| import requests | |
| import os, io | |
| import gradio as gr | |
| # from PIL import Image | |
| # API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic" | |
| SECRET_TOKEN = os.getenv("SECRET_TOKEN") | |
| API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-dc5-panoptic" | |
| headers = {"Authorization": f'Bearer {SECRET_TOKEN}'} | |
| def image_classifier(inp): | |
| return {'cat': 0.3, 'dog': 0.7} | |
| def query(filename): | |
| with open(filename, "rb") as f: | |
| data = f.read() | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| def rb(img): | |
| # initialiaze io to_bytes converter | |
| img_byte_arr = io.BytesIO() | |
| # define quality of saved array | |
| img.save(img_byte_arr, format='JPEG', subsampling=0, quality=100) | |
| # converts image array to bytesarray | |
| img_byte_arr = img_byte_arr.getvalue() | |
| response = requests.post(API_URL, headers=headers, data=img_byte_arr) | |
| return response.json() | |
| inputs = gr.inputs.Image(type="pil", label="Upload an image") | |
| demo = gr.Interface(fn=rb, inputs=inputs, outputs="json") | |
| demo.launch() | |
| # 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) | |