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| import gradio as gr | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| import time | |
| # Load the YOLO model on the CPU | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) | |
| def move_robot(command): | |
| """ | |
| Simulate sending a command to the robot to move. | |
| Replace this with actual communication code to send commands to the robot. | |
| """ | |
| if command == "forward": | |
| print("Robot moving forward") | |
| elif command == "backward": | |
| print("Robot moving backward") | |
| elif command == "left": | |
| print("Robot turning left") | |
| elif command == "right": | |
| print("Robot turning right") | |
| elif command == "stop": | |
| print("Robot stopped") | |
| return f"Command sent: {command}" | |
| def process_camera_feed(): | |
| """Function to process single frame and run object detection""" | |
| try: | |
| # Initialize camera | |
| cap = cv2.VideoCapture(0) | |
| # Check if camera opened successfully | |
| if not cap.isOpened(): | |
| print("Error: Could not open camera") | |
| return np.zeros((480, 640, 3), dtype=np.uint8) | |
| # Read a frame | |
| ret, frame = cap.read() | |
| if ret: | |
| # Run YOLO detection | |
| results = model(frame) | |
| processed_frame = results.render()[0] | |
| # Convert BGR to RGB for display | |
| processed_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB) | |
| else: | |
| processed_frame = np.zeros((480, 640, 3), dtype=np.uint8) | |
| # Release the camera | |
| cap.release() | |
| return processed_frame | |
| except Exception as e: | |
| print(f"Error processing camera feed: {e}") | |
| return np.zeros((480, 640, 3), dtype=np.uint8) | |
| def continuously_update_feed(): | |
| """Function to continuously update the camera feed""" | |
| while True: | |
| yield process_camera_feed() | |
| time.sleep(0.1) # 100ms delay between updates | |
| def robot_interface(): | |
| with gr.Blocks() as interface: | |
| with gr.Row(): | |
| # Left column for controls | |
| with gr.Column(scale=1): | |
| # Control buttons | |
| forward_btn = gr.Button("Forward", variant="primary") | |
| backward_btn = gr.Button("Backward", variant="primary") | |
| left_btn = gr.Button("Left", variant="primary") | |
| right_btn = gr.Button("Right", variant="primary") | |
| stop_btn = gr.Button("Stop", variant="secondary") | |
| status_output = gr.Textbox(label="Robot Status") | |
| # Right column for camera feed | |
| with gr.Column(scale=2): | |
| # Camera feed output | |
| camera_output = gr.Image( | |
| label="Robot Camera Feed with Object Detection", | |
| type="numpy", | |
| height=480 | |
| ) | |
| # Update button for camera | |
| update_btn = gr.Button("Start Camera", variant="primary") | |
| # Set up button click events | |
| forward_btn.click(fn=move_robot, inputs=[gr.Textbox(value="forward", visible=False)], outputs=status_output) | |
| backward_btn.click(fn=move_robot, inputs=[gr.Textbox(value="backward", visible=False)], outputs=status_output) | |
| left_btn.click(fn=move_robot, inputs=[gr.Textbox(value="left", visible=False)], outputs=status_output) | |
| right_btn.click(fn=move_robot, inputs=[gr.Textbox(value="right", visible=False)], outputs=status_output) | |
| stop_btn.click(fn=move_robot, inputs=[gr.Textbox(value="stop", visible=False)], outputs=status_output) | |
| # Set up camera update | |
| update_btn.click( | |
| fn=process_camera_feed, | |
| inputs=None, | |
| outputs=camera_output | |
| ) | |
| return interface | |
| # Launch the Gradio interface | |
| if __name__ == "__main__": | |
| robot_interface().launch(share=True, debug=True) |