mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 06:44:02 +00:00
304 lines
11 KiB
Python
304 lines
11 KiB
Python
import cv2
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import numpy as np
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import os
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import argparse
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# from svm import predict_image_array, load_model
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#提取西红柿，使用S+L的图像
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def extract_s_l(image_path):
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image = cv2.imread(image_path)
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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(image_path, threshold=190):
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# 读取图像
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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# 应用阈值分割
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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(image):
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# 将图像转换为灰度图像
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# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 使用Otsu阈值分割
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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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# 提取花萼，使用G-R的图像
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def extract_g_r(image):
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# image = cv2.imread(image_path)
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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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#提取西红柿，使用R-B的图像
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def extract_r_b(image_path):
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image = cv2.imread(image_path)
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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(image_path):
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image = cv2.imread(image_path)
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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(image, threshold, color=255):
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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(image1, image2, operation='and'):
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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(bin_img):
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# 使用connectedComponentsWithStats函数找到连通区域
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8)
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# 找到最大的连通区域（除了背景）
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largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
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# 创建一个新的二值图像，只显示最大的连通区域
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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(bin_img, kernel_size=(5, 5)):
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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(bin_img, kernel_size=(5, 5)):
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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(original_img, bin_img):
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bin_img_processed = close_operation(bin_img, kernel_size=(15, 15))
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# cv2.imshow('Close Operation', bin_img_processed)
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# bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19))
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# cv2.imshow('Open Operation', bin_img_processed)
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# 现在使用处理后的bin_img_processed查找轮廓
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contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 如果没有找到轮廓，直接返回原图
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if not contours:
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return original_img
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# 找到最大轮廓
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max_contour = max(contours, key=cv2.contourArea)
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# 多边形近似的精度调整
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epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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# 绘制轮廓
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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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# 使用白色填充最大轮廓
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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(original_img, bin_img):
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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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# 得到完整的西红柿二值图像，除了绿色花萼
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def fill_holes(bin_img):
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# 复制 bin_img 到 img_filled
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img_filled = bin_img.copy()
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# 获取图像的高度和宽度
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height, width = bin_img.shape
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# 创建一个掩码，比输入图像大两个像素点
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mask = np.zeros((height + 2, width + 2), np.uint8)
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# 使用 floodFill 函数填充黑色区域
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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# 反转填充后的图像
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img_filled_d = cv2.bitwise_not(img_filled)
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# 使用 bitwise_or 操作合并原图像和填充后的图像
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img_filled = cv2.bitwise_or(bin_img, img_filled)
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# 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小
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# img_filled = img_filled[:height, :width]
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img_filled_d = img_filled_d[:height, :width]
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return img_filled, img_filled_d
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def bitwise_and_rgb_with_binary(rgb_img, bin_img):
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# 将二值图像转换为三通道图像
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bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR)
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# 使用 bitwise_and 操作合并 RGB 图像和二值图像
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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(image_path, lower_hsv, upper_hsv):
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# 读取图像
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image = cv2.imread(image_path)
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# 将图像从BGR转换到HSV
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# 使用阈值获取指定区域的二值图像
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mask = cv2.inRange(hsv, lower_hsv, upper_hsv)
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# 找到二值图像的连通区域
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
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# 找到最大的连通区域（除了背景）
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largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
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# 创建一个新的二值图像，只显示最大的连通区域
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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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# 复制 new_bin_img 到 img_filled
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img_filled = new_bin_img.copy()
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# 获取图像的高度和宽度
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height, width = new_bin_img.shape
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# 创建一个掩码，比输入图像大两个像素点
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mask = np.zeros((height + 2, width + 2), np.uint8)
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# 使用 floodFill 函数填充黑色区域
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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# 反转填充后的图像
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img_filled_inv = cv2.bitwise_not(img_filled)
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# 使用 bitwise_or 操作合并原图像和填充后的图像
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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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def main():
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data',
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help='the directory path of images')
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parser.add_argument('--threshold_s_l', type=int, default=180,
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help='the threshold for s_l')
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parser.add_argument('--threshold_r_b', type=int, default=15,
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help='the threshold for r_b')
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args = parser.parse_args()
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for img_file in os.listdir(args.dir_path):
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if img_file.endswith('.bmp'):
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img_path = os.path.join(args.dir_path, img_file)
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s_l = extract_s_l(img_path)
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otsu_thresholded = otsu_threshold(s_l)
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img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded)
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img_fore_defect = extract_g_r(img_fore)
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cv2.imshow('img_fore_defect1', img_fore_defect)
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img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b)
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cv2.imshow('img_fore_defect2', img_fore_defect)
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thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l)
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new_bin_img = largest_connected_component(thresholded_s_l)
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# zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE))
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# cv2.imshow('zhongggggg', zhongggggg)
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new_otsu_bin_img = largest_connected_component(otsu_thresholded)
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filled_img, defect = fill_holes(new_bin_img)
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defect = bitwise_and_rgb_with_binary(cv2.imread(img_path), defect)
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cv2.imshow('defect', defect)
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edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img)
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org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img)
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fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask)
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fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20)
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#统计白色像素点个数
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print(np.sum(fore_g_r_t == 255))
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print(np.sum(mask == 255))
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print(np.sum(fore_g_r_t == 255)/np.sum(mask == 255))
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fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t)
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# cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
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org_defect_new = bitwise_and_rgb_with_binary(edge, new_bin_img)
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res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
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nogreen = bitwise_and_rgb_with_binary(edge, res)
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cv2.imshow('nogreen', nogreen)
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white = find_reflection(img_path)
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# SVM预测
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# 加载模型
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# model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib')
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# 对图像进行预测
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# predicted_mask = predict_image_array(image, model, scaler)
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cv2.imshow('white', white)
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cv2.imshow('fore', fore)
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cv2.imshow('fore_g_r_t', fore_g_r_t)
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cv2.imshow('mask', mask)
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cv2.imshow('res', res)
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# lower_hsv = np.array([19, 108, 15])
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# upper_hsv = np.array([118, 198, 134])
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# max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv)
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# cv2.imshow('Max Connected Area', max_connected_area)
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# 显示原始图像
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original_img = cv2.imread(img_path)
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cv2.imshow('Original', original_img)
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cv2.imshow('thresholded_s_l', thresholded_s_l)
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cv2.imshow('Largest Connected Component', new_bin_img)
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cv2.imshow('Filled', filled_img)
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cv2.imshow('Defect', defect)
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cv2.imshow('Org_defect', org_defect)
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cv2.imshow('otsu_thresholded', new_otsu_bin_img)
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#显示轮廓
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cv2.imshow('Edge', edge)
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# 等待用户按下任意键
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cv2.waitKey(0)
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# 关闭所有窗口
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main() |