 255:
                    first_black_array.append(height - y)
                    first_black_pixel_found = 1
                    cv2.line(frame,(x,y),(x,y),(0,255,0),1)

    cv2.imshow('frame',frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

谢谢！

Answer 1

这就是问题...

for x in range(width):
    for y in range(height):
         if canny[y,x] == 255:

Numpy.argmax 是解决方案...

for x in range(width-1):
    # Slice the relevant column from the image
    # The image 'column' is a tall skinny image, only 1px thick
    column = np.array(canny[:,x:x+1])
    # Use numpy to find the first non-zero value
    railPoint = np.argmax(column)

完整代码：

import cv2, numpy as np, time
# Get start time
start = time.time()
# Read in the image
img = cv2.imread('/home/stephen/Desktop/rail.jpg')[40:,10:-10]
# Canny filter
canny = cv2.Canny(img, 85, 255)
# Get height and width
height, width = canny.shape
# Create list to store rail points
railPoints = []
# Iterate though each column in the image
for position in range(width-1):
    # Slice the relevant column from the image
    # The image 'column' is a tall skinny image, only 1px thick
    column = np.array(canny[:,position:position+1])
    # Use numpy to find the first non-zero value
    railPoint = np.argmax(column)
    # Add the railPoint to the list of rail points
    railPoints.append(railPoint)
    # Draw a circle on the image
    cv2.circle(img, (position, railPoint), 1, (123,234,123), 2)
cv2.imshow('img', img)                      
k = cv2.waitKey(1)
cv2.destroyAllWindows()
print(time.time() - start)

我使用 Numpy 的解决方案耗时 6 毫秒，而你的解决方案耗时 266 毫秒。

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