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import gradio as gr
import cv2
from transformers import YolosImageProcessor, YolosForObjectDetection
from PIL import Image
import torch
# Load model and processor
model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
def process_frame(frame):
# Resize the frame to reduce processing time
frame = cv2.resize(frame, (640, 360)) # downscaling the frame
# Convert the frame (numpy array) to PIL image
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# Prepare the image for the model
inputs = image_processor(images=image, return_tensors="pt")
# Perform object detection
with torch.no_grad():
outputs = model(**inputs)
# Post-process the outputs to extract bounding boxes and labels
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
# Draw the bounding boxes on the original frame
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
cv2.putText(frame, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}",
(int(box[0]), int(box[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return frame
def video_object_detection(video):
cap = cv2.VideoCapture(video)
processed_frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Optionally skip frames to speed up processing
# if int(cap.get(cv2.CAP_PROP_POS_FRAMES)) % 2 == 0: # Process every 2nd frame
processed_frame = process_frame(frame)
processed_frames.append(processed_frame)
cap.release()
# Convert processed frames to a video for display
height, width, _ = processed_frames[0].shape
output_video = cv2.VideoWriter('/tmp/output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 20, (width, height))
for frame in processed_frames:
output_video.write(frame)
output_video.release()
return '/tmp/output.mp4'
# Create Gradio interface with live=True
iface = gr.Interface(fn=video_object_detection, inputs="video", outputs="video", title="YOLOs-Tiny Video Detection", live=True)
iface.launch()
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