145 lines
5.6 KiB
Python
145 lines
5.6 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@File : roi_fix.py
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@Contact : zpyovo@hotmail.com
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@License : (C)Copyright 2018-2019, Lab501-TransferLearning-SCUT
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@Description :
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@Modify Time @Author @Version @Desciption
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------------ ------- -------- -----------
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2024/2/27 00:02 Pengyu Zhang 1.0 None
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'''
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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'''批量固定尺寸'''
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import os
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import cv2
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from tqdm import tqdm
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import numpy as np
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def merge_overlapping_rectangles(rectangles):
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if len(rectangles) <= 1:
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return rectangles
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merged_rectangles = []
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for rect in sorted(rectangles, key=lambda x: x[2]*x[3], reverse=True):
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x, y, w, h = rect
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if not any((x < x2 + w2 and x + w > x2 and y < y2 + h2 and y + h > y2) for x2, y2, w2, h2 in merged_rectangles):
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merged_rectangles.append(rect)
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return merged_rectangles
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def roi_Inner_detector(input,):
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# 定义结构元素（内核）
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kernel = np.ones((3, 3), np.uint8)
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# 开运算
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opening = cv2.morphologyEx(input, cv2.MORPH_OPEN, kernel)
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# 定义结构元素（内核）
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kernel = np.ones((3, 3), np.uint8)
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# 闭运算
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closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
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# 使用 Canny 边缘检测
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edges = cv2.Canny(closing, 50, 255, apertureSize=3)
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# 寻找轮廓
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contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# 临时存储矩形
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temp_rectangles = []
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# 识别矩形轮廓
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for contour in contours:
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# 轮廓近似
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epsilon = 0.001 * cv2.arcLength(contour, True)
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approx = cv2.approxPolyDP(contour, epsilon, True)
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if len(approx) >= 4:
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x, y, w, h = cv2.boundingRect(approx)
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aspect_ratio = w / float(h)
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area_contour = cv2.contourArea(contour)
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min_area_threshold = 0
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# print('aspect_ratio',aspect_ratio)
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# print('area_contour',area_contour)
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if aspect_ratio >= 0.80 and aspect_ratio < 1.2 and 6500 < area_contour < 13000:
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area_bounding_rect = w * h
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area_ratio = area_contour / area_bounding_rect
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# print('aspect_ratio', area_ratio)
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# print('area_ratio', area_ratio)
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# print('area_contour', area_contour)
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if 0.80 < area_ratio < 1.05:
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temp_rectangles.append((x, y, w, h))
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# 合并重叠的方框
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merged_rectangles = merge_overlapping_rectangles(temp_rectangles)
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return merged_rectangles
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input_dir = '/project/dataset/QR2024/org100/orange-0-roi-fix'
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output_dir = '/project/dataset/QR2024/org100/orange-0-roi-fix2fix'
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size = 128
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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# 遍历输入目录中的图片
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for filename in tqdm(os.listdir(input_dir), desc='Processing'):
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if filename.endswith('.jpg') or filename.endswith('.png'):
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input_path = os.path.join(input_dir, filename)
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image = cv2.imread(input_path)
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height, width, _ = image.shape
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if height < size:
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image = cv2.resize(np.array(image), (size, size), interpolation=cv2.INTER_AREA)
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else:
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image = cv2.resize(np.array(image), (size, size), interpolation=cv2.INTER_LINEAR)
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image_gay = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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_, binary_image_A = cv2.threshold(image_gay, 50, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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if len(merged_rectangles) == 0 :
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_, binary_image_A = cv2.threshold(image_gay, 150, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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# print('150')
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if len(merged_rectangles) == 0 :
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_, binary_image_A = cv2.threshold(image_gay, 100, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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# print('100')
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if len(merged_rectangles) == 0 :
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_, binary_image_A = cv2.threshold(image_gay, 125, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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# print('120')
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if len(merged_rectangles) == 0 :
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_, binary_image_A = cv2.threshold(image_gay, 75, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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if len(merged_rectangles) == 0:
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_, binary_image_A = cv2.threshold(image_gay, 85, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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if len(merged_rectangles) == 0:
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_, binary_image_A = cv2.threshold(image_gay, 115, 255, cv2.THRESH_BINARY)
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merged_rectangles = roi_Inner_detector(binary_image_A)
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# print('75')
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# if len(merged_rectangles) == 0 :
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# _, binary_image_A = cv2.threshold(image_gay, 0, 255, cv2.THRESH_BINARY)
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# merged_rectangles = roi_Inner_detector(binary_image_A)
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if merged_rectangles:
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outermost_rectangle = max(merged_rectangles, key=lambda x: x[2] * x[3])
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x, y, w, h = outermost_rectangle
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image_crope = image[y + 1:y - 1 + h, x + 1:x - 1 + w]
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image_crope = cv2.resize(image_crope, (32, 32), interpolation=cv2.INTER_AREA)
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output_path = os.path.join(output_dir, filename)
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cv2.imwrite(output_path, image_crope)
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else:
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print("failed",filename)
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# output_path = os.path.join(output_dir, filename)
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# cv2.imwrite(output_path, image)
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# 保存裁剪后的图像
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print("裁剪完成！") |