import gradio as gr import cv2 import torch from PIL import Image from transformers import DetrImageProcessor, DetrForObjectDetection # Load the pre-trained DETR model processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm") model.eval() # Function for image object detection def image_object_detection(image_pil): # Process the image with the DETR model inputs = processor(images=image_pil, return_tensors="pt") outputs = model(**inputs) # Convert the image to numpy array for drawing bounding boxes image_np = cv2.cvtColor(cv2.cvtColor(cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR), cv2.COLOR_BGR2RGB), cv2.COLOR_RGB2BGR) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image_pil.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] # Draw bounding boxes on the image for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [int(round(i)) for i in box.tolist()] cv2.rectangle(image_np, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2) label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}" cv2.putText(image_np, label_text, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) return image_np # Function for live object detection from the camera def live_object_detection(image_pil): # Process the frame with the DETR model inputs = processor(images=image_pil, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image_pil.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] # Draw bounding boxes on the image for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [int(round(i)) for i in box.tolist()] cv2.rectangle(image_pil, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2) label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}" cv2.putText(image_pil, label_text, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) return image_pil # Define the Gradio interface iface = gr.Interface( fn=[image_object_detection, live_object_detection], inputs=[ gr.Image(type="pil", label="Upload an image for object detection", hover=True), "webcam", ], outputs=["image", "image"], live=True, ) # Launch the Gradio interface iface.launch()