95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
import json
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import logging
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import cv2
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"""
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根据裁剪之后的图片,每个点的坐标需要重新计算,以新的图片的宽高作为坐标系
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"""
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def re_cal_point(point,offset):
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# 相当于所有的x坐标向左平移了offset个距离
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point['x'] = point['x'] - offset
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"""
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过滤矩形
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1 高度过大的不要
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2 整个矩形全部身体都在裁剪区域之外的不要
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返回值:
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1 过滤之后的矩形
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2 裁剪之后的图片
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"""
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def filter_rectangle(image_path, points):
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# 高度过大矩形过滤参数
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max_height_rate = 0.5 # 矩形高度占整个画面高度的最大比例，如果超过该比例，则认为是无效矩形
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# 裁剪参数
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left_x_cut_rate=0.1 # 左边界的裁剪比例，从左边开始裁剪百分之多少
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right_x_cut_rate=0.1# 右边界的裁剪比例，从右边开始裁剪百分之多少
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image = cv2.imread(image_path)
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image_height = image.shape[0] # 获取图片高度
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image_width = image.shape[1] # 获取图片宽度
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image_x_min = int(image_width * left_x_cut_rate) # 左边界的裁剪点
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image_x_max = int(image_width * (1 - right_x_cut_rate)) # 右边界的裁剪点
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#开始过滤矩形
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bad_point_index = []
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print(f'开始过滤矩形,原有矩形数为{len(points)}')
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for index in range(len(points)):
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point = points[index]
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# 高度过大过滤
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if point['height'] > image_height * max_height_rate:
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bad_point_index.append(index)
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continue
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# x坐标范围过滤,整个矩形全部身体都在裁剪区域之外的不要
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x_min = point['x'] # 矩形四个矩形坐标中x的最小值
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x_max = point['x'] + point['width'] # 矩形四个矩形坐标中x的最大值
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# 如果矩形x的 最大值 小于 左边界，去除这个矩形
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if x_max < image_x_min:
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bad_point_index.append(index)
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continue
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# 如果矩形x的 最小值 大于 右边界，去除这个矩形
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if x_min > image_x_max:
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bad_point_index.append(index)
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continue
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# 过滤,只保留有效矩形
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filtered_points = []
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for i, point in enumerate(points):
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# 如果当前矩形的索引在bad_point_index中，则去除这个矩形
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if i not in bad_point_index:
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# 重新计算点的坐标
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re_cal_point(point,image_x_min)
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# 塞入结果
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filtered_points.append(point)
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print(f'过滤矩形结束,过滤之后的矩形数为{len(filtered_points)}')
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# 图片裁剪
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# 裁剪图片 (height方向不变，宽度方向裁剪)
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cropped_image = image[:, image_x_min:image_x_max]
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# 展示
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# cv2.imshow("cropped_image", cropped_image)
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# cv2.imshow("image", image)
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# for i in range(len(filtered_points)):
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# p = filtered_points[i]
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# cv2.rectangle(cropped_image, (p['x'], p['y']), (p['x'] + p['width'], p['y'] + p['height']), (0, 0, 255), 2)
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# # 写编号
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# cv2.putText(cropped_image, str(i), (p['x'], p['y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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# cv2.imshow("cropped_image_draw", cropped_image)
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# cv2.waitKey(0)
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return filtered_points, cropped_image
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# 测试代码
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# def read_from_json(file_path):
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# with open(file_path, 'r') as f:
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# loaded_array = json.load(f)
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# return loaded_array
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# cnts = read_from_json("data_sub/test_1/data_sub.json")
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# filter_rectangle("data_sub/test_1/wide_image.png",cnts) |