Update pages/Data Collection.py

pages/Data Collection.py

@@ -555,67 +555,87 @@ def image_details_page():
     # Basic Operations Section
     elif page =="Basic Operations":
         
+    
+        st.title("What is OpenCV?")
+        st.header("Understanding Open Source Computer Vision")
+        
+        # Introduction
+        st.write("""
+        OpenCV (Open Source Computer Vision) is a free and open-source library designed for real-time computer vision tasks. 
+        It is widely used in industries like healthcare, security, robotics, and AI to process images and videos effectively.
+        """)
+
         st.code(""" 
         import cv2
         import numpy as np
         """)
         
-        st.title("OpenCV Basics: Image Handling")
-        
-        # Theory Section
-        st.header("1. `cv2.imread` - Reading an Image")
+        # Features Section
+        st.subheader("Key Features of OpenCV:")
         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
+        - **Image Processing**: Resize, crop, filter, and manipulate images easily.  
+        - **Object Detection**: Detect faces, objects, and track their movements in real-time.  
+        - **Video Analysis**: Perform video stabilization, motion detection, and frame-by-frame analysis.  
+        - **Machine Learning Integration**: Combine with AI frameworks for advanced tasks like face recognition and augmented reality.  
         """)
         
         # Theory Section
-        st.header("2. `cv2.imshow` - Displaying an Image")
+        st.header("Reading 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.
+        - It converts an 2D image into Machine representation value array.
+        - **cv2.imread("path)** this method going to convert image to 3D aray as it used default colour space **RGB**.
+        - The data type of image should be **uint8**.
         """)
         st.code("""
-        # Code to display an image
-        cv2.imshow("Sample Image", img)
+        # Code to read an image
+        img = cv2.imread('BGR_image', 1)  # by default it considers this as coloured image
+        print(img)
+         """)
+
+        st.code("""
+        img = cv2.imread("gray_scale_image",0)  # when we want it in 2D array use parametre `flags=0` it considers as grayscale image
+        print(img)
         """)
         
+        
         # Theory Section
-        st.header("3. `cv2.waitKey` - Waiting for a Key Input")
+        st.header("`cv2.imshow`")
         st.markdown("""
-        - **Purpose**: Waits for the specified time (in milliseconds) for a key press to proceed or close the window.
+        - After creating or reading an image, we can display it using OpenCV. Here’s how the key functions work together:
+        - The `imshow()` function creates a pop-up window to display the image.
+        - Internally, it converts the numerical array into a visual image.
         - **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.
+        Window Name: Title of the pop-up window (string).
+        Image Array: The array representing the image.
         """)
-        st.code("""
-        # Code to wait for a key press
-        cv2.waitKey(0)  # Wait indefinitely until key press
+
+        # Theory Section
+        st.header("`cv2.waitKey`")
+        st.markdown("""
+        - The main purpose Waits for a key press and adds a delay before closing the pop-up window.
+        - `waitKey(0)` or `waitKey()` Keeps the window open indefinitely until a key is pressed.
+        - `waitkey(10) After 10 milli seconds the pop up window will be closed when we use `waitkey(n)` after n milliseconds window closes.
         """)
         
         # Theory Section
-        st.header("4. `cv2.destroyAllWindows` - Closing OpenCV Windows")
+        st.header("`cv2.destroyAllWindows`")
         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.
+        - **Usage**:This makes sure that memory is cleared and helps avoid crashes by getting rid of resources when the image is no longer needed.
         """)
+
+        st.markdown("<h5 style='color: green;'>These three functions must work together to display and manage images effectively./h5>", unsafe_allow_html=True)
+
+        
         st.code("""
+        img = cv2.imshow("Window name",image)
+
+        # Window name : Name of the window
+        # image : The image we created
+
+       # Code to wait for a key press
+        cv2.waitKey()  # Wait indefinitely until key press
+        
         # Code to close all windows
         cv2.destroyAllWindows()  # Close all OpenCV windows
         """)