111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
import numpy as np
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import time, cv2
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def ms(t1, t0):
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return (t1 - t0) * 1000.0
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def center_coordinate(boundbxs):
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'''
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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 mixCthc_postprocess(preds, _mask_cv,pars=None):
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'''考虑船上人过滤'''
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'''输入：危化品的结果（类别+坐标）、原图、mask图像
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过程：获得mask的轮廓，判断危化品是否在轮廓内。
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在，则保留且绘制；不在，舍弃。
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返回：最终绘制的结果图、最终危化品（坐标、类别、置信度），
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'''
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'''1、最大分隔路面作为判断依据'''
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# zoom_factor=4 #缩小因子设置为4，考虑到numpy中分别遍历xy进行缩放耗时大。
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# speedroad = _mask_cv.copy()
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# speedroad = _mask_cv[speedroad==1]
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# _mask_cv[0] = _mask_cv[1]
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original_height = _mask_cv.shape[0]
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original_width = _mask_cv.shape[1]
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zoom_factor = original_width / 480.0
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zoom_height = int(original_height / zoom_factor)
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zoom_width = int(original_width / zoom_factor)
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_mask_cv = cv2.resize(_mask_cv, (zoom_width, zoom_height)) # 缩小原图,宽在前，高在后
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t4 = time.time()
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print('+' * 10, '_mask_cv shape信息', _mask_cv.shape)
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img_gray = cv2.cvtColor(_mask_cv, cv2.COLOR_BGR2GRAY) if len(_mask_cv.shape) == 3 else _mask_cv #
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t5 = time.time()
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contours, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# 寻找轮廓(多边界)
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contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2)
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contour_info = []
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for c in contours:
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contour_info.append((
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c,
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cv2.isContourConvex(c),
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cv2.contourArea(c),
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))
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contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
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t6 = time.time()
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# print('+'*10,'路面分隔信息',len(contour_info))
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'''新增模块：：如果路面为空，则返回原图、无危化品等。'''
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if contour_info == []:
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# final_img=_img_cv
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final_Cthc_filterroad = []
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timeInfos = 0
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return final_Cthc_filterroad, timeInfos
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else:
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# print(contour_info[0])
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max_contour = contour_info[0]
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max_contour = max_contour[0] * zoom_factor # contours恢复原图尺寸
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max_contour = max_contour.astype(np.int32)
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# print(max_contour)
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t7 = time.time()
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'''2.1、preds中Cthc取出，car取出。'''
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init_Cthc = []
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# init_car = []
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for i in range(len(preds)):
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if preds[i][5] == 0:
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init_Cthc.append(preds[i])
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# elif preds[i][5] == 3:
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# init_car.append(preds[i])
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# person
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# points = max_contour.reshape((-1, 1, 2))
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# cv2.polylines(image, [points], isClosed=True, color=(0, 255, 0), thickness=2)
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'''3、preds中Cthc，通过1中路面过滤'''
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init_Cthc_filterroad = init_Cthc
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final_Cthc_filterroad = []
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for i in range(len(init_Cthc_filterroad)):
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center_x, center_y = center_coordinate(init_Cthc_filterroad[i])
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# print('#'*20,'line176:',len(max_contour),np.array(max_contour).shape,(center_x, center_y))
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# 返回 1、-1 或 0，分别对应点在多边形内部、外部或边界上的情况
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flag = cv2.pointPolygonTest(max_contour, (int(center_x), int(center_y)),
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False) # 若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。
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if flag == 1:
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final_Cthc_filterroad.append(init_Cthc_filterroad[i])
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else:
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pass
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t9 = time.time()
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timeInfos = ' findMaxroad:%.1f releJudge:%.1f' % (ms(t6, t4), ms(t9, t6))
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return final_Cthc_filterroad, timeInfos # 返回最终绘制的结果图、危化品（坐标、类别、置信度）
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