updated app.py

app.py

@@ -2,16 +2,21 @@ import cv2
 import numpy as np
 import streamlit as st
 from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
 
-def get_dominant_color(image, k=1):
+def get_dominant_colors(image, k=8):
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image = image.reshape((-1, 3))
     
     kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
     kmeans.fit(image)
     
-    dominant_color = kmeans.cluster_centers_[0].astype(int)
-    return tuple(dominant_color)
+    colors = kmeans.cluster_centers_.astype(int)
+    labels = kmeans.labels_
+    counts = np.bincount(labels)
+    percentages = counts / sum(counts) * 100
+    
+    return list(zip(colors, percentages))
 
 def is_warm_or_cool(color):
     r, g, b = color
@@ -19,20 +24,90 @@ def is_warm_or_cool(color):
     return "Warm" if warm else "Cool"
 
 def complementary_color(color):
-    r, g, b = color
+    r, g, b = map(int, color)
     return (255 - r, 255 - g, 255 - b)
 
+def plot_color_palette(colors):
+    fig, ax = plt.subplots(figsize=(8, 1))
+    ax.imshow([colors], extent=[0, len(colors), 0, 1])
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.spines[:].set_visible(False)
+    st.pyplot(fig)
+
+def rgb_to_hex(color):
+    return "#" + "".join(f"{c:02x}" for c in color)
+
+def get_color_name(rgb):
+    color_names = {
+        (255, 0, 0): "Red", (0, 255, 0): "Green", (0, 0, 255): "Blue",
+        (255, 255, 0): "Yellow", (0, 255, 255): "Cyan", (255, 0, 255): "Magenta",
+        (128, 0, 0): "Maroon", (128, 128, 0): "Olive", (0, 128, 0): "Dark Green",
+        (128, 0, 128): "Purple", (0, 128, 128): "Teal", (0, 0, 128): "Navy",
+        (255, 165, 0): "Orange", (139, 69, 19): "Brown", (255, 192, 203): "Pink",
+        (192, 192, 192): "Silver", (128, 128, 128): "Gray", (0, 0, 0): "Black",
+        (255, 255, 255): "White"
+    }
+    closest_color = min(color_names.keys(), key=lambda x: np.linalg.norm(np.array(x) - np.array(rgb)))
+    return color_names[closest_color]
+
+def answer_question(question, colors, percentages):
+    if "dominant" in question.lower():
+        return f"The dominant color is {get_color_name(colors[0])} with {percentages[0]:.2f}% presence."
+    elif "complementary" in question.lower():
+        comp_color = complementary_color(colors[0])
+        return f"The complementary color is {comp_color}."
+    elif "warm or cool" in question.lower():
+        return f"The dominant color is a {is_warm_or_cool(colors[0])} color."
+    else:
+        return "I can answer questions related to dominant color, complementary color, or warm/cool classification."
+
 st.title("VQA for Colors and Color Theory")
+
+st.markdown("""
+### 🎨 Welcome to Color Explorer!
+Upload an image and let the magic begin! Our app analyzes the dominant colors, identifies complementary colors, and even determines if the hues are warm or cool. Perfect for designers, artists, and color enthusiasts!
+""")
+
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
 
 if uploaded_file is not None:
+    st.image(uploaded_file, caption=f"Uploaded Image: {uploaded_file.name}", use_container_width=True)
+    
     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
     image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
     
-    dominant = get_dominant_color(image)
+    color_data = get_dominant_colors(image)
+    colors, percentages = zip(*color_data)
+    color_names = [get_color_name(color) for color in colors]
+    
+    st.subheader("Color Analysis")
+    
+    for name, color, percent in zip(color_names, colors, percentages):
+        st.markdown(
+            f'<div style="display: flex; align-items: center; gap: 15px; margin-bottom: 10px;">'
+            f'<div style="width: 50px; height: 50px; background-color: {rgb_to_hex(color)}; border-radius: 5px;"></div>'
+            f'<div><strong>{name}</strong></div>'
+            f'<div>HEX: {rgb_to_hex(color)}</div>'
+            f'<div>RGB: {tuple(map(int, color))}</div>'
+            f'<div>Percentage: {percent:.2f}%</div>'
+            f'</div>',
+            unsafe_allow_html=True
+        )
+    
+    dominant = colors[0]
     comp_color = complementary_color(dominant)
     
-    st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_column_width=True)
-    st.write(f"**Dominant Color:** {dominant}")
+    st.subheader("Additional Info")
     st.write(f"**Temperature:** This is a {is_warm_or_cool(dominant)} color.")
-    st.write(f"**Complementary Color:** {comp_color}")
+    st.write(f"**Complementary Color:** {get_color_name(comp_color)} (HEX: {rgb_to_hex(comp_color)}, RGB: {comp_color})")
+    
+    st.subheader("Generated Color Palette")
+    plot_color_palette(colors)
+    
+    # VQA Section
+    st.subheader("Ask a Question About the Image")
+    user_question = st.text_input("Enter your question:")
+    if user_question:
+        answer = answer_question(user_question, colors, percentages)
+        st.write("**Answer:**", answer)

requirements.txt

@@ -2,3 +2,6 @@ streamlit
 opencv-python
 numpy
 scikit-learn
+matplotlib
+colorthief
+