def measure_tree_width(image_path, trunk_corners):
result = Result() # 保存当前图片的分割结果，初始化为失败
result.set_image_path(image_path)
result.set_time(0)
if not os.path.exists(image_path):
result.set_info(InfoEnum.IMAGE_NOT_EXIST)
return result
if trunk_corners is None:
result.set_info(InfoEnum.BAD_MANUAL_ANNO)
return result
result.set_trunk_left_top(trunk_corners['left_top'])
result.set_trunk_left_bottom(trunk_corners['left_bottom'])
result.set_trunk_right_top(trunk_corners['right_top'])
result.set_trunk_left_bottom(trunk_corners['right_bottom'])
im = cv2.imread(image_path) # 加载图片
im, resize_ratio = resize_image_with_ratio(im) # 调整尺寸
# 检测标定物
calibrator = get_calibrator(im, 0, DEBUG and SHOW)
if calibrator is None:
result.set_info(InfoEnum.CALIBRATOR_DET_FAILED)
return result
# 在结果中保存激光点坐标
# resize坐标 -> 原始坐标
org_calibrate_pts = []
for calibrate_pt in calibrator.get_calibrate_points():
org_calibrate_pt = recover_coordinate(calibrate_pt, resize_ratio)
org_calibrate_pts.append(org_calibrate_pt)
result.set_calibrate_points(org_calibrate_pts)
# 计算树径
trunk_left_top = resize_coordinate(trunk_corners['left_top'], resize_ratio)
trunk_left_bottom = resize_coordinate(trunk_corners['left_bottom'],
resize_ratio)
trunk_right_top = resize_coordinate(trunk_corners['right_top'],
resize_ratio)
trunk_right_bottom = resize_coordinate(trunk_corners['right_bottom'],
resize_ratio)
l_line = Edge(trunk_left_top, trunk_left_bottom)
r_line = Edge(trunk_right_top, trunk_right_bottom)
alpha = angle(l_line.vec(), r_line.vec())
if alpha < 5:
pixel_dis_l2r = parallel_distance(l_line, r_line)
pixel_dis_r2l = parallel_distance(r_line, l_line)
if pixel_dis_r2l is not None and pixel_dis_r2l is not None:
pixel_width = (pixel_dis_l2r + pixel_dis_r2l) / 2.0
RP_ratio = calibrator.RP_ratio()
real_width = (pixel_width * RP_ratio)
result.set_info(InfoEnum.SUCCESS)
result.set_width(real_width)
result.set_conf(1.0)
else:
result.set_info(InfoEnum.BAD_MANUAL_ANNO)
else:
result.set_info(InfoEnum.BAD_MANUAL_ANNO)
return result
def measure_all(image_path_list):
"""
为一批图像测算树径
:param image_path_list: 图像路径列表
:return: 树径测算结果列表
"""
if image_path_list is None or len(image_path_list) == 0:
# 输入不合法
return {'results': []}
seg_count = 0 # 计数分割操作的次数
results_all = [] # 保存所有结果
image_num = len(image_path_list)
for i, im_path in enumerate(image_path_list):
im_id = im_path.split('/')[-1].split('.')[0]
result = Result() # 当前图片测量结果
result.set_image_path(im_path)
results_all.append(result.get_result())
time_start = time.time()
if DEBUG:
print('-' * 30)
print('[%d/%d]: %s' % (image_num, i + 1, im_path.split('/')[-1]))
if not os.path.exists(im_path):
# 图像不存在
result.set_info(InfoEnum.IMAGE_NOT_EXIST)
continue
im_org = cv2.imread(im_path) # 加载图片
im, resize_ratio = resize_image_with_ratio(im_org) # 调整尺寸
# step1: 检测标定物
calibrator = get_calibrator(im, im_id, DEBUG and SHOW)
if calibrator is None:
result.set_info(InfoEnum.CALIBRATOR_DET_FAILED)
continue
# 检查站位
calibrator_ratio = calibrator.get_pixel_scale() / im.shape[0]
if calibrator_ratio > 0.25:
result.set_info(InfoEnum.STAND_TOO_CLOSE)
return result
# 在结果中保存标定物坐标
# resize坐标 -> 原始坐标
org_calibrate_pts = []
for calibrate_pt in calibrator.get_calibrate_points():
org_calibrate_pt = recover_coordinate(calibrate_pt, resize_ratio)
org_calibrate_pts.append(org_calibrate_pt)
result.set_calibrate_points(org_calibrate_pts)
# step2: 覆盖标定物，避免标定物影响分割
im_cover = calibrator.cover_calibrator(im)
if DEBUG:
visualize_image(im_cover,
'img_cover',
im_id=im_id,
show=DEBUG and SHOW)
# 参考标定物切割图片
# 图片块尺寸由小到大迭代尝试
crop_params = [2, 4]
for j, n_crop in enumerate(crop_params):
im_patch = calibrator.crop_image(im_cover, n_dis_w=n_crop)
patch_h, patch_w, _ = im_patch.shape
if DEBUG:
print('H:%d, W:%d' % (patch_h, patch_w))
visualize_image(im_patch,
'patch_%d' % n_crop,
im_id=im_id,
show=DEBUG and SHOW)
# step4: 分割树干
if im_patch.shape[0] > config.NET_MAX_WIDTH and im_patch.shape[
1] > config.NET_MAX_WIDTH:
# 待分割的目标尺寸太大
result.set_info(InfoEnum.TRUNK_TOO_THICK)
break
# 交互式分割
trunk_mask = segment_trunk_int(im_patch,
calibrator.positive_pts(),
None,
im_id=seg_count)
seg_count += 1
if DEBUG:
visualize_image(trunk_mask,
'trunk_mask',
im_id=im_id,
show=DEBUG and SHOW)
# step5: 计算树径
trunk = Trunk(trunk_mask)
if DEBUG:
visualize_image(trunk.contour_mask,
'trunk_contour',
im_id=im_id,
show=DEBUG and SHOW)
if not trunk.is_seg_succ():
# 初步估计分割是否成功
result.set_info(InfoEnum.TRUNK_EDGE_UNCLEAR)
if DEBUG:
print(InfoEnum.TRUNK_EDGE_UNCLEAR)
continue
else:
# 计算实际树径
RP_ratio = calibrator.RP_ratio() # 缩放因子
shot_distance = calibrator.shot_distance() # 拍摄距离
trunk_width, seg_conf, patch_trunk_corners = trunk.real_width_v2(
shot_distance, RP_ratio)
if trunk_width > 0:
# 置信度：分割置信度 x 标定置信度
conf = seg_conf * calibrator.get_conf()
time_end = time.time()
time_consume = int(time_end - time_start)
# 图片块坐标 -> resize图片坐标
trunk_left_top = calibrator.recover_coordinate(
patch_trunk_corners['left_top'])
trunk_right_top = calibrator.recover_coordinate(
patch_trunk_corners['right_top'])
trunk_left_bottom = calibrator.recover_coordinate(
patch_trunk_corners['left_bottom'])
trunk_right_bottom = calibrator.recover_coordinate(
patch_trunk_corners['right_bottom'])
# resize坐标 -> 原图坐标
trunk_left_top = recover_coordinate(
trunk_left_top, resize_ratio)
trunk_left_bottom = recover_coordinate(
trunk_left_bottom, resize_ratio)
trunk_right_top = recover_coordinate(
trunk_right_top, resize_ratio)
trunk_right_bottom = recover_coordinate(
trunk_right_bottom, resize_ratio)
result.set_width(trunk_width)
result.set_conf(conf)
result.set_trunk_left_top(trunk_left_top)
result.set_trunk_right_top(trunk_right_top)
result.set_trunk_left_bottom(trunk_left_bottom)
result.set_trunk_right_bottom(trunk_right_bottom)
result.set_info(InfoEnum.SUCCESS)
result.set_time(time_consume)
if DEBUG:
print('Trunk width: %.2f CM (%.2f).' %
(trunk_width / 10.0, conf))
pts = org_calibrate_pts + \
[trunk_left_top, trunk_left_bottom, trunk_right_top, trunk_right_bottom]
show_pts(im_org, pts, im_id=im_id, show=DEBUG and SHOW)
break
else:
result.set_info(InfoEnum.TRUNK_EDGE_UNCLEAR)
if DEBUG:
print('Error is too large.')
output = {'results': results_all}
return output