def cal_evaluation_index(path_images, path_labels, proposals, image_info):
precision = 0.
recall = 0.
fscore = 0.
images_name = image_info['image_name']
width = image_info['width']
height = image_info['height']
for index, name in enumerate(images_name):
img = cv2.imread(os.path.join(path_images, name))
im_scale = [height / img.shape[0], width / img.shape[1]]
filename_label = name.split('.')[0] + '.txt'
with open(os.path.join(path_labels, filename_label),
'r',
encoding='utf-8') as f:
lines = f.readlines()
gt_boxes = np.zeros((len(lines), 4), dtype=np.float32)
for x, line in enumerate(lines):
l = line.split('\t')
gt_boxes[x][0] = float(l[0]) * im_scale[1]
gt_boxes[x][1] = float(l[1]) * im_scale[0]
gt_boxes[x][2] = float(l[2]) * im_scale[1]
gt_boxes[x][3] = float(l[3]) * im_scale[0]
inds = np.where(proposals[:, 0] == index)[0]
proposal = proposals[inds, :]
if (Config.USE_C):
overlaps = Utils.c_cal_overlaps(proposal[:, 2:], gt_boxes)
else:
overlaps = Utils.cal_overlaps(proposals[:, 2:], gt_boxes)
# 求每个proposal的最大overlap
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(overlaps.shape[0]),
argmax_overlaps]
max_overlaps = max_overlaps[max_overlaps > 0.7]
precision = precision + max_overlaps.shape[0] / overlaps.shape[0]
recall = recall + max_overlaps.shape[0] / gt_boxes.shape[0]
precision = precision / len(images_name)
recall = recall / len(images_name)
fscore = 2 * precision * recall / (precision + recall + 0.0001)
return precision, recall, fscore
def generate_labels_bboxs(self, gt_boxes, feature_map_h, feature_map_w,
stride):
part_anchors, part_index, num_anchors = self._generate_all_anchors(
feature_map_h, feature_map_w, stride)
if (Config.USE_C):
part_overlaps = Utils.c_cal_overlaps(part_anchors, gt_boxes)
else:
part_overlaps = Utils.cal_overlaps(part_anchors, gt_boxes)
part_labels = self._cal_all_labels(part_overlaps)
part_bbox_target = self._cal_all_box_targets(part_overlaps,
part_anchors, gt_boxes)
part_bbox_inside_weights = np.zeros((len(part_index), 4),
dtype=np.float32)
part_bbox_inside_weights[part_labels == 1, :] = np.ones((1, 4))
part_bbox_outside_weights = np.zeros((len(part_index), 4),
dtype=np.float32)
part_bbox_outside_weights[part_labels == 1, :] = np.ones((1, 4))
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = Utils.unmap(part_labels, num_anchors, part_index, fill=-1)
bbox_targets = Utils.unmap(part_bbox_target,
num_anchors,
part_index,
fill=0) # 这些anchor的真值是0，也p即没有值
bbox_inside_weights = Utils.unmap(part_bbox_inside_weights,
num_anchors,
part_index,
fill=0) # 内部权重以0填充
bbox_outside_weights = Utils.unmap(part_bbox_outside_weights,
num_anchors,
part_index,
fill=0) # 外部权重以0填充
labels = labels.reshape(-1, 1)
bbox_targets = bbox_targets.reshape(-1, 4)
bbox_inside_weights = bbox_inside_weights.reshape(-1, 4)
bbox_outside_weights = bbox_outside_weights.reshape(-1, 4)
return labels, bbox_targets, bbox_inside_weights, bbox_outside_weights
