mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
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130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/4/11 15:18
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# @Author : TG
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# @File : parameter calculation.py
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# @Software: PyCharm
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import cv2
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import numpy as np
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from scipy.ndimage.measurements import label, find_objects
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def get_tomato_dimensions(edge_img):
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"""
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根据番茄边缘二值化轮廓图,计算番茄的长径、短径和长短径比值。
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使用最小外接矩形和最小外接圆两种方法。
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参数:
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edge_img (numpy.ndarray): 番茄边缘二值化轮廓图,背景为黑色,番茄区域为白色。
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返回:
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tuple: (长径, 短径, 长短径比值)
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"""
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# 最小外接矩形
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rect = cv2.minAreaRect(cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0])
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major_axis, minor_axis = rect[1]
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aspect_ratio = max(major_axis, minor_axis) / min(major_axis, minor_axis)
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# # 最小外接圆
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# (x, y), radius = cv2.minEnclosingCircle(
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# cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0])
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# diameter = 2 * radius
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# aspect_ratio_circle = 1.0
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return (max(major_axis, minor_axis), min(major_axis, minor_axis), aspect_ratio)
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def get_defect_info(defect_img):
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"""
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根据番茄区域缺陷二值化轮廓图,计算缺陷区域的个数和总面积。
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参数:
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defect_img (numpy.ndarray): 番茄区域缺陷二值化轮廓图,背景为黑色,番茄区域为白色,缺陷区域为黑色连通域。
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返回:
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tuple: (缺陷区域个数, 缺陷区域像素面积，缺陷像素总面积)
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"""
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nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(defect_img, connectivity=4)
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max_area = max(stats[i, cv2.CC_STAT_AREA] for i in range(1, nb_components))
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areas = []
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for i in range(1, nb_components):
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area = stats[i, cv2.CC_STAT_AREA]
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if area != max_area:
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areas.append(area)
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number_defects = len(areas)
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total_pixels = sum(areas)
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return number_defects, areas, total_pixels
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def connected_components_analysis(binary_image):
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"""
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从二值化图像计算黑色连通域个数和各个黑色连通域像素面积及黑色像素总面积。
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参数:
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binary_image (numpy.ndarray): 二值化图像, 其中 0 表示白色, 1 表示黑色。
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返回:
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num_components (int): 黑色连通域的个数。
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component_areas (list): 每个黑色连通域的像素面积。
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total_black_area (int): 黑色像素的总面积。
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"""
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# 标记连通域
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labeled_image, num_components = label(binary_image)
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# 获取每个连通域的像素位置
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slices = find_objects(labeled_image)
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# 计算每个连通域的像素面积
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component_areas = []
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for slice_obj in slices:
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component_area = np.sum(binary_image[slice_obj])
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component_areas.append(component_area)
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# 计算黑色像素的总面积
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total_black_area = np.sum(binary_image)
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return num_components, component_areas, total_black_area
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def main():
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# 读取图像
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defect_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\Largest Connected Component_screenshot_15.04.2024.png', 0)
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edge_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\mask_screenshot_15.04.2024.png', 0)
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filled_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\Filled_screenshot_15.04.2024.png', 0)
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# print(defect_image.shape)
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# print(edge_image.shape)
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# print(filled_image.shape)
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# 执行二值化处理
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_, thresh_defect = cv2.threshold(defect_image, 127, 255, cv2.THRESH_BINARY_INV)
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_, thresh_edge = cv2.threshold(edge_image, 127, 255, cv2.THRESH_BINARY)
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_, thresh_filled = cv2.threshold(filled_image, 127, 255, cv2.THRESH_BINARY)
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print(thresh_defect.shape)
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print(thresh_edge.shape)
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print(thresh_filled.shape)
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# # 直接使用二值图像
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# thresh_defect = defect_image
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# thresh_edge = edge_image
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# thresh_filled = filled_image
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# 获取番茄的长径、短径和长短径比值
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major_axis, minor_axis, aspect_ratio = get_tomato_dimensions(thresh_edge)
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# 获取缺陷区域的个数和总面积
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num_defects, areas, total_pixels = get_defect_info(thresh_defect)
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# 获取黑色连通域的个数、各个连通域的面积和总黑色面积
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num_components, component_areas, total_black_area = connected_components_analysis(thresh_filled)
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print(f'番茄的长径为{major_axis},短径为{minor_axis},长短径比值为{aspect_ratio}')
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print(f'缺陷区域的个数为{num_defects},像素个数分别为{areas},缺陷总面积为{total_pixels}')
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print(f'黑色连通域的个数为{num_components},像素个数分别为{component_areas},黑色像素总面积为{total_black_area}')
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if __name__ == '__main__':
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main()
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