Update pages/Data Collection.py

pages/Data Collection.py

@@ -508,55 +508,55 @@ def image_details_page():
         - **In images**: Each row represents a set of data points (pixels), and the columns represent their features.
         - **In tables**:Each row represents an individual data point, and each column corresponds to a feature of that data point.
         """)
-    
-    st.header("Color Spaces")
-    
-    # Explanation for Color Spaces
-    st.write("""
-    Color space is a technique used to represent the colors of an image. This technique helps us preserve the colors while converting them into numerical values, which machine learning models can understand.
-    
-    For example, in image classification tasks like differentiating between dogs and cats:
-    - The first step is to collect a bunch of dog and cat images. These images may be in formats such as PNG, JPG, or JPEG.
-    - However, machine learning models can only understand numbers, so color spaces are used to convert the image colors into numerical representations.
-    """)
-    
-    # Subheading for Black and White color space
-    st.subheader("1. Black and White")
-    st.write("""
-    - Represents only two colors: **Black (0)** and **White (255)**.
-    - Used for simple image processing tasks where color isn't essential.
-    - **Disadvantage**: It only preserves black and white, so other colors (like red, green, or brown) are completely lost.
-    - For example, by using the image's width and height (rows and columns), we can create a **2D array** where each pixel is represented by either 0 (black) or 255 (white). 
-    - **Use case**: Binary classification problems like simple object detection, where only the presence or absence of a feature matters.
-    """)
-    
-    # Subheading for Grayscale color space
-    st.subheader("2. Grayscale")
-    st.write("""
-    - Extends black and white to include **256 shades of gray**.
-    - Preserves brightness details but loses color information.
-    - **Disadvantage**: If the image has colors like red, green, or brown, it cannot preserve those since grayscale only represents shades of gray.
-    - After converting an image to grayscale, each pixel can take values from 0 (black) to 255 (white), with every intermediate value representing a shade of gray.
-    - **Use case**: Applications where only intensity (brightness) matters, like edge detection or certain medical imaging applications.
-    """)
-    
-    # Subheading for RGB color space
-    st.subheader("3. RGB (Red, Green, Blue)")
-    st.write("""
-    - Combines three color channels: **Red**, **Green**, and **Blue**.
-    - Each channel can represent **256 shades** (0-255).
-    - By mixing different intensities of red, green, and blue, you can create over **16 million possible colors**.
-    - This is the most commonly used color space for colored images and is widely used in digital displays, cameras, and image processing tasks.
-    - In RGB color space, each pixel is represented by three values, one for each channel (Red, Green, Blue). The image is represented as a **3D array** where each pixel has three values (R, G, B).
-    - **Disadvantage**: It requires more data (3 values per pixel), which can be computationally intensive.
-    """)
-    
-    # Connecting the concepts
-    st.subheader("Key Differences Between 2D and 3D Arrays in Color Spaces")
-    st.write("""
-    - **2D Arrays**: Used in Black and White or Grayscale color spaces. Each pixel is represented by a single value (black/white or a shade of gray).
-    - **3D Arrays**: Used in RGB color space, where each pixel is represented by three values (Red, Green, Blue), forming a 3D structure of the image.
-    """)
+        
+        st.header("Color Spaces")
+        
+        # Explanation for Color Spaces
+        st.write("""
+        Color space is a technique used to represent the colors of an image. This technique helps us preserve the colors while converting them into numerical values, which machine learning models can understand.
+        
+        For example, in image classification tasks like differentiating between dogs and cats:
+        - The first step is to collect a bunch of dog and cat images. These images may be in formats such as PNG, JPG, or JPEG.
+        - However, machine learning models can only understand numbers, so color spaces are used to convert the image colors into numerical representations.
+        """)
+        
+        # Subheading for Black and White color space
+        st.subheader("1. Black and White")
+        st.write("""
+        - Represents only two colors: **Black (0)** and **White (255)**.
+        - Used for simple image processing tasks where color isn't essential.
+        - **Disadvantage**: It only preserves black and white, so other colors (like red, green, or brown) are completely lost.
+        - For example, by using the image's width and height (rows and columns), we can create a **2D array** where each pixel is represented by either 0 (black) or 255 (white). 
+        - **Use case**: Binary classification problems like simple object detection, where only the presence or absence of a feature matters.
+        """)
+        
+        # Subheading for Grayscale color space
+        st.subheader("2. Grayscale")
+        st.write("""
+        - Extends black and white to include **256 shades of gray**.
+        - Preserves brightness details but loses color information.
+        - **Disadvantage**: If the image has colors like red, green, or brown, it cannot preserve those since grayscale only represents shades of gray.
+        - After converting an image to grayscale, each pixel can take values from 0 (black) to 255 (white), with every intermediate value representing a shade of gray.
+        - **Use case**: Applications where only intensity (brightness) matters, like edge detection or certain medical imaging applications.
+        """)
+        
+        # Subheading for RGB color space
+        st.subheader("3. RGB (Red, Green, Blue)")
+        st.write("""
+        - Combines three color channels: **Red**, **Green**, and **Blue**.
+        - Each channel can represent **256 shades** (0-255).
+        - By mixing different intensities of red, green, and blue, you can create over **16 million possible colors**.
+        - This is the most commonly used color space for colored images and is widely used in digital displays, cameras, and image processing tasks.
+        - In RGB color space, each pixel is represented by three values, one for each channel (Red, Green, Blue). The image is represented as a **3D array** where each pixel has three values (R, G, B).
+        - **Disadvantage**: It requires more data (3 values per pixel), which can be computationally intensive.
+        """)
+        
+        # Connecting the concepts
+        st.subheader("Key Differences Between 2D and 3D Arrays in Color Spaces")
+        st.write("""
+        - **2D Arrays**: Used in Black and White or Grayscale color spaces. Each pixel is represented by a single value (black/white or a shade of gray).
+        - **3D Arrays**: Used in RGB color space, where each pixel is represented by three values (Red, Green, Blue), forming a 3D structure of the image.
+        """)
         
     # Basic Operations Section
     elif page =="Basic Operations":