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import gradio as gr
from PIL import Image, ImageDraw, ImageFont
from transformers import pipeline
import cv2
import numpy as np
import tempfile
import os
# Initialize the object detection pipeline
object_detector = pipeline("object-detection",
model="facebook/detr-resnet-50")
def draw_bounding_boxes(frame, detections):
"""
Draws bounding boxes on the video frame based on the detections.
"""
# Convert numpy array to PIL Image
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(frame_rgb)
draw = ImageDraw.Draw(pil_image)
# Use default font
font = ImageFont.load_default()
for detection in detections:
box = detection['box']
xmin = int(box['xmin'])
ymin = int(box['ymin'])
xmax = int(box['xmax'])
ymax = int(box['ymax'])
# Draw the bounding box
draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
# Create label with score
label = detection['label']
score = detection['score']
text = f"{label} {score:.2f}"
# Draw text with background rectangle for visibility
text_bbox = draw.textbbox((xmin, ymin), text, font=font)
draw.rectangle([
(text_bbox[0], text_bbox[1]),
(text_bbox[2], text_bbox[3])
], fill="red")
draw.text((xmin, ymin), text, fill="white", font=font)
# Convert back to numpy array
frame_with_boxes = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
return frame_with_boxes
def process_video(video_path):
"""
Process the video file and return the path to the processed video
"""
try:
# Open the video file
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
return None
# Get video properties
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Create temporary file for output video
temp_output = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
output_path = temp_output.name
temp_output.close()
# Initialize video writer
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
frame_count = 0
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Process every nth frame to speed up processing
process_every_n_frames = 2 # Adjust this value to process more or fewer frames
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 1
# Only process every nth frame
if frame_count % process_every_n_frames == 0:
# Convert frame to RGB for the model
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Detect objects
detections = object_detector(frame_rgb)
# Draw bounding boxes
frame = draw_bounding_boxes(frame, detections)
# Write the frame
out.write(frame)
# Print progress
progress = (frame_count / total_frames) * 100
print(f"Processing: {progress:.1f}% complete", end='\r')
# Release everything
cap.release()
out.release()
return output_path
except Exception as e:
print(f"Error processing video: {str(e)}")
return None
def detect_objects_in_video(video):
"""
Gradio interface function for video object detection
"""
if video is None:
return None
try:
# Process the video
output_path = process_video(video)
if output_path is None:
return None
return output_path
except Exception as e:
print(f"Error during video processing: {str(e)}")
return None
# Create the Gradio interface
demo = gr.Interface(
fn=detect_objects_in_video,
inputs=[
gr.Video(label="Upload Video")
],
outputs=[
gr.Video(label="Processed Video")
],
title="Video Object Detection",
description="""
Upload a video to detect and track objects within it.
The application will process the video and draw bounding boxes around detected objects
with their labels and confidence scores.
Note: Processing may take some time depending on the video length.
"""
)
if __name__ == "__main__":
demo.launch() |