mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 06:44:02 +00:00
551 lines
22 KiB
Python
551 lines
22 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/6/4 21:34
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# @Author : GG
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# @File : classifer.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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import logging
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import os
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import utils
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from root_dir import ROOT_DIR
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from sklearn.ensemble import RandomForestRegressor
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import joblib
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class Tomato:
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def __init__(self):
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''' 初始化 Tomato 类。'''
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pass
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def extract_s_l(self, image):
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'''
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提取图像的 S 通道（饱和度）和 L 通道（亮度），并将两者相加。
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:param image: 输入的 BGR 图像
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:return: S 通道和 L 通道相加的结果
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'''
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
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s_channel = hsv[:, :, 1]
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l_channel = lab[:, :, 0]
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result = cv2.add(s_channel, l_channel)
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return result
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def find_reflection(self, image, threshold=190):
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'''
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通过阈值处理识别图像中的反射区域。
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:param image: 输入的单通道图像
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:param threshold: 用于二值化的阈值
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:return: 二值化后的图像，高于阈值的部分为白色，其余为黑色
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'''
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_, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY)
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return reflection
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def otsu_threshold(self, image):
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'''
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使用 Otsu 大津法自动计算并应用阈值，进行图像的二值化处理。
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:param image: 输入的单通道图像
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:return: 二值化后的图像
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'''
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_, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return binary
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def extract_g_r(self, image):
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'''
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提取图像中的 G 通道（绿色），放大并减去 R 通道（红色）。
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:param image: 输入的 BGR 图像
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:return: G 通道乘以 1.5 后减去 R 通道的结果
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'''
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g_channel = image[:, :, 1]
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r_channel = image[:, :, 2]
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result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel)
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return result
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def extract_r_b(self, image):
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'''
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提取图像中的 R 通道（红色）和 B 通道（蓝色），并进行相减。
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:param image: 输入的 BGR 图像
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:return: R 通道减去 B 通道的结果
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'''
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r_channel = image[:, :, 2]
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b_channel = image[:, :, 0]
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result = cv2.subtract(r_channel, b_channel)
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return result
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def extract_r_g(self, image):
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'''
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提取图像中的 R 通道（红色）和 G 通道（绿色），并进行相减。
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:param image: 输入的 BGR 图像
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:return: R 通道减去 G 通道的结果
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'''
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r_channel = image[:, :, 2]
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g_channel = image[:, :, 1]
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result = cv2.subtract(r_channel, g_channel)
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return result
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def threshold_segmentation(self, image, threshold, color=255):
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'''
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对图像进行阈值分割，高于阈值的部分设置为指定的颜色。
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:param image: 输入的单通道图像
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:param threshold: 阈值
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:param color: 设置的颜色值
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:return: 分割后的二值化图像
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'''
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_, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY)
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return result
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def bitwise_operation(self, image1, image2, operation='and'):
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'''
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对两幅图像执行位运算（与或运算）。
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:param image1: 第一幅图像
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:param image2: 第二幅图像
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:param operation: 执行的操作类型（'and' 或 'or'）
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:return: 位运算后的结果
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'''
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if operation == 'and':
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result = cv2.bitwise_and(image1, image2)
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elif operation == 'or':
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result = cv2.bitwise_or(image1, image2)
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else:
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raise ValueError("operation must be 'and' or 'or'")
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return result
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def largest_connected_component(self, bin_img):
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'''
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提取二值图像中的最大连通区域。
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:param bin_img: 输入的二值图像
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:return: 只包含最大连通区域的二值图像
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'''
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8)
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if num_labels <= 1:
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return np.zeros_like(bin_img)
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largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
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new_bin_img = np.zeros_like(bin_img)
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new_bin_img[labels == largest_label] = 255
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return new_bin_img
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def close_operation(self, bin_img, kernel_size=(5, 5)):
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'''
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对二值图像进行闭运算，用于消除内部小孔和连接接近的对象。
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:param bin_img: 输入的二值图像
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:param kernel_size: 核的大小
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:return: 进行闭运算后的图像
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'''
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
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closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel)
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return closed_img
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def open_operation(self, bin_img, kernel_size=(5, 5)):
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'''
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对二值图像进行开运算，用于去除小的噪点。
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:param bin_img: 输入的二值图像
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:param kernel_size: 核的大小
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:return: 进行开运算后的图像
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'''
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
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opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel)
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return opened_img
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def draw_tomato_edge(self, original_img, bin_img):
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'''
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在原始图像上绘制最大西红柿轮廓的近似多边形。
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:param original_img: 原始 BGR 图像
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:param bin_img: 西红柿的二值图像
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:return: 带有绘制边缘的原始图像和边缘掩码
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'''
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bin_img_processed = self.close_operation(bin_img, kernel_size=(15, 15))
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contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return original_img, np.zeros_like(bin_img)
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max_contour = max(contours, key=cv2.contourArea)
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epsilon = 0.0006 * cv2.arcLength(max_contour, True)
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3)
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mask = np.zeros_like(bin_img)
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cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED)
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return original_img, mask
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def draw_tomato_edge_convex_hull(self, original_img, bin_img):
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'''
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在原始图像上绘制最大西红柿轮廓的凸包。
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:param original_img: 原始 BGR 图像
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:param bin_img: 西红柿的二值图像
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:return: 带有绘制凸包的原始图像
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'''
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bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0)
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contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return original_img
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max_contour = max(contours, key=cv2.contourArea)
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hull = cv2.convexHull(max_contour)
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cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3)
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return original_img
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def fill_holes(self, bin_img):
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'''
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使用 floodFill 算法填充图像中的孔洞。
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:param bin_img: 输入的二值图像
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:return: 填充后的图像
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'''
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img_filled = bin_img.copy()
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height, width = bin_img.shape
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mask = np.zeros((height + 2, width + 2), np.uint8)
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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img_filled_inv = cv2.bitwise_not(img_filled)
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img_filled = cv2.bitwise_or(bin_img, img_filled_inv)
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return img_filled
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def bitwise_and_rgb_with_binary(self, rgb_img, bin_img):
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'''
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将 RGB 图像与二值图像进行按位与操作，用于将二值区域应用于原始图像。
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:param rgb_img: 原始 RGB 图像
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:param bin_img: 二值图像
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:return: 按位与后的结果图像
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'''
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bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR)
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result = cv2.bitwise_and(rgb_img, bin_img_3channel)
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return result
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def extract_max_connected_area(self, image, lower_hsv, upper_hsv):
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'''
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提取图像中满足 HSV 范围条件的最大连通区域，并填充孔洞。
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:param image: 输入的 BGR 图像
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:param lower_hsv: HSV 范围的下限
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:param upper_hsv: HSV 范围的上限
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:return: 处理后的图像
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'''
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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mask = cv2.inRange(hsv, lower_hsv, upper_hsv)
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
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largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
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new_bin_img = np.zeros_like(mask)
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new_bin_img[labels == largest_label] = 255
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img_filled = new_bin_img.copy()
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height, width = new_bin_img.shape
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mask = np.zeros((height + 2, width + 2), np.uint8)
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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img_filled_inv = cv2.bitwise_not(img_filled)
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img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv)
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return img_filled
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class Passion_fruit:
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def __init__(self, hue_value=37, hue_delta=10, value_target=25, value_delta=10):
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# 初始化常用参数
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self.hue_value = hue_value
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self.hue_delta = hue_delta
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self.value_target = value_target
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self.value_delta = value_delta
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def create_mask(self, hsv_image):
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# 创建H通道阈值掩码
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lower_hue = np.array([self.hue_value - self.hue_delta, 0, 0])
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upper_hue = np.array([self.hue_value + self.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, self.value_target - self.value_delta])
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lower_value_2 = np.array([0, 0, self.value_target + self.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_1 = cv2.bitwise_not(value_mask_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_and(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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return cv2.bitwise_and(hue_mask, value_mask)
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def apply_morphology(self, mask):
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# 应用形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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return cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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def find_largest_component(self, mask):
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# 寻找最大连通组件
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, 4, cv2.CV_32S)
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if num_labels < 2:
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return None # 没有找到显著的组件
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max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 跳过背景
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return (labels == max_label).astype(np.uint8) * 255
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def draw_contours_on_image(self, original_image, mask_image):
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"""
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在原图上绘制轮廓
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:param original_image: 原图的NumPy数组
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:param mask_image: 轮廓mask的NumPy数组
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:return: 在原图上绘制轮廓后的图像
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"""
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# 确保mask_image是二值图像
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_, binary_mask = cv2.threshold(mask_image, 127, 255, cv2.THRESH_BINARY)
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# 查找mask图像中的轮廓
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contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 在原图上绘制轮廓
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cv2.drawContours(original_image, contours, -1, (0, 255, 0), 2)
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return original_image
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def bitwise_and_rgb_with_binary(self, rgb_img, bin_img):
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'''
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将 RGB 图像与二值图像进行按位与操作，用于将二值区域应用于原始图像。
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:param rgb_img: 原始 RGB 图像
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:param bin_img: 二值图像
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:return: 按位与后的结果图像
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'''
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bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR)
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result = cv2.bitwise_and(rgb_img, bin_img_3channel)
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return result
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class Spec_predict(object):
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def __init__(self, load_from=None, debug_mode=False, class_weight=None):
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if load_from is None:
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self.model = RandomForestRegressor(n_estimators=100)
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else:
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self.load(load_from)
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self.log = utils.Logger(is_to_file=debug_mode)
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self.debug_mode = debug_mode
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def load(self, path=None):
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if path is None:
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path = os.path.join(ROOT_DIR, 'models')
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model_files = os.listdir(path)
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if len(model_files) == 0:
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self.log.log("No model found!")
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return 1
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self.log.log("./ Models Found:")
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_ = [self.log.log("├--" + str(model_file)) for model_file in model_files]
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file_times = [model_file[6:-2] for model_file in model_files]
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latest_model = model_files[int(np.argmax(file_times))]
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self.log.log("└--Using the latest model: " + str(latest_model))
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path = os.path.join(ROOT_DIR, "models", str(latest_model))
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if not os.path.isabs(path):
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logging.warning('给的是相对路径')
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return -1
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if not os.path.exists(path):
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logging.warning('文件不存在')
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return -1
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with open(path, 'rb') as f:
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model_dic = joblib.load(f)
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self.model = model_dic['model']
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return 0
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def predict(self, data_x):
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'''
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对数据进行预测
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:param data_x: 波段选择后的数据
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:return: 预测结果二值化后的数据，0为背景，1为黄芪,2为杂质2，3为杂质1，4为甘草片，5为红芪
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'''
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data_y = self.model.predict(data_x)
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return data_y
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# def get_tomato_dimensions(edge_img):
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# """
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# 根据边缘二值化轮廓图,计算果子的长径、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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#
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# 参数:
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# edge_img (numpy.ndarray): 边缘二值化轮廓图,背景为黑色,番茄区域为白色。
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#
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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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# if edge_img is None or edge_img.any() == 0:
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# return (0, 0)
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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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#
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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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#
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# return (max(major_axis, minor_axis), min(major_axis, minor_axis))
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# def get_defect_info(defect_img):
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# """
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# 根据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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#
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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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#
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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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# # 检查输入是否为空
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# if defect_img is None or defect_img.any() == 0:
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# return (0, 0)
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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:
|
||
# areas.append(area)
|
||
# number_defects = len(areas)
|
||
# total_pixels = sum(areas)
|
||
# return number_defects, total_pixels
|
||
|
||
class Data_processing:
|
||
def __init__(self):
|
||
pass
|
||
|
||
def contour_process(self, image_array):
|
||
# 应用中值滤波
|
||
image_filtered = cv2.medianBlur(image_array, 5)
|
||
|
||
# 形态学闭操作
|
||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
|
||
image_closed = cv2.morphologyEx(image_filtered, cv2.MORPH_CLOSE, kernel)
|
||
|
||
# 查找轮廓
|
||
contours, _ = cv2.findContours(image_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
|
||
# 创建空白图像以绘制轮廓
|
||
image_contours = np.zeros_like(image_array)
|
||
|
||
# 进行多边形拟合并填充轮廓
|
||
for contour in contours:
|
||
epsilon = 0.001 * cv2.arcLength(contour, True)
|
||
approx = cv2.approxPolyDP(contour, epsilon, True)
|
||
if cv2.contourArea(approx) > 100: # 仅处理较大的轮廓
|
||
cv2.drawContours(image_contours, [approx], -1, (255, 255, 255), -1)
|
||
|
||
return image_contours
|
||
|
||
def analyze_ellipse(self, image_array):
|
||
# 查找白色区域的轮廓
|
||
_, binary_image = cv2.threshold(image_array, 127, 255, cv2.THRESH_BINARY)
|
||
contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
|
||
# 初始化变量用于存储最大轮廓的长径和短径
|
||
major_axis = 0
|
||
minor_axis = 0
|
||
|
||
# 对每个找到的轮廓，找出可以包围它的最小椭圆，并计算长径和短径
|
||
for contour in contours:
|
||
if len(contour) >= 5: # 至少需要5个点来拟合椭圆
|
||
ellipse = cv2.fitEllipse(contour)
|
||
(center, axes, orientation) = ellipse
|
||
major_axis0 = max(axes)
|
||
minor_axis0 = min(axes)
|
||
# 更新最大的长径和短径
|
||
if major_axis0 > major_axis:
|
||
major_axis = major_axis0
|
||
minor_axis = minor_axis0
|
||
|
||
return major_axis, minor_axis
|
||
|
||
def analyze_defect(self, image_array):
|
||
# 查找白色区域的轮廓
|
||
_, binary_image = cv2.threshold(image_array, 127, 255, cv2.THRESH_BINARY)
|
||
contours_white, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
|
||
# 初始化统计数据
|
||
count_black_areas = 0
|
||
total_pixels_black_areas = 0
|
||
|
||
# 对于每个白色区域，查找内部的黑色小区域
|
||
for contour in contours_white:
|
||
# 创建一个mask以查找内部的黑色区域
|
||
mask = np.zeros_like(image_array)
|
||
cv2.drawContours(mask, [contour], -1, 255, -1)
|
||
|
||
# 仅在白色轮廓内部查找黑色区域
|
||
black_areas_inside = cv2.bitwise_and(cv2.bitwise_not(image_array), mask)
|
||
|
||
# 查找黑色区域的轮廓
|
||
contours_black, _ = cv2.findContours(black_areas_inside, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
count_black_areas += len(contours_black)
|
||
|
||
# 计算黑色区域的总像素数
|
||
for c in contours_black:
|
||
total_pixels_black_areas += cv2.contourArea(c)
|
||
|
||
number_defects = count_black_areas
|
||
total_pixels = total_pixels_black_areas
|
||
return number_defects, total_pixels
|
||
|
||
def weight_estimates(self, long_axis, short_axis):
|
||
"""
|
||
根据西红柿的长径、短径和直径估算其体积。
|
||
使用椭圆体积公式计算体积。
|
||
参数:
|
||
diameter (float): 西红柿的直径
|
||
long_axis (float): 西红柿的长径
|
||
short_axis (float): 西红柿的短径
|
||
返回:
|
||
float: 估算的西红柿体积
|
||
"""
|
||
density = 0.652228972
|
||
a = long_axis / 2
|
||
b = short_axis /2
|
||
volume = 4 / 3 * np.pi * a * b * b
|
||
weigth = volume * density
|
||
return weigth
|
||
def analyze_tomato(self, img):
|
||
"""
|
||
分析给定图像，提取和返回西红柿的长径、短径、缺陷数量和缺陷总面积，并返回处理后的图像。
|
||
使用 Tomoto 类的图像处理方法，以及自定义的尺寸和缺陷信息获取函数。
|
||
参数:
|
||
img (numpy.ndarray): 输入的 BGR 图像
|
||
返回:
|
||
tuple: (长径, 短径, 缺陷区域个数, 缺陷区域总像素, 处理后的图像)
|
||
"""
|
||
tomato = Tomato() # 创建 Tomato 类的实例
|
||
# 设置 S-L 通道阈值并处理图像
|
||
threshold_s_l = 180
|
||
threshold_fore_g_r_t = 20
|
||
s_l = tomato.extract_s_l(img)
|
||
thresholded_s_l = tomato.threshold_segmentation(s_l, threshold_s_l)
|
||
new_bin_img = tomato.largest_connected_component(thresholded_s_l)
|
||
# 绘制西红柿边缘并获取缺陷信息
|
||
edge, mask = tomato.draw_tomato_edge(img, new_bin_img)
|
||
org_defect = tomato.bitwise_and_rgb_with_binary(edge, new_bin_img)
|
||
fore = tomato.bitwise_and_rgb_with_binary(img, mask)
|
||
fore_g_r_t = tomato.threshold_segmentation(tomato.extract_g_r(fore), threshold=threshold_fore_g_r_t)
|
||
# 统计白色像素点个数
|
||
# print(np.sum(fore_g_r_t == 255))
|
||
# print(np.sum(mask == 255))
|
||
# print(np.sum(fore_g_r_t == 255) / np.sum(mask == 255))
|
||
green_percentage = np.sum(fore_g_r_t == 255) / np.sum(mask == 255)
|
||
green_percentage = round(green_percentage, 2) * 100
|
||
# 获取西红柿的尺寸信息
|
||
long_axis, short_axis = self.analyze_ellipse(mask)
|
||
# 获取缺陷信息
|
||
number_defects, total_pixels = self.analyze_defect(new_bin_img)
|
||
# 将处理后的图像转换为 RGB 格式
|
||
rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB)
|
||
diameter = (long_axis + short_axis) / 2
|
||
return diameter, green_percentage, number_defects, total_pixels, rp
|
||
|
||
def analyze_passion_fruit(self, img, hue_value=37, hue_delta=10, value_target=25, value_delta=10):
|
||
if img is None:
|
||
print("Error: 无图像数据.")
|
||
return None
|
||
|
||
# 创建PassionFruit类的实例
|
||
pf = Passion_fruit(hue_value=hue_value, hue_delta=hue_delta, value_target=value_target, value_delta=value_delta)
|
||
|
||
hsv_image = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||
combined_mask = pf.create_mask(hsv_image)
|
||
combined_mask = pf.apply_morphology(combined_mask)
|
||
max_mask = pf.find_largest_component(combined_mask)
|
||
|
||
contour_mask = self.contour_process(max_mask)
|
||
long_axis, short_axis = self.analyze_ellipse(contour_mask)
|
||
weigth = self.weight_estimates(long_axis, short_axis)
|
||
number_defects, total_pixels = self.analyze_defect(max_mask)
|
||
edge = pf.draw_contours_on_image(img, contour_mask)
|
||
org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask)
|
||
rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB)
|
||
diameter = (long_axis + short_axis) / 2
|
||
|
||
return diameter, weigth, number_defects, total_pixels, rp
|