def eval_func(input_json_path, gt_json_path, iou_threshold = 0.5):
# load json file as dict
with open(input_json_path, 'r') as f:
input_dict = json.load(f)
with open(gt_json_path, 'r') as f:
gt_dict = json.load(f)
# Initial config
global_tp = 0
global_fp = 0
global_fn = 0
for input_img_key, input_cnts in input_dict.items():
detections = input_reading(input_cnts)
groundtruths = gt_reading(gt_dict, input_img_key) # .replace('res', 'gt'))
detections = detection_filtering(detections, groundtruths) # filters detections overlapping with DC area
groundtruths = gt_filtering(groundtruths)
iou_table = np.zeros((len(groundtruths), len(detections)))
det_flag = np.zeros((len(detections), 1))
gt_flag = np.zeros((len(groundtruths), 1))
tp = 0
fp = 0
fn = 0
for gt_id, gt in enumerate(groundtruths):
gt_x = list(map(int, np.squeeze(gt['points'][:, 0])))
gt_y = list(map(int, np.squeeze(gt['points'][:, 1])))
if len(detections) > 0:
for det_id, detection in enumerate(detections):
# print(detection['points'])
det_x = list(map(int, np.squeeze(detection['points'][:, 0])))
det_y = list(map(int, np.squeeze(detection['points'][:, 1])))
iou_table[gt_id, det_id] = iou(det_x, det_y, gt_x, gt_y)
best_matched_det_id = np.argmax(
iou_table[gt_id, :]) # identified the best matched detection candidates with current groundtruth
matched_id = np.where(iou_table[gt_id, :] >= iou_threshold)
if iou_table[gt_id, best_matched_det_id] >= iou_threshold:
if matched_id[0].shape[0] < 2:
tp = tp + 1.0
global_tp = global_tp + 1.0
det_flag[best_matched_det_id] = 1
gt_flag[gt_id] = 1
else:
tp = tp + 1.0
global_tp = global_tp + 1.0
det_flag[best_matched_det_id] = 1
gt_flag[gt_id] = 1
# if there are more than 1 matched detection, only 1 is contributed to tp, the rest are fp
fp = fp + (matched_id[0].shape[0] - 1.0)
# Update local and global tp, fp, and fn
inv_gt_flag = 1 - gt_flag
fn = np.sum(inv_gt_flag)
inv_det_flag = 1 - det_flag
fp = fp + np.sum(inv_det_flag)
global_fp = global_fp + fp
global_fn = global_fn + fn
if tp + fp == 0:
local_precision = 0
else:
local_precision = tp / (tp + fp)
if tp + fn == 0:
local_recall = 0
else:
local_recall = tp / (tp + fn)
print('{0:12} Precision: {1:.4f}, Recall: {2:.4f}'.format(input_img_key + '.jpg', # .replace('res', 'gt')
local_precision, local_recall))
global_precision = global_tp / (global_tp + global_fp)
global_recall = global_tp / (global_tp + global_fn)
f_score = 2 * global_precision * global_recall / (global_precision + global_recall)
print('Global Precision: {:.4f}, Recall: {:.4f}, F_score: {:.4f}'.format(global_precision, global_recall, f_score))
print('over')
iou_table = np.zeros((len(groundtruths), len(detections)))
det_flag = np.zeros((len(detections), 1))
gt_flag = np.zeros((len(groundtruths), 1))
tp = 0
fp = 0
fn = 0
for gt_id, gt in enumerate(groundtruths):
gt_x = list(map(int, np.squeeze(gt['points'][:, 0])))
gt_y = list(map(int, np.squeeze(gt['points'][:, 1])))
if len(detections) > 0:
for det_id, detection in enumerate(detections):
# print(detection['points'])
det_x = list(map(int, np.squeeze(detection['points'][:, 0])))
det_y = list(map(int, np.squeeze(detection['points'][:, 1])))
iou_table[gt_id, det_id] = iou(det_x, det_y, gt_x, gt_y)
best_matched_det_id = np.argmax(
iou_table[gt_id, :]) # identified the best matched detection candidates with current groundtruth
matched_id = np.where(iou_table[gt_id, :] >= iou_threshold)
if iou_table[gt_id, best_matched_det_id] >= iou_threshold:
if matched_id[0].shape[0] < 2:
tp = tp + 1.0
global_tp = global_tp + 1.0
det_flag[best_matched_det_id] = 1
gt_flag[gt_id] = 1
else:
tp = tp + 1.0
global_tp = global_tp + 1.0
det_flag[best_matched_det_id] = 1