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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import cv2
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import numpy as np
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def segment_image_by_variance(image_path, m, n, variance_threshold):
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# 读取图像
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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if image is None:
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print("Error loading image")
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return None
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# 图像的高度和宽度
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h, w = image.shape
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# 计算每个块的尺寸
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block_h, block_w = h // m, w // n
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# 创建空白图像
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segmented_image = np.zeros_like(image)
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# 遍历每个块
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for row in range(m):
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for col in range(n):
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# 计算块的位置
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y1, x1 = row * block_h, col * block_w
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y2, x2 = y1 + block_h, x1 + block_w
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# 提取块
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block = image[y1:y2, x1:x2]
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# 计算方差
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variance = np.var(block)
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# 根据方差设置新图像的对应区块
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if variance > variance_threshold:
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segmented_image[y1:y2, x1:x2] = 1
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else:
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segmented_image[y1:y2, x1:x2] = 0
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# 将新图像的值扩展到0-255范围，以便可视化
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segmented_image *= 255
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return segmented_image
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# 示例用法
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image_path = '/Users/xs/PycharmProjects/super-tomato/tomato_img_25/60.bmp' # 替换为你的番茄图像路径
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m, n = 300, 300 # 划分的区块数
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variance_threshold = 80 # 方差的阈值
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segmented_image = segment_image_by_variance(image_path, m, n, variance_threshold)
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if segmented_image is not None:
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cv2.imshow("Segmented Image", segmented_image)
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cv2.imshow("Original Image", cv2.imread(image_path))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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