def test(lane_agent, test_images, thresh=p.threshold_point, color=None):
result = lane_agent.predict_lanes_test(test_images)
confidences, offsets, instances = result[-1]
num_batch = len(test_images)
out_x = []
out_y = []
out_images = []
for i in range(num_batch):
# test on test data set
# image = deepcopy(test_images[i])
image = test_images[i]
image = np.rollaxis(image, axis=2, start=0)
image = np.rollaxis(image, axis=2, start=0) * 255.0
image = image.astype(np.uint8).copy()
confidence = confidences[i].view(p.grid_y, p.grid_x).cpu().data.numpy()
offset = offsets[i].cpu().data.numpy()
offset = np.rollaxis(offset, axis=2, start=0)
offset = np.rollaxis(offset, axis=2, start=0)
instance = instances[i].cpu().data.numpy()
instance = np.rollaxis(instance, axis=2, start=0)
instance = np.rollaxis(instance, axis=2, start=0)
# generate point and cluster
raw_x, raw_y = generate_result(confidence, offset, instance, thresh)
# eliminate fewer points
in_x, in_y = eliminate_fewer_points(raw_x, raw_y)
# sort points along y
in_x, in_y = util.sort_along_y(in_x, in_y)
in_x, in_y = eliminate_out(in_x, in_y, confidence) #, deepcopy(image))
in_x, in_y = util.sort_along_y(in_x, in_y)
in_x, in_y = eliminate_fewer_points(in_x, in_y)
result_image = util.draw_points(in_x, in_y, image,
color) #deepcopy(image))
out_x.append(in_x)
out_y.append(in_y)
out_images.append(result_image)
return out_x, out_y, out_images
def test_ori(h_crop,
lane_agent,
ori_image,
test_images,
ratio_w,
ratio_h,
draw_type,
thresh=p.threshold_point): # p.threshold_point:0.81
result = lane_agent.predict_lanes_test(test_images)
confidences, offsets, instances = result[-1]
num_batch = len(test_images)
out_x = []
out_y = []
out_images = []
for i in range(num_batch):
# test on test data set
image = deepcopy(test_images[i])
image = np.rollaxis(image, axis=2, start=0)
image = np.rollaxis(image, axis=2, start=0) * 255.0
image = image.astype(np.uint8).copy()
confidence = confidences[i].view(p.grid_y, p.grid_x).cpu().data.numpy()
offset = offsets[i].cpu().data.numpy()
offset = np.rollaxis(offset, axis=2, start=0)
offset = np.rollaxis(offset, axis=2, start=0)
instance = instances[i].cpu().data.numpy()
instance = np.rollaxis(instance, axis=2, start=0)
instance = np.rollaxis(instance, axis=2, start=0)
raw_x, raw_y = generate_result(confidence, offset, instance, thresh) #
in_x, in_y = eliminate_fewer_points(raw_x, raw_y)
in_x, in_y = util.sort_along_y(in_x, in_y)
if draw_type == 'line':
result_image = util.draw_lines_ori(in_x, in_y, ori_image, ratio_w,
ratio_h) # 将最后且后处理后的坐标点绘制与原图上.
elif draw_type == 'point':
result_image = util.draw_point_ori(in_x, in_y, ori_image, ratio_w,
ratio_h,
h_crop) # 将最后且后处理后的坐标点绘制与原图上.
else:
result_image = util.draw_points(in_x, in_y, ori_image, ratio_w,
ratio_h) # 将最后且后处理后的坐标点绘制与原图上.
out_x.append(in_x)
out_y.append(in_y)
out_images.append(result_image)
return out_x, out_y, out_images
