Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from sklearn.cluster import KMeans
+import numpy as np
+import cv2
+from webcolors import hex_to_rgb, rgb_to_hex
+from scipy.spatial import KDTree
+from collections import Counter
+
+model = tf.keras.models.load_model("model.h5")
+
+classes = [
+    "background", "skin", "left eyebrow", "right eyebrow",
+    "left eye", "right eye", "nose", "upper lip", "inner mouth",
+    "lower lip", "hair"
+]
+
+def face_skin_extract(pred, image_x):
+    output = np.zeros_like(image_x, dtype=np.uint8)
+    mask = (pred == 1)
+    output[mask] = image_x[mask]
+    return output
+
+def extract_dom_color_kmeans(img):
+
+    
+    mask = ~np.all(img == [0, 0, 0], axis=-1) # Mask to exclude black pixels
+
+    
+    non_black_pixels = img[mask] # Extract non-black pixels
+
+    
+    k_cluster = KMeans(n_clusters=3, n_init="auto") # Apply KMeans clustering on non-black pixels
+    k_cluster.fit(non_black_pixels)
+
+
+
+
+    width = 300
+    palette = np.zeros((50, width, 3), dtype=np.uint8)
+    
+    n_pixels = len(k_cluster.labels_)
+    counter = Counter(k_cluster.labels_)
+    
+    perc = {i: np.round(counter[i] / n_pixels, 2) for i in counter}
+    perc = dict(sorted(perc.items()))
+
+    cluster_centers = k_cluster.cluster_centers_
+    
+    # print("Cluster Percentages:", perc)
+    # print("Cluster Centers (RGB):", cluster_centers)
+
+    val = list(perc.values())
+    val.sort()
+    res = val[-1]
+    print(res)
+    sec_high_val = list(perc.keys())[list(perc.values()).index(res)]
+    rgb_list = cluster_centers[sec_high_val]
+
+    step = 0
+    for idx, center in enumerate(k_cluster.cluster_centers_):
+        width_step = int(perc[idx] * width + 1)
+        palette[:, step:step + width_step, :] = center
+        step += width_step
+    
+    return rgb_list
+
+def closest_tone_match(rgb_tuple):
+    skin_tones = {'Monk 10': '#292420', 'Monk 9': '#3a312a', 'Monk 8':'#604134', 'Monk 7':'#825c43', 'Monk 6':'#a07e56', 'Monk 5':'#d7bd96', 'Monk 4':'#eadaba', 'Monk 3':'#f7ead0', 'Monk 2':'#f3e7db', 'Monk 1':'#f6ede4'}
+
+    rgb_values = []
+    names = []
+    for monk in skin_tones:
+        names.append(monk)
+        rgb_values.append(hex_to_rgb(skin_tones[monk]))
+    
+    kdt_db = KDTree(rgb_values)
+    distance, index = kdt_db.query(rgb_tuple)
+    monk_hex = skin_tones[names[index]]
+    derived_hex = rgb_to_hex((int(rgb_tuple[0]), int(rgb_tuple[1]), int(rgb_tuple[2])))
+    return names[index],monk_hex,derived_hex 
+
+def process_image(image_path):
+
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    image_x = cv2.resize(image, (512, 512))
+    image_norm = image_x / 255.0
+    image_norm = np.expand_dims(image_norm, axis=0).astype(np.float32)
+    
+    pred = model.predict(image_norm)[0]
+    pred = np.argmax(pred, axis=-1).astype(np.int32)
+    
+    face_skin = face_skin_extract(pred, image_x)
+    face_skin_vis = cv2.imread(face_skin)
+    dominant_color_rgb = extract_dom_color_kmeans(face_skin)
+    
+    monk_tone, monk_hex, derived_hex = closest_tone_match(
+        (dominant_color_rgb[0], dominant_color_rgb[1], dominant_color_rgb[2])
+    )
+    
+    return [monk_tone,derived_hex,monk_hex,face_skin_vis]
+    
+
+inputs = gr.Image(type="filepath", label="Upload Face Image")
+outputs = [
+    gr.Label(label="Monk Skin Tone"),          
+    gr.ColorPicker(label="Derived Color"),   
+    gr.ColorPicker(label="Closest Monk Color"),
+    # gr.JSON(label="Dominant RGB Values"),      
+    gr.Image(label="Skin Mask Visualization")
+]
+
+interface = gr.Interface(
+    fn=process_image,
+    inputs=inputs,
+    outputs=outputs,
+    title="Skin Tone Analysis",
+    description="Upload a face image to analyse skin tone and find closest Monk skin tone match."
+)
+
+if __name__ == "__main__":
+    interface.launch()