GPT

Load the Pre-Trained YOLOv5 Model

import cv2 import torch from PIL import Image Load the pre-trained YOLOv5 model model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) Set the model to evaluation mode model.eval() Open the input

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import cv2
import torch
from PIL import Image

# Load the pre-trained YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Set the model to evaluation mode
model.eval()

# Open the input video file
input_video = cv2.VideoCapture('input_video.mp4')

# Get the video dimensions and frames per second (FPS)
width = int(input_video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(input_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(input_video.get(cv2.CAP_PROP_FPS))

# Create the output video file
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
output_video = cv2.VideoWriter('output_video.mp4', fourcc, fps, (width, height))

# Process the video frame by frame
while input_video.isOpened():
 ret, frame = input_video.read()
 if not ret:
 break

 # Convert the frame to PIL image format and perform object detection
 img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
 results = model(img)

 # Filter the results to keep only the "person" class detections
 person_detections = results.xyxy[0][results.xyxy[0][:, 5] == 0]

 # Draw bounding boxes around detected persons
 for x1, y1, x2, y2, conf, _ in person_detections:
 x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
 cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)

 # Write the processed frame to the output video
 output_video.write(frame)

# Release the input and output video files
input_video.release()
output_video.release()

# Close all OpenCV windows
cv2.destroyAllWindows()

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Source-listed license: MIT. Prompt text is preserved; titles and previews may be shortened for display. Source examples are not NexGateHub test results.

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