Objects365 Dataset (#2932)

* add object365

* ADD CONVERSION SCRIPT

* fix transcript

* Reformat and simplify

* spelling

* Update get_objects365.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

data/scripts/get_objects365.py

@@ -0,0 +1,33 @@
+# Objects365 https://www.objects365.org labels JSON to YOLO script
+# 1. Download Object 365 from the Object 365 website And unpack all images in datasets/object365/images
+# 2. Place this file and zhiyuan_objv2_train.json file in datasets/objects365
+# 3. Execute this file from datasets/object365 path
+# /datasets
+#     /objects365
+#         /images
+#         /labels
+
+from pycocotools.coco import COCO
+
+coco = COCO("zhiyuan_objv2_train.json")
+cats = coco.loadCats(coco.getCatIds())
+nms = [cat["name"] for cat in cats]
+print("COCO categories: \n{}\n".format(" ".join(nms)))
+for categoryId, cat in enumerate(nms):
+    catIds = coco.getCatIds(catNms=[cat])
+    imgIds = coco.getImgIds(catIds=catIds)
+    print(cat)
+    # Create a subfolder in this directory called "labels". This is where the annotations will be saved in YOLO format
+    for im in coco.loadImgs(imgIds):
+        width, height = im["width"], im["height"]
+        path = im["file_name"].split("/")[-1]  # image filename
+        try:
+            with open("labels/train/" + path.replace(".jpg", ".txt"), "a+") as file:
+                annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                for a in coco.loadAnns(annIds):
+                    x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                    x, y = x + w / 2, y + h / 2  # xy to center
+                    file.write(f"{categoryId} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
+
+        except Exception as e:
+            print(e)