61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
import json
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import logging
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import cv2
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def filter_points(image_path,points):
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# 高度过大点过滤参数
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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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# x范围过滤，只保留中间部分的点，只保留矩形完整出现在在画面中间的那部分矩形。
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left_x_cut_rate=0.2 # 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=0.2 # 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]
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image_width = image.shape[1]
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image_x_min = image_width * left_x_cut_rate
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image_x_max = image_width * (1 - right_x_cut_rate)
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#开始过滤点
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bad_point_index = []
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print(f'开始过滤点,原有点数为{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
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# 原point中的x和y,是矩形左上角的坐标
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x_min = point['x'] # 矩形四个点坐标中x的最小值
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x_max = point['x'] + point['width'] # 矩形四个点坐标中x的最大值
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# 如果矩形x的最小值小于左边界，去除这个矩形
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if x_min < 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的最大值大于右边界，去除这个矩形
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if x_max > image_x_max:
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bad_point_index.append(index)
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continue
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# 删除bad_point_index
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filtered_points = []
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for i, point in enumerate(points):
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if i not in bad_point_index:
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filtered_points.append(point)
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print(f'过滤点结束,过滤之后的点数为{len(filtered_points)}')
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return filtered_points
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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_points("data_sub/test_1/wide_image.png",cnts) |