mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 06:44:02 +00:00
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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def dual_threshold_and_max_component(image_path, hue_value=37, hue_delta=10, value_target=30, value_delta=10):
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# 读取图像
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image = cv2.imread(image_path)
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# 检查图像是否读取成功
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if image is None:
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print("Error: Image could not be read.")
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return
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# 将图像从BGR转换到HSV色彩空间
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hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# 创建H通道阈值掩码
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lower_hue = np.array([hue_value - hue_delta, 0, 0])
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upper_hue = np.array([hue_value + hue_delta, 255, 255])
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hue_mask = cv2.inRange(hsv_image, lower_hue, upper_hue)
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# 创建V通道排除中心值的掩码
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lower_value_1 = np.array([0, 0, 0])
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upper_value_1 = np.array([180, 255, value_target - value_delta])
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lower_value_2 = np.array([0, 0, value_target + value_delta])
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upper_value_2 = np.array([180, 255, 255])
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value_mask_1 = cv2.inRange(hsv_image, lower_value_1, upper_value_1)
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value_mask_2 = cv2.inRange(hsv_image, lower_value_2, upper_value_2)
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value_mask = cv2.bitwise_or(value_mask_1, value_mask_2)
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# 合并H通道和V通道掩码
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combined_mask = cv2.bitwise_and(hue_mask, value_mask)
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# 形态学操作 - 开运算，去除小的粘连
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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combined_mask = cv2.morphologyEx(combined_mask, cv2.MORPH_OPEN, kernel)
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# 连通域分析
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(combined_mask, 4, cv2.CV_32S)
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# 找出最大的连通区域（除了背景）
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max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 跳过背景
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max_mask = (labels == max_label).astype(np.uint8) * 255
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cv2.imshow('max_mask', max_mask)
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# 使用掩码生成结果图像
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result_image = cv2.bitwise_and(image, image, mask=max_mask)
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# 设置背景为白色
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result_image[max_mask == 0] = [255, 255, 255]
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# 将结果图像从BGR转换到RGB以便正确显示
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result_image_rgb = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
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# 使用matplotlib显示原始图像和结果图像
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plt.figure(figsize=(10, 5))
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plt.subplot(1, 2, 1)
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plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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plt.title('Original Image')
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plt.axis('off')
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plt.subplot(1, 2, 2)
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plt.imshow(result_image_rgb)
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plt.title('Largest Connected Component on White Background')
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plt.axis('off')
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plt.show()
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# 使用函数
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image_path = r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img/39.bmp' # 替换为你的图片路径
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dual_threshold_and_max_component(image_path) |