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import torch |
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from PIL import Image, ImageDraw |
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from transformers import DetrImageProcessor, DetrForObjectDetection |
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from diffusers import StableDiffusionInpaintPipeline |
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import gradio as gr |
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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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pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16) |
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pipe = pipe.to("cpu") |
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def detect_objects(image): |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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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)[0] |
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detected_objects = [] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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if score > 0.9: |
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box = [round(i) for i in box.tolist()] |
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detected_objects.append({"label": model.config.id2label[label.item()], "box": box}) |
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return detected_objects |
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def display_detected_objects(image): |
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detected_objects = detect_objects(image) |
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labeled_image = image.copy() |
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draw = ImageDraw.Draw(labeled_image) |
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object_labels = [] |
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for obj in detected_objects: |
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box = obj["box"] |
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label = obj["label"] |
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draw.rectangle(box, outline="red", width=3) |
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draw.text((box[0], box[1]), label, fill="red") |
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object_labels.append(f"{label} at {box}") |
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return labeled_image, gr.update(choices=object_labels) |
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def inpaint_image(image, selected_objects): |
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detected_objects = detect_objects(image) |
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mask = Image.new("L", image.size, 0) |
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draw = ImageDraw.Draw(mask) |
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for obj in detected_objects: |
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object_label = f"{obj['label']} at {obj['box']}" |
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if object_label in selected_objects: |
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box = obj["box"] |
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draw.rectangle(box, fill=255) |
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image = image.convert("RGB") |
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mask = mask.convert("RGB") |
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output = pipe(prompt="a modern interior", image=image, mask_image=mask).images[0] |
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return output |
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with gr.Blocks() as interface: |
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with gr.Row(): |
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image_input = gr.Image(type="pil", label="Input Image") |
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objects_list = gr.CheckboxGroup(label="Detected Objects") |
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labeled_image_output = gr.Image(label="Labeled Image") |
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final_output = gr.Image(label="Output Image") |
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detect_button = gr.Button("Detect Objects") |
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inpaint_button = gr.Button("Remove Selected Objects") |
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detect_button.click(fn=display_detected_objects, inputs=image_input, outputs=[labeled_image_output, objects_list]) |
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inpaint_button.click(fn=inpaint_image, inputs=[image_input, objects_list], outputs=final_output) |
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interface.launch() |
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