Spaces:
Sleeping
Sleeping
File size: 5,011 Bytes
0a1fb69 e7f0a52 149db20 e7f0a52 4809422 e7f0a52 149db20 55f4e33 4809422 b44bdba 4809422 e7f0a52 4809422 95981ec 4809422 95981ec 4809422 b44bdba 4809422 149db20 4809422 149db20 e7f0a52 4809422 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
import gradio as gr
import numpy as np
import time
import threading
# Global flag to control detection
detection_active = False
def process_video_frame(frame):
"""
Placeholder function for processing each video frame
Replace this with your actual face spoofing detection logic
"""
if frame is None:
return "No frame received", "", ""
# Simulate processing time (remove this in production)
time.sleep(0.1)
# Placeholder result - replace with your actual detection
is_real = np.random.choice([True, False], p=[0.7, 0.3])
confidence = np.random.uniform(0.8, 1.0)
result = "Real Face" if is_real else "Spoof Detected"
status = "Processing live feed..."
conf_text = f"{confidence:.2%}"
return status, result, conf_text
def start_detection():
"""
Start the detection process
"""
global detection_active
detection_active = True
return "Processing live feed..."
def stop_detection():
"""
Stop the detection process
"""
global detection_active
detection_active = False
return "Detection stopped"
def process_frames(video_feed, status_text, result_text, confidence_text):
"""
Continuously process frames when detection is active
"""
while detection_active:
if video_feed is not None:
status, result, conf = process_video_frame(video_feed)
status_text.update(value=status)
result_text.update(value=result)
confidence_text.update(value=conf)
time.sleep(0.1) # Adjust the sleep time as needed
with gr.Blocks() as demo:
gr.Markdown("# Real-Time Face Spoofing Detection")
with gr.Row():
with gr.Column(scale=2):
# Main video feed
video_feed = gr.Image(label="Live Camera Feed", streaming=True)
with gr.Column(scale=1):
# Status and results
status_text = gr.Textbox(label="Status", value="Waiting for camera...")
result_text = gr.Textbox(label="Detection Result")
confidence_text = gr.Textbox(label="Confidence Score")
# Control buttons
start_button = gr.Button("Start Detection", variant="primary")
stop_button = gr.Button("Stop", variant="secondary")
gr.Markdown("""
### Instructions:
1. Allow camera access when prompted
2. Click 'Start Detection' to begin real-time analysis
3. Click 'Stop' to pause the detection
### Note:
- Keep your face centered and well-lit
- Maintain a stable position for better results
- Detection results update in real-time
""")
# Event handlers
start_button.click(
fn=start_detection,
outputs=status_text
)
stop_button.click(
fn=stop_detection,
outputs=status_text
)
# Start a thread to process frames when detection is active
threading.Thread(
target=process_frames,
args=(video_feed, status_text, result_text, confidence_text),
daemon=True
).start()
if __name__ == "__main__":
demo.launch()
# import gradio as gr
# import numpy as np
# import time
# def process_image(img):
# """Placeholder function - replace with your backend integration"""
# if img is None:
# return "No image provided", "", ""
# time.sleep(1) # Simulate processing
# return "Processing Complete", "Real Face", "Confidence: 95%"
# with gr.Blocks() as demo:
# gr.Markdown("# Face Spoofing Detection System")
# with gr.Tabs():
# with gr.Tab("Webcam Detection"):
# webcam = gr.Image(label="Webcam Feed")
# webcam_status = gr.Textbox(label="Status", value="Ready")
# webcam_result = gr.Textbox(label="Detection Result")
# webcam_conf = gr.Textbox(label="Confidence Score")
# webcam_button = gr.Button("Analyze")
# webcam_button.click(
# fn=process_image,
# inputs=webcam,
# outputs=[webcam_status, webcam_result, webcam_conf]
# )
# with gr.Tab("Image Upload"):
# image_input = gr.Image(label="Upload Image")
# image_status = gr.Textbox(label="Status", value="Ready")
# image_result = gr.Textbox(label="Detection Result")
# image_conf = gr.Textbox(label="Confidence Score")
# image_button = gr.Button("Analyze")
# image_button.click(
# fn=process_image,
# inputs=image_input,
# outputs=[image_status, image_result, image_conf]
# )
# gr.Markdown("""
# ### Instructions:
# 1. Choose either Webcam or Image Upload tab
# 2. For webcam: Allow camera access and take a photo
# 3. For images: Upload an image from your device
# 4. Click Analyze to process the image
# """)
# if __name__ == "__main__":
# demo.launch() |