64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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# 读取图片
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image = cv2.imread('p41.png')
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# 将图片转换为RGB格式
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# 增强饱和度
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hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV)
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hsv[..., 1] = hsv[..., 1] * 6 # 增强饱和度
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enhanced_image_rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
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# 进行Prewitt边缘检测
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gray_image = cv2.cvtColor(enhanced_image_rgb, cv2.COLOR_RGB2GRAY)
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prewittx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=3)
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prewitty = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=3)
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prewitt_edge = cv2.magnitude(prewittx, prewitty)
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_, thresholded_image = cv2.threshold(prewitt_edge, 50, 255, cv2.THRESH_BINARY)
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# 寻找轮廓
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contours, hierarchy = cv2.findContours(thresholded_image.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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# 过滤面积小于阈值的轮廓
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filtered_contours = []
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# 计算和显示每个轮廓的最小斜率
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for contour in contours:
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rect = cv2.minAreaRect(contour) # 获取最小外接矩形
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box = cv2.boxPoints(rect) # 计算矩形的四个角点
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box = np.intp(box) # 角点坐标整数化 # 角点坐标整数化
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# 求边长
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widths = [np.linalg.norm(box[i] - box[(i + 1) % 4]) for i in range(4)]
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sorted_widths = sorted(widths)
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# 宽和高
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width, height = sorted_widths[0], sorted_widths[1]
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# 角度调整
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angle = rect[2]
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# if width < height:
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# angle += 90
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area = width * height
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# 计算斜率
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slope = np.tan(np.radians(angle))
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if angle < 45 and area > 5000:
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filtered_contours.append(contour)
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# 将二值图像转换为RGB格式，以便能在其上绘制彩色轮廓
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contoured_image = cv2.cvtColor(thresholded_image.astype(np.uint8), cv2.COLOR_GRAY2RGB)
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# 使用红色来绘制轮廓
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cv2.drawContours(contoured_image, filtered_contours, -1, (255, 0, 0), 2)
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# 使用matplotlib显示图像
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plt.figure(figsize=(8, 8))
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plt.imshow(contoured_image)
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plt.title('Filtered Image with Red Contours and Slope Info')
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plt.axis('off')
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plt.show()
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