Delete pages/Data Collection.py

pages/Data Collection.py

@@ -1,227 +0,0 @@
-import streamlit as st
-import numpy as np
-
-# Helper function for subheadings
-def subheading(text):
-    """Displays a subheader with consistent styling."""
-    st.markdown(f"<h3 style='color:teal;'>{text}</h3>", unsafe_allow_html=True)
-
-# Sidebar for navigation
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to", ["Introduction", "Basic Operations","Converting Image to Black&White ,BGR & Gray SCale"])
-
-# App Title and Description
-st.title("Image Processing Fundamentals")
-st.write("""
-This app introduces the basics of image processing, helping you understand how images are formed, represented, and handled programmatically. 
-It's designed for beginners exploring computer vision concepts.
-""")
-
-# Introduction Section
-if page == "Introduction":
-    st.header("Introduction")
-    st.write("""
-    Images play a crucial role in various fields, including art, science, and technology.  
-    In this app, you will learn:
-    - How images are captured and represented.
-    - Different color spaces and their applications.
-    - Basic operations on images using Python libraries.
-    """)
-
-    st.header("Understanding Images")
-    
-    # Subsections
-    subheading("What is an Image?")
-    st.write("""
-    An image is a **2D representation of light**, created when light reflects off an object and is captured by a camera or our eyes.
-    """)
-
-    subheading("How is an Image Formed?")
-    st.write("""
-    - **Light Source**: Light from sources like the sun or a bulb hits an object.
-    - **Reflection**: Light bounces off the object's surface.
-    - **Capture**: The reflected light is recorded by a camera sensor or the human eye.
-    """)
-
-    subheading("Why is an Image Represented as a Grid?")
-    st.write("""
-    - Images are stored as grids of **pixels**, where each pixel represents a single point of color and brightness.
-    - These grids are also called **image matrices**.
-    """)
-
-    st.header("Color Spaces")
-    
-    subheading("1. Black and White")
-    st.write("""
-    - Represents two colors: **Black (0)** and **White (255)**.
-    - Used for simple image processing tasks where color isn't essential.
-    """)
-
-    subheading("2. Grayscale")
-    st.write("""
-    - Extends black and white to include **256 shades of gray**.
-    - Preserves brightness details but loses color information.
-    """)
-
-    subheading("3. RGB (Red, Green, Blue)")
-    st.write("""
-    - Combines three color channels: **Red**, **Green**, and **Blue**.
-    - Most commonly used for colored images, capable of representing over **16 million colors**.
-    """)
-
-# Basic Operations Section
-elif page == "Basic Operations":
-    
-    st.code(""" 
-    import cv2
-    import numpy as np
-    """)
-    
-    st.title("OpenCV Basics: Image Handling")
-    
-    # Theory Section
-    st.header("1. `cv2.imread` - Reading an Image")
-    st.markdown("""
-    - **Purpose**: Converts an image file into a numerical array that represents its pixel data.
-    - **Default Behavior**: Reads the image in the **BGR color space**.
-    - **Parameters**:  
-      - `path`: Specifies the location of the image file.  
-      - `flags`:  
-        - `1`: Reads the image as a color image (default).  
-        - `0`: Reads the image as a grayscale image (2D array).
-    - **Output**: A numerical array where each element corresponds to a pixel's intensity or color.
-    """)
-    st.code("""
-    # Code to read an image
-    img = cv2.imread('path/to/your/image.jpg', 1)  # Read as color image
-    st.image(img, channels="BGR")  # Display image in Streamlit
-    """)
-    
-    # Theory Section
-    st.header("2. `cv2.imshow` - Displaying an Image")
-    st.markdown("""
-    - **Purpose**: Opens a pop-up window to show an image.
-    - **Parameters**:  
-      - `Window Name`: Title of the display window.
-      - `Image`: Array data representing the image.
-    """)
-    st.code("""
-    # Code to display an image
-    cv2.imshow("Sample Image", img)
-    """)
-    
-    # Theory Section
-    st.header("3. `cv2.waitKey` - Waiting for a Key Input")
-    st.markdown("""
-    - **Purpose**: Waits for the specified time (in milliseconds) for a key press to proceed or close the window.
-    - **Parameters**:
-      - `Delay`:  
-        - If `0`: Waits indefinitely until a key is pressed.  
-        - If a positive value: Waits for that duration in milliseconds.
-    - **Usage**: Prevents the program from closing the window immediately.
-    """)
-    st.code("""
-    # Code to wait for a key press
-    cv2.waitKey(0)  # Wait indefinitely until key press
-    """)
-    
-    # Theory Section
-    st.header("4. `cv2.destroyAllWindows` - Closing OpenCV Windows")
-    st.markdown("""
-    - **Purpose**: Closes all OpenCV-created windows.
-    - **Usage**: Ensures proper memory cleanup and avoids crashes by freeing up resources after the display is no longer needed.
-    - **Best Practice**: Always use this after displaying images to clean up the resources.
-    """)
-    st.code("""
-    # Code to close all windows
-    cv2.destroyAllWindows()  # Close all OpenCV windows
-    """)
-    
-    st.markdown("""
-    ### Additional Notes
-    - **Why Use `cv2.waitKey`?**  
-      Without this, the image display window will close immediately after the program finishes execution.
-      
-    - **Handling Pop-Up Windows**  
-      - Use `cv2.destroyAllWindows()` to close all pop-up windows and release system resources properly.
-    """)
-
-
-elif page =="Converting Image to Black&White ,BGR & Gray SCale":
-
-    from PIL import Image
-    
-    # Title of the app
-    st.header("Black & White Images")
-    
-    # Display the code
-    st.subheader("Code to Create and Display Black & White Images")
-    st.code("""
-    import numpy as np
-    import streamlit as st
-    
-    # Create images
-    white_img = np.full((500, 500), 255, dtype=np.uint8)  # White image
-    black_img = np.zeros((500, 500), dtype=np.uint8)      # Black image
-
-     # Display in OpenCV
-    cv2.imshow("White image", white_img)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-    cv2.imshow("Black image", black_img)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-    """, language="python")
-    
-    
-    # Section 1: Grayscale Image
-    st.header("Grayscale Image Creation")
-    st.code("""
-    # Grayscale image creation
-    gray_img = np.full((500, 500), 155, dtype=np.uint8)
-    
-    # Display in OpenCV
-    cv2.imshow("Gray Image", gray_img)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-    """, language="python")
-    
-    # Section 2: BGR Channels
-    st.header("BGR Color Channels")
-    st.code("""
-    # Creating BGR channels
-    blue_channel = cv2.merge([
-        np.full((300, 300), 255, dtype=np.uint8),  # Blue
-        np.zeros((300, 300), dtype=np.uint8),     # Green
-        np.zeros((300, 300), dtype=np.uint8)      # Red
-    ])
-    green_channel = cv2.merge([
-        np.zeros((300, 300), dtype=np.uint8),     # Blue
-        np.full((300, 300), 255, dtype=np.uint8), # Green
-        np.zeros((300, 300), dtype=np.uint8)      # Red
-    ])
-    red_channel = cv2.merge([
-        np.zeros((300, 300), dtype=np.uint8),     # Blue
-        np.zeros((300, 300), dtype=np.uint8),     # Green
-        np.full((300, 300), 255, dtype=np.uint8)  # Red
-    ])
-    """, language="python")
-    
-    st.code("""
-    # Display in OpenCV
-    cv2.imshow("Blue Channel", blue_channel)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-    
-    cv2.imshow("Green Channel", green_channel)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-    
-    cv2.imshow("Red Channel", red_channel)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-    """, language="python")
-    
-    if st.button("Go Back"):
-        st.experimental_set_query_params(page="Introduction")