Update testdata.py

testdata.py

@@ -15,7 +15,7 @@
 """PP4AV dataset."""
 
 import os
-import glob as glob
+from glob import glob
 from tqdm import tqdm
 from pathlib import Path
 from typing import List
@@ -73,16 +73,12 @@ class TestData(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "image": datasets.Image(),
-                    "faces": datasets.Sequence(
+                    #"data": datasets.Sequence(datasets.Value("float32"), length=4),
+                    "data": datasets.Sequence(
                         {
-                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                            "faces": datasets.Sequence(datasets.Value("float32"), length=4),
                         }
                     ),
-                    "plates": datasets.Sequence(
-                        {
-                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                        }
-                    ), 
                 }
             ),
             supervised_keys=None,
@@ -109,45 +105,44 @@ class TestData(datasets.GeneratorBasedBuilder):
         annotation_dir = os.path.join(annot_dir, "annotations", "fisheye")
         files = []
         
-        
-        for file_type in IMG_EXT:
-            files.extend(list(Path(image_dir).glob(f'**/*.{file_type}')))
-            
         idx = 0
-        for image_path in tqdm(files):
-            img_relative_path = image_path.relative_to(image_dir)
-            gt_pah = (Path(annotation_dir) / img_relative_path).with_suffix('.txt')
+        #datasets.logging.info(image_dir)
+        for file in glob(os.path.join(image_dir, "*.png")):
+            objects = []
+            objects.append(
+                {
+                    "faces": [1, 2, 3, 4]
+                }
+            )
+            yield idx, {"image": file, "data": objects}
+            idx += 1
+        # for file_type in IMG_EXT:
+        #     files.extend(list(Path(image_dir).glob(f'**/*.{file_type}')))
             
-
-            annotation = defaultdict(list)
-            with open(gt_pah, 'r') as f:
-                line = f.readline().strip()
-                while line:
-                    assert re.match(r'^\d( [\d\.]+){4,5}$', line), 'Incorrect line: %s' % line
-                    cls, cx, cy, w, h = line.split()[:5]
-                    cls, cx, cy, w, h = int(cls), float(cx), float(cy), float(w), float(h)
-                    x1, y1, x2, y2 = cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
-                    annotation[cls].append([x1, y1, x2, y2])
-                    line = f.readline().strip()
-
-            faces = []
-            plates = []
+        # idx = 0
+        # for image_path in tqdm(files):
+            # img_relative_path = image_path.relative_to(image_dir)
+            #gt_pah = (Path(annotation_dir) / img_relative_path).with_suffix('.txt')
             
-            for cls, bboxes in annotation.items():
-                for x1, y1, x2, y2 in bboxes:
-                    if cls == 0:
-                        faces.append(
-                            {
-                                "bbox": [x1, y1, x2, y2]
-                            }
-                        )
-                    else:
-                        plates.append(
-                            {
-                                "bbox": [x1, y1, x2, y2]
-                            }
-                        )
+            #annotation = parse_annotation(gt_pah)
+
+            # annotation = defaultdict(list)
+            # with open(gt_pah, 'r') as f:
+            #     line = f.readline().strip()
+            #     while line:
+            #         assert re.match(r'^\d( [\d\.]+){4,5}$', line), 'Incorrect line: %s' % line
+            #         cls, cx, cy, w, h = line.split()[:5]
+            #         cls, cx, cy, w, h = int(cls), float(cx), float(cy), float(w), float(h)
+            #         x1, y1, x2, y2 = cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
+            #         annotation[cls].append([x1, y1, x2, y2])
+            #         line = f.readline().strip()
+            # datasets.logging.INFO(annotation)
+            # abcd =acd
+            # for cls, bboxes in annotation.items():
+            #     for x1, y1, x2, y2 in bboxes:
+            #         if cls == 0:
+            #             faces.append({"bbox": [x1, y1, x2, y2]})
+            #         else:
+            #             plates({"bbox": [x1, y1, x2, y2]})
             
-            yield idx, {"image": str(image_path), "faces": faces, "plates": plates}
-            idx += 1