update samples/test/eval_mAP_on_coco_val/pd2cocojson.py.

Signed-off-by: jario-jin <jariof@foxmail.com>

samples/test/eval_mAP_on_coco_val/pd2cocojson.py

@@ -17,42 +17,42 @@ coco_json = []
 # load images dir
 images = os.listdir(images_path)
 for image in images:
-  print(image)
-  # get image name
-  image_name, image_suffix = os.path.splitext(image)
-  # get image W and H
-  image_path = images_path + '/' + image
-  img = cv2.imread(image_path)
-  height, width, _ = img.shape
+    print(image)
+    # get image name
+    image_name, image_suffix = os.path.splitext(image)
+    # get image W and H
+    image_path = images_path + '/' + image
+    img = cv2.imread(image_path)
+    height, width, _ = img.shape
 
-  # read pred's txt
-  pred_path = preds_path + '/' + image_name + '.txt'
-  if not os.path.exists(pred_path):
-    continue
-  with open(pred_path, 'r') as f:
-    preds = f.readlines()
-    preds = [l.strip() for l in preds]
-    for j,pred in enumerate(preds):
-      pred = pred.split(' ')
-      category_id = int(pred[0])
-      x = float(pred[1]) * width
-      y = float(pred[2]) * height
-      w = float(pred[3]) * width
-      h = float(pred[4]) * height
-      xmin = x - w / 2
-      ymin = y - h / 2
-      xmax = x + w / 2
-      ymax = y + h / 2
+    # read pred's txt
+    pred_path = preds_path + '/' + image_name + '.txt'
+    if not os.path.exists(pred_path):
+        continue
+    with open(pred_path, 'r') as f:
+        preds = f.readlines()
+        preds = [l.strip() for l in preds]
+        for j, pred in enumerate(preds):
+            pred = pred.split(' ')
+            category_id = int(pred[0])
+            x = float(pred[1]) * width
+            y = float(pred[2]) * height
+            w = float(pred[3]) * width
+            h = float(pred[4]) * height
+            xmin = x - w / 2
+            ymin = y - h / 2
+            xmax = x + w / 2
+            ymax = y + h / 2
                 
-      coco_json.append({
-        'image_id': int(image_name),
-        'category_id': category_id + 1,
-        'bbox': [xmin, ymin, w, h],
-        'score': float(pred[5]),
-        'area': w * h})
+            coco_json.append({
+            'image_id': int(image_name),
+            'category_id': category_id + 1,
+            'bbox': [xmin, ymin, w, h],
+            'score': float(pred[5]),
+            'area': w * h})
 
 # save json
 with open(os.path.join(coco_json_save), 'w') as f:
-  json.dump(coco_json, f, indent=2)
+    json.dump(coco_json, f, indent=2)
 
 print(len(coco_json), 'Done!')