update samples from Release-114 as a part of 1.38.0 SDK stable release

how-to-use-azureml/automated-machine-learning/image-object-detection/coco2jsonl.py

@@ -0,0 +1,127 @@
+import json
+import os
+import sys
+import argparse
+
+# Define Converters
+
+
+class CocoToJSONLinesConverter:
+    def convert(self):
+        raise NotImplementedError
+
+
+class BoundingBoxConverter(CocoToJSONLinesConverter):
+    def __init__(self, coco_data):
+        self.json_lines_data = []
+        self.categories = {}
+        self.coco_data = coco_data
+        self.image_id_to_data_index = {}
+        for i in range(0, len(coco_data["images"])):
+            self.json_lines_data.append({})
+            self.json_lines_data[i]["image_url"] = ""
+            self.json_lines_data[i]["image_details"] = {}
+            self.json_lines_data[i]["label"] = []
+        for i in range(0, len(coco_data["categories"])):
+            self.categories[coco_data["categories"][i]["id"]] = coco_data["categories"][
+                i
+            ]["name"]
+
+    def _populate_image_url(self, index, coco_image):
+        self.json_lines_data[index]["image_url"] = coco_image["file_name"]
+        self.image_id_to_data_index[coco_image["id"]] = index
+
+    def _populate_image_details(self, index, coco_image):
+        file_name = coco_image["file_name"]
+        self.json_lines_data[index]["image_details"]["format"] = file_name[
+            file_name.rfind(".") + 1 :
+        ]
+        self.json_lines_data[index]["image_details"]["width"] = coco_image["width"]
+        self.json_lines_data[index]["image_details"]["height"] = coco_image["height"]
+
+    def _populate_bbox_in_label(self, label, annotation, image_details):
+        # if bbox comes as normalized, skip normalization.
+        if max(annotation["bbox"]) < 1.5:
+            width = 1
+            height = 1
+        else:
+            width = image_details["width"]
+            height = image_details["height"]
+        label["topX"] = annotation["bbox"][0] / width
+        label["topY"] = annotation["bbox"][1] / height
+        label["bottomX"] = (annotation["bbox"][0] + annotation["bbox"][2]) / width
+        label["bottomY"] = (annotation["bbox"][1] + annotation["bbox"][3]) / height
+
+    def _populate_label(self, annotation):
+        index = self.image_id_to_data_index[annotation["image_id"]]
+        image_details = self.json_lines_data[index]["image_details"]
+        label = {"label": self.categories[annotation["category_id"]]}
+        self._populate_bbox_in_label(label, annotation, image_details)
+        self._populate_isCrowd(label, annotation)
+        self.json_lines_data[index]["label"].append(label)
+
+    def _populate_isCrowd(self, label, annotation):
+        if "iscrowd" in annotation.keys():
+            label["isCrowd"] = annotation["iscrowd"]
+
+    def convert(self):
+        for i in range(0, len(self.coco_data["images"])):
+            self._populate_image_url(i, self.coco_data["images"][i])
+            self._populate_image_details(i, self.coco_data["images"][i])
+        for i in range(0, len(self.coco_data["annotations"])):
+            self._populate_label(self.coco_data["annotations"][i])
+        return self.json_lines_data
+
+
+if __name__ == "__main__":
+    # Parse arguments that are passed into the script
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_coco_file_path", type=str, required=True)
+    parser.add_argument("--output_dir", type=str, required=True)
+    parser.add_argument("--output_file_name", type=str, required=True)
+    parser.add_argument(
+        "--task_type",
+        type=str,
+        required=True,
+        choices=["ObjectDetection"],
+        default="ObjectDetection",
+    )
+    parser.add_argument("--base_url", type=str, default=None)
+
+    args = parser.parse_args()
+
+    input_coco_file_path = args.input_coco_file_path
+    output_dir = args.output_dir
+    output_file_path = output_dir + "/" + args.output_file_name
+    task_type = args.task_type
+    base_url = args.base_url
+
+    def read_coco_file(coco_file):
+        with open(coco_file) as f_in:
+            return json.load(f_in)
+
+    def write_json_lines(converter, filename, base_url=None):
+        json_lines_data = converter.convert()
+        with open(filename, "w") as outfile:
+            for json_line in json_lines_data:
+                if base_url is not None:
+                    image_url = json_line["image_url"]
+                    json_line["image_url"] = (
+                        base_url + image_url[image_url.rfind("/") + 1 :]
+                    )
+                json.dump(json_line, outfile, separators=(",", ":"))
+                outfile.write("\n")
+            print(f"Conversion completed. Converted {len(json_lines_data)} lines.")
+
+    coco_data = read_coco_file(input_coco_file_path)
+
+    print("Converting for {}".format(task_type))
+
+    # Defined in azureml.contrib.dataset.labeled_dataset.LabeledDatasetTask.OBJECT_DETECTION.value
+    if task_type == "ObjectDetection":
+        converter = BoundingBoxConverter(coco_data)
+        write_json_lines(converter, output_file_path, base_url)
+
+    else:
+        print("ERROR: Invalid Task Type")
+        pass