使用 OpenCV，我创建了两个函数：
下一步，在应用正确的代码后，我的目标是创建一个视频图像结果，其中几个过滤器重合的点（例如，一个水平条出现在一个圆圈内）只要效果重叠就会突出显示，而其余部分图像变暗。我的目标是在那些毫秒内清楚地看到重叠区域的存在。
什么代码可以给我那个结果？
import cv2
import numpy as np
cap = cv2.VideoCapture(r'C:\Users\Usuario\untitled\13.mp4')
# Setup for optical flow filter
ret, frame1 = cap.read()
if frame1.shape[0] > 1400:
frame1 = cv2.resize(frame1, None, fx = 1/4, fy = 1/4)
if frame1.shape[0] > 500:
frame1 = cv2.resize(frame1, None, fx = 1/2, fy = 1/2)
prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
# Create numpy arrays to hold the results of contour_detection and corner_detection functions
lines_edges = np.zeros_like(frame1)
output = np.zeros_like(frame1)
#HORIZONTAL LINES ####################################
def horizontal_lines_detection():
global lines_edges
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
kernel_size = 25 #25 (default 5)
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) #0
low_threshold = 5 #5, (default 50)
high_threshold = 84 #84 (default 150)
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
rho = 2 # distance resolution in pixels of the Hough grid 1
theta = np.pi / 120 # angular resolution in radians of the Hough grid #180
threshold = 25 # minimum number of votes (intersections in Hough grid cell) 15
min_line_length = 5 # minimum number of pixels making up a line 50
max_line_gap = 39 # maximum gap in pixels between connectable line segments 19
line_image = np.copy(img) # * 0 # creating a blank to draw lines on
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
if lines is not None:
for line in lines[:3]: #7
for x1,y1,x2,y2 in line:
#cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)
cv2.line(line_image,(x1,y1),(x2,y2),(0,0,255),2)
#cv2.circle(line_image, (x1, y1), r-25, (255, 255, 0), 1)
mergi = cv2.merge((imgray, line_image))
cv2.imshow('img', img)
# cv2.imshow('line_image', line_image)
lines_edges = cv2.addWeighted(img, 0.5, line_image, 0.8, 0)
#cv2.imshow('lines_edges', lines_edges)
# CIRCLE DETECTION #################################### slow on video! ###
def circle_detection():
global output
imgcirc = img.copy()
gray = cv2.cvtColor(imgcirc, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 5, 3) # 5, 3 is good
output = img.copy()
circles = cv2.HoughCircles(imgray, cv2.HOUGH_GRADIENT, 1.3, 100, maxRadius = 100, minRadius = 3) #1.2, 100, 100, 0
#print(circles)
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
# loop over the (x, y) coordinates and radius of the circles
for (x, y, r) in circles[:2]:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
#cv2.circle(output, (x, y), r, (0, 255, 0), 2)
cv2.circle(output, (x, y), int(r*0.75), (0, 0, 0), 2)
cv2.rectangle(output, (x - 3, y - 3), (x + 3, y + 3), (255, 255, 255), 1)
cv2.rectangle(output, (x, y), (x, y), (0, 128, 255), -1)
cv2.rectangle(output, (x - 10, y - 10), (x + 10, y + 10), (0, 128, 255), 1)
#cv2.imshow('output', output)
while cap.isOpened():
ret, img = cap.read()
if img.shape[0] > 1400:
img = cv2.resize(img, None, fx = 1/4, fy = 1/4)
if img.shape[0] > 500:
img = cv2.resize(img, None, fx = 1/2, fy = 1/2)
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Call functions
horizontal_lines_detection()
circle_detection()
# Draw contours and corners onto the original video frame
#img_with_contours = cv2.addWeighted(img, 0.5, imgc, 0.5, 0)
img_with_corners = cv2.addWeighted(img, 0.9, imgcorn, 0.9, 0)
img_with_lines = cv2.addWeighted(img_with_corners, 0.5, lines_edges, 0.9, 0)
img_with_lines_circles = cv2.addWeighted(img_with_lines, 0.9, output, 0.3, 0)
img_with_lines_circles_optf = cv2.addWeighted(img_with_lines_circles, 0.9, opticalf_img, 0.6, 0)
# img_with_lines_circles1 = cv2.subtract(img_with_lines, output)
img_with_lines_output_add = img_with_lines - output
img_with_lines_circles_optf = cv2.addWeighted(img_with_lines_output_add, 0.7, opticalf_img, 0.9, 0)
# Display the modified frame
cv2.imshow('img_with_corners', img_with_corners)
cv2.imshow('img_with_lines', img_with_lines)
cv2.imshow('img_with_lines_circles', img_with_lines_circles)
cv2.imshow('img_with_lines_output_add', img_with_lines_output_add)
cv2.imshow('img_with_lines_circles_optf', img_with_lines_circles_optf)
key = cv2.waitKey(25) #controls speed of video frame in ms
if key == 27:
break
cap.release()
cv2.destroyAllWindows()```