Upload app.py

app.py

@@ -0,0 +1,151 @@
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import colorsys
+from sklearn.cluster import KMeans
+import io
+
+def rgb_to_hsv(rgb):
+    """Convert RGB to HSV color space."""
+    return colorsys.rgb_to_hsv(rgb[0]/255.0, rgb[1]/255.0, rgb[2]/255.0)
+
+def classify_undertone(h, s, v):
+    """
+    Classify skin undertones based on HSV color space.
+    This is a simplified approximation and should not be used for precise skin tone analysis.
+    """
+    if 0.05 <= h <= 0.15:  # Warm orange-yellow range
+        return 'Warm'
+    elif 0.55 <= h <= 0.75:  # Cool blue-purple range
+        return 'Cool'
+    else:
+        return 'Neutral'
+
+def classify_seasonal_type(h, s, v):
+    """
+    Classify seasonal color types based on HSV values.
+    This is a highly simplified approximation.
+    """
+    if 0.05 <= h <= 0.15 and s > 0.5 and v > 0.5:
+        return 'Spring'
+    elif 0.15 <= h <= 0.35 and s < 0.5 and v > 0.5:
+        return 'Summer'
+    elif 0.35 <= h <= 0.55 and s > 0.5 and v > 0.5:
+        return 'Autumn'
+    elif (h <= 0.05 or h >= 0.75) and s < 0.3 and v < 0.5:
+        return 'Winter'
+    else:
+        return 'Undefined'
+
+def analyze_image_colors(img):
+    """
+    Analyze colors in an image, create visualizations, and perform clustering.
+    """
+    # Convert image to RGB if needed
+    img = img.convert('RGB')
+    
+    # Convert image to numpy array
+    img_array = np.array(img)
+    
+    # Reshape the image to be a list of pixels
+    pixels = img_array.reshape(-1, 3)
+    
+    # Perform K-means clustering
+    kmeans = KMeans(n_clusters=10, random_state=42)
+    kmeans.fit(pixels)
+    
+    # Get cluster centers and labels
+    colors = kmeans.cluster_centers_
+    labels = kmeans.labels_
+    
+    # Convert RGB to HSV for further analysis
+    hsv_colors = np.array([rgb_to_hsv(color) for color in colors])
+    
+    # Classify undertones and seasonal types
+    undertones = [classify_undertone(h, s, v) for h, s, v in hsv_colors]
+    seasonal_types = [classify_seasonal_type(h, s, v) for h, s, v in hsv_colors]
+    
+    # Visualizations
+    plt.figure(figsize=(15, 10))
+    
+    # 1. Color Gradient Plot
+    plt.subplot(2, 2, 1)
+    luminance = np.dot(colors, [0.299, 0.587, 0.114])
+    sorted_indices = np.argsort(luminance)
+    sorted_colors = colors[sorted_indices] / 255.0
+    
+    plt.imshow(sorted_colors[np.newaxis, :], aspect='auto', extent=[0, 1, 0, 1])
+    plt.title('Color Gradient (Sorted by Luminance)')
+    plt.xlabel('Color Progression')
+    plt.xticks([])
+    plt.yticks([])
+    
+    # 2. Color Bar Plot
+    plt.subplot(2, 2, 2)
+    cluster_counts = np.unique(labels, return_counts=True)[1]
+    plt.bar(range(len(colors)), cluster_counts, color=colors/255)
+    plt.title('Color Distribution')
+    plt.xlabel('Cluster')
+    plt.ylabel('Pixel Count')
+    
+    # 3. Pie Chart of Undertones
+    plt.subplot(2, 2, 3)
+    undertone_counts = np.unique(undertones, return_counts=True)
+    plt.pie(undertone_counts[1], labels=undertone_counts[0], autopct='%1.1f%%')
+    plt.title('Undertone Distribution')
+    
+    # 4. Pie Chart of Seasonal Types
+    plt.subplot(2, 2, 4)
+    seasonal_counts = np.unique(seasonal_types, return_counts=True)
+    plt.pie(seasonal_counts[1], labels=seasonal_counts[0], autopct='%1.1f%%')
+    plt.title('Seasonal Type Distribution')
+    
+    plt.tight_layout()
+    
+    # Save plot to a buffer
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.close()
+    
+    # Prepare color cluster details
+    cluster_details = []
+    for i, (color, undertone, season) in enumerate(zip(colors, undertones, seasonal_types)):
+        cluster_details.append({
+            'Cluster': i,
+            'RGB Color': color,
+            'Undertone': undertone,
+            'Seasonal Type': season
+        })
+    
+    return buf, cluster_details
+
+def main():
+    st.title('Image Color Analysis Tool')
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+        # Analyze colors
+        st.write('Analyzing image colors...')
+        plot_buffer, cluster_details = analyze_image_colors(image)
+        
+        # Display analysis plot
+        st.image(plot_buffer, caption='Color Analysis Visualization')
+        
+        # Display cluster details
+        st.subheader('Color Cluster Details')
+        for cluster in cluster_details:
+            st.write(f"**Cluster {cluster['Cluster']}:**")
+            st.write(f"  - RGB Color: {cluster['RGB Color']}")
+            st.write(f"  - Undertone: {cluster['Undertone']}")
+            st.write(f"  - Seasonal Type: {cluster['Seasonal Type']}")
+
+if __name__ == '__main__':
+    main()