Question

我需要拍摄视频文件，将其分成几帧，确定感兴趣的区域，并将其应用于每个帧。然后，我需要确定视频中黑色像素随时间逐渐消失的百分比。视频显示颗粒移向正极，并且可以将其视为黑色/棕色液体。
到目前为止，我设法将视频分成多个帧，然后放入称为arr的数组中。我可以裁剪感兴趣的区域，但它要求我为每个区域做一个。但是，我只想执行一次并将其应用于每个框架。然后，我需要计算和绘制粒子的扩散。我正在使用OpenCV 3.4和python 3.6。

我是python和opencv的新手，因此感谢您的帮助或建议。

def vid_frame（vid_file）：

# Playing video from file inputted
video = cv2.VideoCapture(vid_file)

try:
    if not os.path.exists("data"):
        os.makedirs("data")
except OSError:
    print("Error: creating directory of data")

currentFrame = 0
num_frames = 10
arr = []
for i in range(0, num_frames):
    ret, frame = video.read()

    if ret == True:

        # Converts frames to gray scale
        #gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)

        # Wait for 'q' to exit the while loop
        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

        # Saves image of the current frame in .jpg file
        name = './data/file' + str(currentFrame) + '.jpg'
        print('Creating...' + name)
        cv2.imwrite(name, frame)

        # Prevents duplicate images
        currentFrame += 1

        # Add frame to array
        arr.append(name)

# When everything is done, release the capture
video.release()
cv2.destroyAllWindows()



if __name__ == '__main__':
    # Iterate over each frame
    for i in arr:
        # Read image
        #im = cv2.imread(i, 0)

        # 0 is the constant for cv::IMREAD_GRAYSCALE
        im = cv2.imread(i, 0)

        # Reduce resolution
        #im = cv2.resize(im, (1000, 1000))

        imC = cv2.resize(im, (int(im.shape[1] / 4), int(im.shape[0] / 4)))

        # Select ROI (Region of Interest)
        showCrosshair = False
        fromCenter = False
        r = cv2.selectROI("Image", imC, fromCenter, showCrosshair)

        # Crop image
        imCrop = imC[int(r[1]):int(r[1] + r[3]), int(r[0]):int(r[0] + r[2])]

        # Display cropped image
        cv2.imshow("Image", imCrop)
        cv2.waitKey(0)

        # Save the cropped image
        cv2.imwrite("resize.jpg", imC)

        # Get image properties
        h, w = np.shape(im)
        print("width:", str(w))
        print("height:", str(h))

        # Iterate over each pixel in the image
        for py in range(0, h):
            avgGray = 0.0
            for px in range(0, w):
                avgGray += im[py][px]
                #print(im[py][px])
                print("Average Gray for row ", py, " is:  ", avgGray/w)

        # Create the threshold
        retval, threshold1 = cv2.threshold(imCrop, 12, 255,

cv2.THRESH_BINARY）

        cv2.imshow("threshold1", threshold1)

        # Find frequency of pixels in range 0-255
        #histr = cv2.calcHist([threshold1], [0], None, [256], [0, 256])

        # Show the plotting graph of an image (histogram)
        plt.plot([0, 50, 100, 150, 200], [0, 1, 2, 3, 4])

        # Labels and Display
        plt.title("Electrophoresis Video")
        plt.xlabel("Time (s)")
        plt.ylabel("Interface Position (mm)")
        plt.grid(True)
        plt.show()

将相同的关注区域应用于多张图片

0 个答案: