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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, progress=gr.Progress()):
"""
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():
raise ValueError("Could not open video file")
# 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))
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Create output video file
output_path = os.path.join(tempfile.gettempdir(), 'output_video.mp4')
# Initialize video writer with H264 codec
fourcc = cv2.VideoWriter_fourcc(*'avc1')
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
if not out.isOpened():
raise ValueError("Could not create output video file")
frame_count = 0
process_every_n_frames = 1 # Process every frame
progress(0, desc="Processing video...")
while True:
ret, frame = cap.read()
if not ret:
break
frame_count += 1
# Process 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)
# Update progress
progress((frame_count / total_frames), desc=f"Processing frame {frame_count}/{total_frames}")
# Release everything
cap.release()
out.release()
# Verify the output file exists and has size
if not os.path.exists(output_path) or os.path.getsize(output_path) == 0:
raise ValueError("Output video file is empty or was not created")
return output_path
except Exception as e:
print(f"Error processing video: {str(e)}")
raise gr.Error(f"Error processing video: {str(e)}")
def detect_objects_in_video(video):
"""
Gradio interface function for video object detection
"""
if video is None:
raise gr.Error("Please upload a video file")
try:
# Process the video
output_path = process_video(video)
return output_path
except Exception as e:
raise gr.Error(f"Error during video processing: {str(e)}")
# Create the Gradio interface
demo = gr.Interface(
fn=detect_objects_in_video,
inputs=[
gr.Video(label="Upload Video", format="mp4")
],
outputs=[
gr.Video(label="Processed Video", format="mp4")
],
title="@GenAILearniverse Project: 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.
""",
examples=[],
cache_examples=False
)
if __name__ == "__main__":
demo.launch()
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