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import cv2 |
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import gradio as gr |
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from deepface import DeepFace |
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def analyze_fn(img_path): |
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img = cv2.imread(img_path) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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objs = DeepFace.analyze(img_path=img_path, actions=['age', 'gender', 'race', 'emotion']) |
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obj = objs[0] |
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age = obj["age"] |
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gender = obj["dominant_gender"] |
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race = obj["dominant_race"] |
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emotion = obj["dominant_emotion"] |
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region = obj["region"] |
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x, y, w, h = region["x"], region["y"], region["w"], region["h"] |
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cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2) |
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return [img, age, gender, race, emotion] |
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with gr.Blocks() as demo: |
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title = """<p><h1 align="center" style="font-size: 36px;">Facial Attribute Analyzer</h1></p>""" |
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gr.HTML(title) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label="Upload Image", type="filepath") |
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analyze = gr.Button("Analyze") |
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with gr.Column(): |
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age_box = gr.Textbox(label="Age") |
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gender_box = gr.Textbox(label="Gender") |
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race_box = gr.Textbox(label="Race") |
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emotion_box = gr.Textbox(label="Emotion") |
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analyze.click(fn=analyze_fn, inputs=image, outputs=[image, age_box, gender_box, race_box, emotion_box], api_name="greet") |
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demo.launch() |
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