108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
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import cv2
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import numpy as np
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import torch
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# from loguru import logger
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def pannel_post_process(preds, pars):
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# pars={'solar':0}
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'''
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将光伏板上覆盖物、裂缝识别出来
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'''
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# print(preds[0])
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# logger.info('\n分类结果返回：%s'%preds)
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preds = torch.tensor(preds[0])
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preds = preds.tolist()
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preds = [[*sublist[:-1], int(sublist[-1])] for sublist in preds] # 类别从浮点型转为整型
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# print(preds)
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# 设置空的列表
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# 1、判断类别中哪些有太阳能板？取出太阳能板检测结果，并取出覆盖物、裂缝检测结果。
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preds_solar = []
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preds_others = []
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for i in range(len(preds)):
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if preds[i][5] in pars['objs']: # 识别为光伏板
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preds_solar.append(preds[i])
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else: # 识别为裂缝、覆盖物
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preds_others.append(preds[i])
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return point_in_rectangle(preds_others, preds_solar)
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def center_coordinate(boundbxs):
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'''
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根据检测矩形框，得到其矩形长度和宽度
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输入：两个对角坐标xyxy
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输出：矩形框重点坐标xy
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'''
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boundbxs_x1 = boundbxs[0]
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boundbxs_y1 = boundbxs[1]
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boundbxs_x2 = boundbxs[2]
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boundbxs_y2 = boundbxs[3]
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center_x = 0.5 * (boundbxs_x1 + boundbxs_x2)
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center_y = 0.5 * (boundbxs_y1 + boundbxs_y2)
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return center_x, center_y
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def fourcorner_coordinate(boundbxs):
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'''
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通过矩形框对角xyxy坐标，得到矩形框轮廓
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输入：两个对角坐标xyxy
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输出：矩形框四个角点坐标，以contours顺序。
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'''
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boundbxs_x1 = boundbxs[0]
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boundbxs_y1 = boundbxs[1]
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boundbxs_x2 = boundbxs[2]
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boundbxs_y2 = boundbxs[3]
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wid = boundbxs_x2 - boundbxs_x1
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hei = boundbxs_y2 - boundbxs_y1
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boundbxs_x3 = boundbxs_x1 + wid
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boundbxs_y3 = boundbxs_y1
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boundbxs_x4 = boundbxs_x1
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boundbxs_y4 = boundbxs_y1 + hei
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contours_rec = [[boundbxs_x1, boundbxs_y1], [boundbxs_x3, boundbxs_y3], [boundbxs_x2, boundbxs_y2],
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[boundbxs_x4, boundbxs_y4]]
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return contours_rec
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def point_in_rectangle(preds_others, preds_solar):
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'''
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遍历所有光伏板异常目标，并输出
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'''
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if not preds_solar:
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return [[],'']
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preds = []
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for i in range(len(preds_others)):
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for solar in preds_solar:
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solar_contour = fourcorner_coordinate(solar)
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solar_contour = np.array(solar_contour, dtype=np.float32)
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center_x, center_y = center_coordinate(preds_others[i])
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# print(cv2.pointPolygonTest(solar_contour, (center_x, center_y), False))
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if cv2.pointPolygonTest(solar_contour, (center_x, center_y), False) == 1:
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preds.append(preds_others[i])
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# logger.info('\n分类结果返回：%s' % preds)
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return [preds,'']
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if __name__ == "__main__":
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# 对应DJI_20230306140129_0001_Z_165.jpg检测结果
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# preds=[[6.49000e+02, 2.91000e+02, 1.07900e+03, 7.33000e+02, 9.08165e-01, 3.00000e+00],
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# [8.11000e+02, 2.99000e+02, 1.31200e+03, 7.65000e+02, 8.61268e-01, 3.00000e+00],
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# [7.05000e+02, 1.96000e+02, 7.19000e+02, 2.62000e+02, 5.66877e-01, 0.00000e+00]]
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# 对应DJI_20230306152702_0001_Z_562.jpg检测结果
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preds = [[7.62000e+02, 7.14000e+02, 1.82800e+03, 9.51000e+02, 9.00902e-01, 3.00000e+00],
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[2.00000e+01, 3.45000e+02, 1.51300e+03, 6.71000e+02, 8.81440e-01, 3.00000e+00],
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[8.35000e+02, 8.16000e+02, 8.53000e+02, 8.30000e+02, 7.07651e-01, 0.00000e+00],
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[1.35600e+03, 4.56000e+02, 1.42800e+03, 4.94000e+02, 6.70549e-01, 2.00000e+00]]
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print('before :\n ', preds)
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# preds=torch.tensor(preds) #返回的预测结果
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imgwidth = 1920
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imgheight = 1680
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pars = {'imgSize': (imgwidth, imgheight), 'wRation': 1 / 6.0, 'hRation': 1 / 6.0, 'smallId': 0, 'bigId': 3,
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'newId': 4, 'recScale': 1.2}
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# 'smallId':0(国旗)，'bigId':3(船只),wRation和hRation表示判断的阈值条件，newId--新目标的id
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# yyy = channel2_post_process([preds], pars) # 送入后处理函数
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#
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# print('after :\n ', yyy)
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