208 lines
7.0 KiB
Python
208 lines
7.0 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@File : roi_img_process.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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2024/1/7 22:57 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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import cv2
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import numpy as np
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from matplotlib import pyplot as plt
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def preprocess_image(image,size,blur):
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# 将图像调整为固定大小
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# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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if size !=-1:
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image = cv2.resize(image, (size, size), interpolation=cv2.INTER_AREA)
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if size == -1:
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image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
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if blur:
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kernel_size = 3
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image = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigmaX=1, sigmaY=0)
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return image
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def clos_open(input,B_p):
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_, binary_image = cv2.threshold(input, B_p, 255, cv2.THRESH_BINARY)
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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(binary_image, 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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return closing
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def img_angle(input):
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# canny边缘检测
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eroded_canny = cv2.Canny(input, 50, 255, apertureSize=3)
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contours, hierarchy = cv2.findContours(eroded_canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 寻找最大元素
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k = 0
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index = 0
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if len(contours) != 0:
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for i in range(len(contours)):
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j = contours[i].size
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if j > k:
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k = j
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index = i
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# 拟合旋转矩形
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cnt = contours[index]
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rect = cv2.minAreaRect(cnt)
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angle = rect[2]
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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# 对点按照y坐标排序（最上和最下的点）
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box = box[box[:, 1].argsort()]
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# 上边的两个点按照x坐标排序（决定左上和右上）
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top_two = box[:2]
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top_two = top_two[top_two[:, 0].argsort()]
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# 下边的两个点按照x坐标排序（决定左下和右下）
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bottom_two = box[2:]
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bottom_two = bottom_two[bottom_two[:, 0].argsort()]
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# 重新组合为左上、右上、右下、左下的顺序
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ordered_box = np.array([top_two[0], top_two[1], bottom_two[1], bottom_two[0]])
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return angle,ordered_box
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def angel_affine(input,angle):
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height, width = np.array(input).shape
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center = (width // 2, height // 2)
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if angle is not None and angle != 0.0:
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if angle > 45.0:
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angle = angle - 90
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# rotate page if not straight relative to QR code
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M = cv2.getRotationMatrix2D(center, angle, 1.0)
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output = cv2.warpAffine(np.array(input), M, (width, height), flags=cv2.INTER_CUBIC,borderMode=cv2.BORDER_REPLICATE)
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return output
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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 roi2_detector(input,min_area_threshold):
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# 使用 Canny 边缘检测
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edges = cv2.Canny(input, 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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if aspect_ratio >= 0.75 and aspect_ratio < 1.2 and min_area_threshold < area_contour < 15000:
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area_bounding_rect = w * h
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area_ratio = area_contour / area_bounding_rect
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if 0.75 < 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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# 选择并绘制最大的方框
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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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rectangle_count = 1
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return outermost_rectangle
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else:
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return None
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from PIL import Image
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def roi_img_processing(input):
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input_ray = cv2.cvtColor(np.array(input), cv2.COLOR_BGR2GRAY)
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img_resize = preprocess_image(input_ray, 128, blur=False)
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img_closing = clos_open(img_resize,B_p=50) # 闭运算
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min_area_threshold=6500
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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if roi_coordinate is None:
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img_closing = clos_open(img_resize, B_p=100) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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if roi_coordinate is None:
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return None
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x, y, w, h = roi_coordinate
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# 截取图像
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input_fix = img_resize[y+1:y-1 + h, x+1:x-1 + w]
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output = preprocess_image(input_fix, 32, blur=True)
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return output
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def roi_detect(input):
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size = 128
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height, width, _ = input.shape
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if height < size:
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img_resize = cv2.resize(np.array(input), (size, size), interpolation=cv2.INTER_AREA)
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else:
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img_resize = cv2.resize(np.array(input), (size, size), interpolation=cv2.INTER_LINEAR)
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image_gay = cv2.cvtColor(img_resize, cv2.COLOR_BGR2GRAY)
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min_area_threshold = 1000
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img_closing = clos_open(image_gay, B_p=50) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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if roi_coordinate is None :
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img_closing = clos_open(img_resize, B_p=100) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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# print('150')
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if roi_coordinate is None :
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img_closing = clos_open(img_resize, B_p=150) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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# print('100')
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if roi_coordinate is None :
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img_closing = clos_open(img_resize, B_p=120) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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if roi_coordinate is None:
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img_closing = clos_open(img_resize, B_p=75) # 闭运算
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roi_coordinate = roi2_detector(img_closing,min_area_threshold)
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if roi_coordinate is None:
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return None
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x, y, w, h = roi_coordinate
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# 截取图像
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input_fix = img_resize[y + 1:y - 1 + h, x + 1:x - 1 + w]
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input_fix = Image.fromarray(np.uint8(input_fix))
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return input_fix
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def roi_minAreaRect(input):
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img_closing = clos_open(input) # 闭运算
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_, box = img_angle(img_closing) # 图像偏差角度
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return box
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