我目前正在尝试检测锥形材料的边缘并找到该形状的角度。
我有一个这样的原始图像：
我写了一个 Python 程序来检测勃艮第背景和灰色材料之间的边缘（代码在下面给出）并且它检测边缘是这样的：
但我不知道如何从检测到的边缘找到角度。有任何想法吗？
import glob import cv2 import numpy as np import ctypes import math def get_screen_resolution(): user32 = ctypes.windll.user32 return user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) def process_photos(): # Get the list of image files in the directory image_files = glob.glob("LEF_*.jpg") # Process each image file for image_file in image_files: process_image(image_file) def process_image(image_file): # Load the image image = cv2.imread(image_file) # Convert the image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Perform color-based segmentation to extract the burgundy regions lower_burgundy = np.array([0, 0, 100]) # Adjust the lower threshold for burgundy color upper_burgundy = np.array([100, 100, 255]) # Adjust the upper threshold for burgundy color mask = cv2.inRange(image, lower_burgundy, upper_burgundy) # Apply a Gaussian blur to the mask to reduce noise blurred_mask = cv2.GaussianBlur(mask, (5, 5), 0) # Perform Canny edge detection on the grayscale image edges_gray = cv2.Canny(gray, 50, 150) # Combine the edges with the burgundy regions using bitwise AND combined_edges = cv2.bitwise_and(edges_gray, blurred_mask) # Dilate the edges to enhance connectivity dilated = cv2.dilate(combined_edges, None, iterations=2) # Find contours of the edges contours, _ = cv2.findContours(dilated.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Approximate the contours to straight lines lines = cv2.HoughLinesP(dilated, rho=1, theta=np.pi/180, threshold=100, minLineLength=100, maxLineGap=10) # Draw the lines on the original image if lines is not None: # Compute the mean line from the detected line segments mean_x1, mean_y1, mean_x2, mean_y2 = np.mean(lines[:, 0, :], axis=0, dtype=np.int32) # Draw the mean line cv2.line(image, (mean_x1, mean_y1), (mean_x2, mean_y2), (0, 255, 0), 2) # Compute the angle of the mean line angle = math.atan2(mean_y2 - mean_y1, mean_x2 - mean_x1) * 180 / np.pi print("Angle:", angle) # Draw the contours on the original image cv2.drawContours(image, contours, -1, (0, 255, 0), 2) # Resize the image to fit the screen resolution screen_width, screen_height = get_screen_resolution() image = cv2.resize(image, (screen_width, screen_height)) # Display the processed image cv2.imshow("Processed Image", image) cv2.waitKey(0) cv2.destroyAllWindows() process_photos()
我对此进行了尝试，使用了一些不同的技术，因此您可以使用、调整和合并您喜欢的任何方面与您自己的方法。
值得注意的是，我使用了：
cv.inRange()
cv.findContours()
#!/usr/bin/env python3 import cv2 as cv import numpy as np from scipy import ndimage import math def lowpass(isignal, w): # Low pass filter # isignal is the input signal # osignal is the output signal # w is the window width that decides how many pixels affect the output kernel = np.lib.pad(np.linspace(1,3,w), (0,w-1), 'reflect') kernel = np.divide(kernel,np.sum(kernel)) osignal = ndimage.convolve(isignal, kernel) return osignal # Load image im = cv.imread('mountain.jpg') h, w = im.shape[:2] # Convert to HSV and segment burgundy background HSV = cv.cvtColor(im, cv.COLOR_BGR2HSV) # Low and high HSV threshold for background bglo = np.uint8([5,30,0]) bghi = np.uint8([30,255,255]) bg = cv.inRange(HSV, bglo, bghi) # Save just for debug, easy cleanup with: rm DEBUG-*png cv.imwrite('DEBUG-bg.png', bg) # Get distance from top of image to first black pixel in each col coltops = (bg!=255).argmax(axis=0) # Get distance from bottom of last black pixel in each col y = h - coltops # Smooth the the curve, I chose image width/10 as the window size - you can experiment ysmooth = lowpass(y, int(w/10)) for x in range(im.shape[1]): # illustration only - can be deleted # Plot smooth curve in red on top of original image # illustration only - can be deleted im[h-ysmooth[x],x] = [0,0,255] # illustration only - can be deleted cv.imwrite('DEBUG-smooth.png', im) # illustration only - can be deleted # Now locate the x-coordinate of the peak of the smoothed curve to get the mountain centre cx = ysmooth.argmax() # prints 792 print(f'DEBUG: x-coordinate of mountain centre is {cx}') # Now split original (not smoothed) y-values into two separate lines - left and right side yLeft = y[:cx] yRight = y[cx:] import matplotlib.pyplot as plt # illustration only - can be deleted # Fit straight line to left side x = np.arange(len(yLeft)) a, b = np.polyfit(x, yLeft, 1) angle = math.degrees(math.atan(a)) print(f'Left side: y = {a:0.3f} * x + {b:0.3f}, => angle: {angle:0.3f}') # Plot the points plt.scatter(x, yLeft, color='lime') # illustration only - can be deleted # Draw line of best fit to plot plt.plot(x, a*x+b, color='steelblue', linestyle='--', linewidth=2) # illustration only - can be deleted plt.savefig('left.png') # illustration only - can be deleted plt.clf() # clear for right side # illustration only - can be deleted # Fit straight line to right side x = np.arange(len(yRight)) a, b = np.polyfit(x, yRight, 1) angle = math.degrees(math.atan(a)) print(f'Right side: y = {a:0.3f} * x + {b:0.3f}, => angle: {angle:0.3f}') # Plot the points plt.scatter(x, yRight, color='lime') # illustration only - can be deleted # Draw line of best fit to plot plt.plot(x, a*x+b, color='steelblue', linestyle='--', linewidth=2)# illustration only - can be deleted plt.savefig('right.png') # illustration only - can be deleted
你可以放心地忽略所有的matplotlib.pyplot东西——这只是为了说明。
matplotlib.pyplot
它输出这个：
Left side: y = 0.598 * x + 213.244, => angle: 30.873 Right side: y = -0.687 * x + 728.566, => angle: -34.492
分割后的背景 ( DEBUG-bg.png) 如下所示：
DEBUG-bg.png
平滑的边缘 ( DEBUG-smooth.png) 如下所示：
DEBUG-smooth.png
绘制的左侧和右侧如下所示：
