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import numpy as np |
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import cv2 |
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import gradio as gr |
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def detect_faces(image_file): |
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image_np = cv2.imread(image_file.name) |
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gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) |
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") |
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faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(10, 10)) |
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if len(faces) > 0: |
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print("Face detected!") |
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else: |
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print("No faces detected.") |
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for (x, y, w, h) in faces: |
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cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2) |
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return image_np |
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interface = gr.Interface( |
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fn=detect_faces, |
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inputs="file", |
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outputs="image", |
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title="Face Detection with Haar Cascade", |
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description="Upload an image file, and the model will detect faces and draw bounding boxes around them.", |
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) |
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interface.launch() |