Create app.py

app.py

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+import io
+import requests
+from PIL import Image
+import torch
+import numpy
+
+from transformers import DetrFeatureExtractor, DetrForSegmentation
+from transformers.models.detr.feature_extraction_detr import rgb_to_id
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
+model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
+
+# prepare image for the model
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+# forward pass
+outputs = model(**inputs)
+
+# use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
+processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
+result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
+
+# the segmentation is stored in a special-format png
+panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
+panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
+# retrieve the ids corresponding to each mask
+panoptic_seg_id = rgb_to_id(panoptic_seg)