Replace YOLO format support in CVAT with Datumaro (#1151)

* Employ transforms and item wrapper

* Add image class and tests

* Add image info support to formats

* Fix cli

* Fix merge and voc converte

* Update remote images extractor

* Codacy

* Remove item name, require path in Image

* Merge images of dataset items

* Update tests

* Add image dir converter

* Update Datumaro format

* Update COCO format with image info

* Update CVAT format with image info

* Update TFrecord format with image info

* Update VOC formar with image info

* Update YOLO format with image info

* Update dataset manager bindings with image info

* Add image name to id transform

* Replace YOLO export and import in CVAT with Datumaro

cvat/apps/annotation/yolo.py

@@ -24,98 +24,46 @@
 
 def load(file_object, annotations):
     from pyunpack import Archive
-    import os
+    import os.path as osp
     from tempfile import TemporaryDirectory
     from glob import glob
-
-    def convert_from_yolo(img_size, box):
-        # convertation formulas are based on https://github.com/pjreddie/darknet/blob/master/scripts/voc_label.py
-        # <x> <y> <width> <height> - float values relative to width and height of image
-        # <x> <y> - are center of rectangle
-        def clamp(value, _min, _max):
-            return max(min(_max, value), _min)
-        xtl = clamp(img_size[0] * (box[0] - box[2] / 2), 0, img_size[0])
-        ytl = clamp(img_size[1] * (box[1] - box[3] / 2), 0, img_size[1])
-        xbr = clamp(img_size[0] * (box[0] + box[2] / 2), 0, img_size[0])
-        ybr = clamp(img_size[1] * (box[1] + box[3] / 2), 0, img_size[1])
-
-        return [xtl, ytl, xbr, ybr]
-
-    def parse_yolo_obj(img_size, obj):
-        label_id, x, y, w, h = obj.split(" ")
-        return int(label_id), convert_from_yolo(img_size, (float(x), float(y), float(w), float(h)))
-
-    def parse_yolo_file(annotation_file, labels_mapping):
-        frame_number = annotations.match_frame(annotation_file)
-        with open(annotation_file, "r") as fp:
-            line = fp.readline()
-            while line:
-                frame_info = annotations.frame_info[frame_number]
-                label_id, points = parse_yolo_obj((frame_info["width"], frame_info["height"]), line)
-                annotations.add_shape(annotations.LabeledShape(
-                    type="rectangle",
-                    frame=frame_number,
-                    label=labels_mapping[label_id],
-                    points=points,
-                    occluded=False,
-                    attributes=[],
-                ))
-                line = fp.readline()
-
-    def load_labels(labels_file):
-        with open(labels_file, "r") as f:
-            return {idx: label.strip() for idx, label in enumerate(f.readlines()) if label.strip()}
+    from datumaro.plugins.yolo_format.importer import YoloImporter
+    from cvat.apps.dataset_manager.bindings import import_dm_annotations
 
     archive_file = file_object if isinstance(file_object, str) else getattr(file_object, "name")
     with TemporaryDirectory() as tmp_dir:
         Archive(archive_file).extractall(tmp_dir)
 
-        labels_file = glob(os.path.join(tmp_dir, "*.names"))
-        if not labels_file:
-            raise Exception("Could not find '*.names' file with labels in uploaded archive")
-        elif len(labels_file) == 1:
-            labels_mapping = load_labels(labels_file[0])
-        else:
-            raise Exception("Too many '*.names' files in uploaded archive: {}".format(labels_file))
-
-        for dirpath, _, filenames in os.walk(tmp_dir):
-            for file in filenames:
-                if ".txt" == os.path.splitext(file)[1]:
-                    parse_yolo_file(os.path.join(dirpath, file), labels_mapping)
+        image_info = {}
+        anno_files = glob(osp.join(tmp_dir, '**', '*.txt'), recursive=True)
+        for filename in anno_files:
+            filename = osp.basename(filename)
+            frame_info = None
+            try:
+                frame_info = annotations.frame_info[
+                    int(osp.splitext(filename)[0])]
+            except Exception:
+                pass
+            try:
+                frame_info = annotations.match_frame(filename)
+                frame_info = annotations.frame_info[frame_info]
+            except Exception:
+                pass
+            if frame_info is not None:
+                image_info[osp.splitext(filename)[0]] = \
+                    (frame_info['height'], frame_info['width'])
+
+        dm_project = YoloImporter()(tmp_dir, image_info=image_info)
+        dm_dataset = dm_project.make_dataset()
+        import_dm_annotations(dm_dataset, annotations)
 
 def dump(file_object, annotations):
-    from zipfile import ZipFile
-    import os
-
-    # convertation formulas are based on https://github.com/pjreddie/darknet/blob/master/scripts/voc_label.py
-    # <x> <y> <width> <height> - float values relative to width and height of image
-    # <x> <y> - are center of rectangle
-    def convert_to_yolo(img_size, box):
-        x = (box[0] + box[2]) / 2 / img_size[0]
-        y = (box[1] + box[3]) / 2 / img_size[1]
-        w = (box[2] - box[0]) / img_size[0]
-        h = (box[3] - box[1]) / img_size[1]
-
-        return x, y, w, h
-
-    labels_ids = {label[1]["name"]: idx for idx, label in enumerate(annotations.meta["task"]["labels"])}
-
-    with ZipFile(file_object, "w") as output_zip:
-        for frame_annotation in annotations.group_by_frame():
-            image_name = frame_annotation.name
-            annotation_name = "{}.txt".format(os.path.splitext(os.path.basename(image_name))[0])
-            width = frame_annotation.width
-            height = frame_annotation.height
-
-            yolo_annotation = ""
-            for shape in frame_annotation.labeled_shapes:
-                if shape.type != "rectangle":
-                    continue
-
-                label = shape.label
-                yolo_bb = convert_to_yolo((width, height), shape.points)
-                yolo_bb = " ".join("{:.6f}".format(p) for p in yolo_bb)
-                yolo_annotation += "{} {}\n".format(labels_ids[label], yolo_bb)
-
-            output_zip.writestr(annotation_name, yolo_annotation)
-        output_zip.writestr("obj.names", "\n".join(l[0] for l in sorted(labels_ids.items(), key=lambda x:x[1])))
+    from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
+    from cvat.apps.dataset_manager.util import make_zip_archive
+    from datumaro.components.project import Environment
+    from tempfile import TemporaryDirectory
+    extractor = CvatAnnotationsExtractor('', annotations)
+    converter = Environment().make_converter('yolo')
+    with TemporaryDirectory() as temp_dir:
+        converter(extractor, save_dir=temp_dir)
+        make_zip_archive(temp_dir, file_object)