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app.py |
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paulmondon |
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Add requirements.txt |
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1.6 kB |
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from transformers import DetrImageProcessor, DetrForObjectDetection |
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import torch |
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from PIL import Image, ImageDraw |
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import gradio as gr |
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import requests |
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import random |
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def detect_objects(image): |
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# Load the pre-trained DETR model |
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processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") |
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model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# convert outputs (bounding boxes and class logits) to COCO API |
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# let's only keep detections with score > 0.9 |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] |
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draw = ImageDraw.Draw(image) |
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for i, (score, label, box) in enumerate(zip(results["scores"], results["labels"], results["boxes"])): |
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box = [round(i, 2) for i in box.tolist()] |
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color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) |
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draw.rectangle(box, outline=color, width=3) |
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label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}" |
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draw.text((box[0], box[1]), label_text, fill=color) |
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return image |
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def upload_image(file): |
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image = Image.open(file.name) |
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image_with_boxes = detect_objects(image) |
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return image_with_boxes |
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iface = gr.Interface( |
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fn=upload_image, |
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inputs="file", |
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outputs="image", |
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title="Object Detection", |
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description="Upload an image and detect objects using DETR model.", |
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allow_flagging=False |
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) |
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iface.launch() |
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