Merge pull request #227 from fastlabel/feature/export-yolo

segementationのプロジェクトのyolo形式でのexport

README.md

@@ -2971,6 +2971,7 @@ Support the following annotation types.
 
 - bbox
 - polygon
+- segmentation
 
 Get tasks and export as YOLO format files.
 
@@ -3097,7 +3098,7 @@ for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"),
 
 ### YOLO To FastLabel
 
-Supported bbox annotation type.
+Supported bbox and segmentation annotation type.
 
 Convert annotation file of YOLO format as a Fastlabel format and create task.
 

examples/export_yolo.py

@@ -0,0 +1,8 @@
+import fastlabel
+
+client = fastlabel.Client()
+
+project_slug = "YOUR_PROJECT_SLUG"
+tasks = client.get_image_tasks(project=project_slug)
+
+client.export_yolo(project=project_slug, tasks=tasks, output_dir="./export_yolo/")

examples/import_yolo.py

@@ -0,0 +1,29 @@
+import glob
+import os
+import time
+
+import fastlabel
+
+client = fastlabel.Client()
+
+project = "YOUR_PROJECT_SLUG"
+
+input_file_path = "./classes.txt"
+input_dataset_path = "./dataset/"
+annotations_map = client.convert_yolo_to_fastlabel(
+    classes_file_path=input_file_path,
+    dataset_folder_path=input_dataset_path,
+    project_type="segmentation",
+)
+for image_file_path in glob.iglob(
+    os.path.join(input_dataset_path, "**/**.jpg"), recursive=True
+):
+    time.sleep(1)
+    name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
+    file_path = image_file_path
+    annotations = (
+        annotations_map.get(name) if annotations_map.get(name) is not None else []
+    )
+    task_id = client.create_image_task(
+        project=project, name=name, file_path=file_path, annotations=annotations
+    )

fastlabel/__init__.py

@@ -2968,7 +2968,10 @@ def convert_pascalvoc_to_fastlabel(self, folder_path: str) -> dict:
         return results
 
     def convert_yolo_to_fastlabel(
-        self, classes_file_path: str, dataset_folder_path: str
+        self,
+        classes_file_path: str,
+        dataset_folder_path: str,
+        project_type: str,
     ) -> dict:
         """
         Convert YOLO format to FastLabel format as annotation files.
@@ -3026,12 +3029,20 @@ def convert_yolo_to_fastlabel(
         image_sizes = self.__get_yolo_image_sizes(dataset_folder_path)
         yolo_annotations = self.__get_yolo_format_annotations(dataset_folder_path)
 
-        return converters.execute_yolo_to_fastlabel(
-            classes,
-            image_sizes,
-            yolo_annotations,
-            os.path.join(*[dataset_folder_path, ""]),
-        )
+        if project_type == "segmentation":
+            return converters.execute_segmentation_yolo_to_fastlabel(
+                classes,
+                image_sizes,
+                yolo_annotations,
+                os.path.join(*[dataset_folder_path, ""]),
+            )
+        else:
+            return converters.execute_bbox_yolo_to_fastlabel(
+                classes,
+                image_sizes,
+                yolo_annotations,
+                os.path.join(*[dataset_folder_path, ""]),
+            )
 
     def __get_yolo_format_classes(self, classes_file_path: str) -> dict:
         """

fastlabel/converters.py

@@ -478,7 +478,7 @@ def __serialize(value: any) -> any:
 def to_yolo(project_type: str, tasks: list, classes: list, output_dir: str) -> tuple:
     if len(classes) == 0:
         coco = to_coco(project_type=project_type, tasks=tasks, output_dir=output_dir)
-        return __coco2yolo(coco)
+        return __coco2yolo(project_type, coco)
     else:
         return __to_yolo(
             project_type=project_type,
@@ -488,7 +488,7 @@ def to_yolo(project_type: str, tasks: list, classes: list, output_dir: str) -> t
         )
 
 
-def __coco2yolo(coco: dict) -> tuple:
+def __coco2yolo(project_type: str, coco: dict) -> tuple:
     categories = coco["categories"]
 
     annos = []
@@ -498,39 +498,82 @@ def __coco2yolo(coco: dict) -> tuple:
 
         # Get objects
         objs = []
-        for annotation in coco["annotations"]:
-            if image["id"] != annotation["image_id"]:
-                continue
+        if project_type == "image_segmentation":
+            objs = __coco2yolo_segmentation(coco, categories, image, dw, dh)
+        else:
+            objs = __coco2yolo_rect(coco, categories, image, dw, dh)
 
-            category_index = "0"
-            for index, category in enumerate(categories):
-                if category["id"] == annotation["category_id"]:
-                    category_index = str(index)
-                    break
+        # get annotation
+        anno = {"filename": image["file_name"], "object": objs}
+        annos.append(anno)
 
-            xmin = annotation["bbox"][0]
-            ymin = annotation["bbox"][1]
-            xmax = annotation["bbox"][0] + annotation["bbox"][2]
-            ymax = annotation["bbox"][1] + annotation["bbox"][3]
+    return annos, categories
 
-            x = (xmin + xmax) / 2
-            y = (ymin + ymax) / 2
-            w = xmax - xmin
-            h = ymax - ymin
 
-            x = str(_truncate(x * dw, 7))
-            y = str(_truncate(y * dh, 7))
-            w = str(_truncate(w * dw, 7))
-            h = str(_truncate(h * dh, 7))
+def __coco2yolo_rect(
+    coco: dict, categories: list, image: dict, dw: float, dh: float
+) -> list[str]:
+    objs = []
+    for annotation in coco["annotations"]:
+        if image["id"] != annotation["image_id"]:
+            continue
 
-            obj = [category_index, x, y, w, h]
-            objs.append(" ".join(obj))
+        category_index = "0"
+        for index, category in enumerate(categories):
+            if category["id"] == annotation["category_id"]:
+                category_index = str(index)
+                break
+        xmin = annotation["bbox"][0]
+        ymin = annotation["bbox"][1]
+        xmax = annotation["bbox"][0] + annotation["bbox"][2]
+        ymax = annotation["bbox"][1] + annotation["bbox"][3]
+
+        x = (xmin + xmax) / 2
+        y = (ymin + ymax) / 2
+        w = xmax - xmin
+        h = ymax - ymin
+
+        x = str(_truncate(x * dw, 7))
+        y = str(_truncate(y * dh, 7))
+        w = str(_truncate(w * dw, 7))
+        h = str(_truncate(h * dh, 7))
+
+        obj = [category_index, x, y, w, h]
+        objs.append(" ".join(obj))
+        return obj
+
+
+def __coco2yolo_segmentation(
+    coco: dict, categories: list, image: dict, dw: float, dh: float
+) -> list[str]:
+    objs = []
+    for annotation in coco["annotations"]:
+        if image["id"] != annotation["image_id"]:
+            continue
 
-        # get annotation
-        anno = {"filename": image["file_name"], "object": objs}
-        annos.append(anno)
+        category_index = "0"
+        for index, category in enumerate(categories):
+            if category["id"] == annotation["category_id"]:
+                category_index = str(index)
+                break
+        # 座標部分を取得
+        for coordinates in annotation["segmentation"]:
+            # 座標を(x, y)のペアに分割し、yoloの小数で表す形式に変換する。
+            yolo_vertices = [
+                {
+                    "x": str(_truncate(coordinates[i] * dw, 7)),
+                    "y": str(_truncate(coordinates[i + 1] * dh, 7)),
+                }
+                for i in range(0, len(coordinates), 2)
+            ]
 
-    return annos, categories
+            # category_index の後に x, yを順番に足していく。
+            obj = [category_index]
+            for v in yolo_vertices:
+                obj.append(v["x"])
+                obj.append(v["y"])
+            objs.append(" ".join(obj))
+    return objs
 
 
 def __to_yolo(project_type: str, tasks: list, classes: list, output_dir: str) -> tuple:
@@ -594,6 +637,7 @@ def __get_yolo_annotation(data: dict) -> dict:
     if (
         annotation_type != AnnotationType.bbox.value
         and annotation_type != AnnotationType.polygon.value
+        and annotation_type != AnnotationType.segmentation.value
     ):
         return None
     if not points or len(points) == 0:
@@ -607,8 +651,36 @@ def __get_yolo_annotation(data: dict) -> dict:
 
     dw = 1.0 / data["width"]
     dh = 1.0 / data["height"]
+    if annotation_type == AnnotationType.segmentation.value:
+        return __segmentation2yolo(value, classes, dw, dh, points)
+    else:
+        bbox = __to_bbox(annotation_type, points)
+        return __bbox2yolo(value, classes, dw, dh, bbox)
+
+
+def __segmentation2yolo(value: str, classes: list, dw: float, dh: float, points: list):
+    objs = []
+    category_index = str(classes.index(value))
+    for shapes in points:
+        for coordinates in shapes:
+            # 座標を(x, y)のペアに分割し、yoloの小数で表す形式に変換する。
+            yolo_vertices = [
+                {
+                    "x": str(_truncate(coordinates[i] * dw, 7)),
+                    "y": str(_truncate(coordinates[i + 1] * dh, 7)),
+                }
+                for i in range(0, len(coordinates), 2)
+            ]
+            # category_index の後に x, yを順番に足していく。
+            obj = [category_index]
+            for v in yolo_vertices:
+                obj.append(v["x"])
+                obj.append(v["y"])
+            objs.append(" ".join(obj))
+    return objs
 
-    bbox = __to_bbox(annotation_type, points)
+
+def __bbox2yolo(value: str, classes: list, dw: float, dh: float, bbox: list):
     xmin = bbox[0]
     ymin = bbox[1]
     xmax = bbox[0] + bbox[2]
@@ -1089,7 +1161,7 @@ def execute_pascalvoc_to_fastlabel(pascalvoc: dict, file_path: str = None) -> tu
     return (file_name, annotations)
 
 
-def execute_yolo_to_fastlabel(
+def execute_bbox_yolo_to_fastlabel(
     classes: dict,
     image_sizes: dict,
     yolo_annotations: dict,
@@ -1140,6 +1212,52 @@ def execute_yolo_to_fastlabel(
     return results
 
 
+def execute_segmentation_yolo_to_fastlabel(
+    classes: dict,
+    image_sizes: dict,
+    yolo_annotations: dict,
+    dataset_folder_path: str = None,
+) -> dict:
+    results = {}
+    for yolo_anno_key in yolo_annotations:
+        annotations = []
+        for each_image_annotation in yolo_annotations[yolo_anno_key]:
+            yolo_class_id = each_image_annotation[0]
+            coordinates = each_image_annotation[1:]
+            image_width, image_height = image_sizes[yolo_anno_key]["size"]
+
+            classs_name = classes[str(yolo_class_id)]
+
+            points = [[[]]]
+            # 座標を(x, y)のペアに分割
+            vertices = [
+                {"x": coordinates[i], "y": coordinates[i + 1]}
+                for i in range(0, len(coordinates), 2)
+            ]
+            for vertice in vertices:
+                points[0][0].append(round(float(image_width) * float(vertice["x"])))
+                points[0][0].append(round(float(image_height) * float(vertice["y"])))
+
+            annotations.append(
+                {
+                    "value": classs_name,
+                    "points": points,
+                    "type": AnnotationType.segmentation.value,
+                }
+            )
+
+        file_path = (
+            image_sizes[yolo_anno_key]["image_file_path"].replace(
+                os.path.join(*[dataset_folder_path, ""]), ""
+            )
+            if dataset_folder_path
+            else image_sizes[yolo_anno_key]["image_file_path"]
+        )
+        results[file_path] = annotations
+
+    return results
+
+
 def __get_annotation_type_by_labelme(shape_type: str) -> str:
     if shape_type == "rectangle":
         return "bbox"