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import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image
import torch
import cv2
import numpy as np
# Initialize the model and processor
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
def process_frame(webcam_image):
# Convert the webcam image from Gradio to the format expected by the model
img = cv2.cvtColor(np.array(webcam_image), cv2.COLOR_RGB2BGR)
pil_image = Image.fromarray(img)
# Process the image
inputs = processor(images=pil_image, return_tensors="pt")
outputs = model(**inputs)
target_sizes = torch.tensor([pil_image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
# Draw bounding boxes and labels on the image
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [int(round(i, 0)) for i in box.tolist()]
cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), (0, 255, 255), 2)
label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}"
cv2.putText(img, label_text, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
# Convert back to RGB for Gradio display
processed_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return Image.fromarray(processed_image)
demo = gr.Interface(
process_frame,
gr.Image(type="pil"),
"image"
)
if __name__ == "__main__":
demo.launch()
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