import streamlit as st import numpy as np import cv2 import matplotlib.pyplot as plt # ----------------------------------------------------------------- # Utility functions # ----------------------------------------------------------------- def generate_colorful_image(height=256, width=256, p_blue=0.5): """ Generates a synthetic image (height x width) with: - 'p_blue' fraction of blueish pixels - (1 - p_blue) fraction of near-white/grey Includes near-blue shades for more realistic challenge. """ img = np.zeros((height, width, 3), dtype=np.uint8) for i in range(height): for j in range(width): if np.random.rand() < p_blue: # Random shade of blue b = np.random.randint(100, 256) g = np.random.randint(0, 121) r = np.random.randint(0, 121) if np.random.rand() < 0.3: # shift to near-blue g += np.random.randint(0, 30) r += np.random.randint(0, 30) b = min(b, 255) g = min(g, 255) r = min(r, 255) img[i, j] = [b, g, r] else: # White/grey region base = np.random.randint(180, 256) diff_r = np.random.randint(-20, 20) diff_g = np.random.randint(-20, 20) diff_b = np.random.randint(-20, 20) b = np.clip(base + diff_b, 0, 255) g = np.clip(base + diff_g, 0, 255) r = np.clip(base + diff_r, 0, 255) img[i, j] = [b, g, r] return img def simple_threshold_blue(image_bgr): """ Simple approach: 1) Convert to HSV 2) Single broad threshold for blue 3) Count ratio of blue pixels """ hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV) # Broad range for 'blue' lower_blue = np.array([90, 50, 50], dtype=np.uint8) upper_blue = np.array([130, 255, 255], dtype=np.uint8) mask = cv2.inRange(hsv, lower_blue, upper_blue) blue_pixels = cv2.countNonZero(mask) total_pixels = image_bgr.shape[0] * image_bgr.shape[1] perc_blue = (blue_pixels / total_pixels) * 100 return perc_blue, mask def plot_color_histogram(image_bgr): """ Creates a matplotlib figure of B, G, and R channel histograms. """ color = ('b','g','r') fig, ax = plt.subplots(figsize=(4,3)) for i,col in enumerate(color): hist = cv2.calcHist([image_bgr],[i],None,[256],[0,256]) ax.plot(hist, color=col) ax.set_xlim([0,256]) ax.set_title("Color Channel Histogram") ax.set_xlabel("Pixel Intensity") ax.set_ylabel("Frequency") fig.tight_layout() return fig # ----------------------------------------------------------------- # Streamlit App # ----------------------------------------------------------------- # Page config st.set_page_config(page_title="ITC PSPD - Blue Area Detection Demo", layout="centered") st.title("Blue Area Detection Demo for ITC PSPD") st.markdown(""" **This assignment showcases an Industry 4.0 approach** to **color segmentation**, demonstrating how tools like Python, OpenCV, and Streamlit can automate **quality checks** (similar to checking the quality of paperboards, packaging prints, or other color-critical products). --- **Why It Matters for ITC PSPD**: - ITC Paperboards & Specialty Papers Division (PSPD) is a leader in paper, packaging, and specialty solutions. - Precise color detection ensures **consistent brand identity**, reduces **defects**, and aligns with ITC's **sustainability** and **innovation** ethos. Below, you can: 1. **Generate** a synthetic image with random shades of **blue** and **near-white** regions. 2. **Analyze** the image using **Simple** thresholding. 3. **Visualize** color histograms and masks. 4. See how this **digital transformation** approach can benefit large-scale production lines. --- """) st.sidebar.header("Generation Controls") p_blue = st.sidebar.slider("Fraction of Blue-ish Pixels", 0.0, 1.0, 0.5, 0.05) img_size = st.sidebar.selectbox("Image Size (px)", [128, 192, 256, 320], index=2) if "random_image" not in st.session_state: st.session_state["random_image"] = None st.sidebar.markdown("---") if st.sidebar.button("Generate New Random Image"): img_bgr = generate_colorful_image(height=img_size, width=img_size, p_blue=p_blue) st.session_state["random_image"] = img_bgr # Check if we have an image if st.session_state["random_image"] is None: st.warning("Use the sidebar to generate a new image.") else: st.subheader("1) Randomly Generated Image & Color Analysis") # Convert BGR->RGB for display img_bgr = st.session_state["random_image"] img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) col1, col2 = st.columns(2) with col1: st.image(img_rgb, caption="Synthesized Image (RGB)", width=250) with col2: # Show color histogram hist_fig = plot_color_histogram(img_bgr) st.pyplot(hist_fig) st.markdown("---") # Simple Threshold st.subheader("2) Simple Threshold Approach") simple_perc_blue, simple_mask = simple_threshold_blue(img_bgr) col3, col4 = st.columns(2) with col3: st.write(f"**Blue Percentage (Simple):** {simple_perc_blue:.2f}%") st.progress(min(simple_perc_blue/100, 1.0)) with col4: st.image(simple_mask, caption="Simple Mask (white=detected blue)", width=250) st.markdown("---") st.markdown(""" --- ## Relevance to ITC PSPD: - **Digital Quality Control**: A color detection system like this can **validate printed matter** (cartons, labels) in real time. - **Big Data Integration**: Results could be uploaded to a **data lake**, enabling historical trend analysis and continuous improvement. - **Sustainability**: **Accurate color checks** reduce material wastage, aligning with ITC’s triple bottom line (economic, social, environmental) philosophy. - **Industry 4.0**: Coupling this solution with **IoT** sensors, real-time dashboards, and advanced analytics ensures **agile and data-driven** paperboard manufacturing. ## Made By: - Name: Kaustubh Raykar - PRN: 21070126048 - Btech AIML 2021-25 - Contact: +91 7020524609 - Symbiosis Institute Of Technology, Pune - kaustubh.raykar.btech2021@sitpune.edu.in - raykarkaustubh@gmail.com --- **Thank you for exploring this demonstration!** """)