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import cv2 |
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import imutils |
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import gradio as gr |
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import numpy as np |
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face_detector = cv2.CascadeClassifier("haar_cascades/haarcascade_frontalface_default.xml") |
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def detect_faces(img, size, neighbours, scale): |
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frame = np.array(img) |
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frame = frame[:, :, ::-1].copy() |
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frame = imutils.resize(frame, width=500) |
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) |
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faceRects = face_detector.detectMultiScale( |
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gray, scaleFactor=scale, minNeighbors=neighbours, minSize=(size, size), |
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flags=cv2.CASCADE_SCALE_IMAGE) |
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box_data = [] |
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class_labels = { |
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0: "face" |
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} |
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for (x,y,w,h) in faceRects: |
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frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) |
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midX = int(x+w/2) |
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midY = int(y+h/2) |
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box = { |
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"position": { |
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"middle": [midX, midY], |
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"width": float(w), |
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"height": float(h) |
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}, |
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"domain" : "pixel", |
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"class_id" : 0 |
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} |
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box_data.append(box) |
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predictions = {"predictions": { |
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"box_data": box_data, |
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"class_labels": class_labels |
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} |
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} |
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re_im =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
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return re_im |
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image = gr.components.Image() |
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out_im = gr.components.Image() |
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size_slider = gr.components.Slider(minimum=5, maximum=50, value=30, step=5, label="MinSize in Pixel") |
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neighbour_slider = gr.components.Slider(minimum=1, maximum=20, value=5, step=1, label="Min Number of Neighbours") |
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scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor") |
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description = """Face Detection with Haar Cascades using OpenCV""" |
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Iface = gr.Interface( |
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fn=detect_faces, |
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inputs=[image, size_slider, neighbour_slider, scale_slider], |
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outputs=out_im, |
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title="Haar Cascade Object Detection", |
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).launch() |
