使用 OpenCV 和 Python 在视频上应用热图

Question

我想编写一个代码，将热图应用于有运动的地方的视频。我写了一个检测运动的代码，有轮廓，但我不知道如何制作热图。

这是我拥有的代码

import cv2
import numpy as np

# upload video
cap = cv2.VideoCapture('test_video.mp4')

#reading two frames
ret, frame1 = cap.read()
ret, frame2 = cap.read()

while cap.isOpened():

     # get diference between two frames
     diff = cv2.absdiff(frame1, frame2)

     # convert diference in gray
     gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)

     # bluring and treshold
     blur = cv2.GaussianBlur(gray, (5,5), 0)
     _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)

     dilated = cv2.dilate(thresh, None, iterations = 3)

     # define contours
     contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
     
     # draw contours
     cv2.drawContours(frame1, contours, -1, (255,0,0), 1)

     # show frames
     cv2.imshow('frame', frame1)
     frame1 = frame2
     ret, frame2 = cap.read()

     if cv2.waitKey(60) == 60:
          break

cv2.destroyAllWindows()
cap.release()

我看过这个链接：Build a Motion Heatmap Video Using OpenCV With Python。我想重现代码，但是很多东西如fourcc, image_folder, 和images没有定义，所以我试着用另一种方式来做。

你能帮我解决这个问题吗？基本上，我想将热图应用于有运动的视频。

Answer 1

这是一个想法。您知道如何使用 opencv 循环播放视频帧，对吗？那么，对于while循环所在的每一帧，将当前帧之后的帧存储在变量中，并比较当前帧和未来帧之间的差异。

通过两帧之间的差异，您可以检测运动的轮廓。假设我们用绿色在图像上绘制轮廓。

在循环之前定义一个空白数组while作为热图；循环的每次迭代，向帧上坐标为绿色的热图的每个坐标添加一定量，并从图像上坐标不是绿色的热图中删除一定量。

1. 导入必要的模块：

import cv2
import numpy as np

2. 定义一个函数来处理两个帧之间的差异，以便更好地检测我们案例的轮廓。这些值可以根据您的需要进行调整：

def process(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_blur = cv2.GaussianBlur(img_gray, (5, 5), 25)
    img_canny = cv2.Canny(img_blur, 5, 50)
    kernel = np.ones((3, 3))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=4)
    img_erode = cv2.erode(img_dilate, kernel, iterations=1)
    return img_erode

3. 定义一个函数，该函数将接收两帧之间差异的处理图像以及要在其上绘制轮廓的图像。它将返回绘制有轮廓（绿色）的图像：

def get_contours(img, img_original):
    img_contours = img_original.copy()
    contours, hierarchies = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    cv2.drawContours(img_contours, contours, -1, (0, 255, 0), -1) 
    return img_contours

4. 定义您的捕获设备，并在循环之前读取两帧while；第一个是循环每次迭代的当前帧while，第二个是循环每次迭代的未来帧while。另外，定义热图的空白图像heat_map：

cap = cv2.VideoCapture("Bar fight security cam.mp4")

success, img1 = cap.read()
success, img2 = cap.read()
heat_map = np.zeros(img1.shape[:-1])

5. 在while循环中，找到两个帧之间的差异，并获取每个循环的当前帧，并根据差异绘制轮廓：

while success:
    diff = cv2.absdiff(img1, img2)
    img_contours = get_contours(process(diff), img1)

6. 向之前定义的函数返回的图像上绿色的3每个坐标添加一个数字，例如 ，并从图像上非绿色的每个坐标中减去 3。为了确保没有颜色导致通道值小于和大于，请将边界应用于热图：heat_mapget_contoursheat_map0255

    heat_map[np.all(img_contours == [0, 255, 0], 2)] += 3
    heat_map[np.any(img_contours != [0, 255, 0], 2)] -= 3
    heat_map[heat_map < 0] = 0
    heat_map[heat_map > 255] = 255

7. 将数组转换heat_map为灰度图像，然后转换为热图像：

    img_mapped = cv2.applyColorMap(heat_map.astype('uint8'), cv2.COLORMAP_JET)

8. 最后，显示帧并检索下一次迭代的帧：

    cv2.imshow("Original", img1)
    cv2.imshow("Heat Map", img_mapped)
    
    img1 = img2
    success, img2 = cap.read()
    
    if cv2.waitKey(1) == ord('q'):
        break

共：

import cv2
import numpy as np

def process(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_blur = cv2.GaussianBlur(img_gray, (5, 5), 25)
    img_canny = cv2.Canny(img_blur, 5, 50)
    kernel = np.ones((3, 3))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=4)
    img_erode = cv2.erode(img_dilate, kernel, iterations=1)
    return img_erode

def get_contours(img, img_original):
    img_contours = img_original.copy()
    contours, hierarchies = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

    cv2.drawContours(img_contours, contours, -1, (0, 255, 0), -1) 
    # If you want to omit smaller contours, loop through the detected contours, and only draw them on the image if they are at least a specific area. Don't forget to remove the line above if you choose the below block of code.
    # for cnt in contours: 
    #     if cv2.contourArea(cnt) > 500:
    #         cv2.drawContours(img_contours, [cnt], -1, (0, 255, 0), -1) 

    return img_contours

cap = cv2.VideoCapture("Bar fight security cam.mp4")

success, img1 = cap.read()
success, img2 = cap.read()
heat_map = np.zeros(img1.shape[:-1])

while success:
    diff = cv2.absdiff(img1, img2)
    img_contours = get_contours(process(diff), img1)

    heat_map[np.all(img_contours == [0, 255, 0], 2)] += 3 # The 3 can be tweaked depending on how fast you want the colors to respond
    heat_map[np.any(img_contours != [0, 255, 0], 2)] -= 3
    heat_map[heat_map < 0] = 0
    heat_map[heat_map > 255] = 255

    img_mapped = cv2.applyColorMap(heat_map.astype('uint8'), cv2.COLORMAP_JET)

#    img1[heat_map > 160] = img_mapped[heat_map > 160] Use this line to draw the heat map on the original video at a specific temperature range. For this it's where ever the temperature is above 160 (min is 0 and max is 255)

    cv2.imshow("Original", img1)
    cv2.imshow("Heat Map", img_mapped)
    
    img1 = img2
    success, img2 = cap.read()
    
    if cv2.waitKey(1) == ord('q'):
        break