Update pages/3_Life Cycle Of ML Project.py

pages/3_Life Cycle Of ML Project.py

@@ -189,65 +189,65 @@ elif st.session_state.page == "unstructured_data":
 elif st.session_state.page == "Introduction_to_image":
     st.header("🖼️ What is Image")
     st.markdown("""
-   An image is a two-dimensional visual representation of objects, people, scenes, or concepts. It can be captured using devices like cameras, scanners, or created digitally. Images are composed of individual units called pixels, which contain information about brightness and color.
-Types of Images:
-- **Raster Images (Bitmap)**: Composed of a grid of pixels. Common formats include:
+            An image is a two-dimensional visual representation of objects, people, scenes, or concepts. It can be captured using devices like cameras, scanners, or created digitally. Images are composed of individual units called pixels, which contain information about brightness and color.
+           Types of Images:
+    - **Raster Images (Bitmap)**: Composed of a grid of pixels. Common formats include:
     - JPEG
     - PNG
     - GIF
-- **Vector Images**: Defined by mathematical equations and geometric shapes like lines and curves. Common format:
+    - **Vector Images**: Defined by mathematical equations and geometric shapes like lines and curves. Common format:
     - SVG (Scalable Vector Graphics)
-- **3D Images**: Represent objects or scenes in three dimensions, often used for rendering and modeling.
-Image Representation:
-- **Grayscale Image**: Each pixel has a single intensity value, typically ranging from 0 (black) to 255 (white), representing different shades of gray.
-- **Color Image**: Usually represented in the RGB color space, where each pixel consists of three values indicating the intensity of Red, Green, and Blue.
-Applications of Images:
-- **Photography & Visual Media**: Capturing moments and storytelling.
-- **Medical Imaging**: Diagnosing conditions using X-rays, MRIs, etc.
-- **Machine Learning & AI**: Tasks like image classification, object detection, and facial recognition.
-- **Remote Sensing**: Analyzing geographic and environmental data using satellite imagery.
-- **Graphic Design & Art**: Creating creative visual content for marketing and design.
-""")
+    - **3D Images**: Represent objects or scenes in three dimensions, often used for rendering and modeling.
+                  Image Representation:
+    - **Grayscale Image**: Each pixel has a single intensity value, typically ranging from 0 (black) to 255 (white), representing different shades of gray.
+    - **Color Image**: Usually represented in the RGB color space, where each pixel consists of three values indicating the intensity of Red, Green, and Blue.
+        Applications of Images:
+    - **Photography & Visual Media**: Capturing moments and storytelling.
+    - **Medical Imaging**: Diagnosing conditions using X-rays, MRIs, etc.
+    - **Machine Learning & AI**: Tasks like image classification, object detection, and facial recognition.
+    - **Remote Sensing**: Analyzing geographic and environmental data using satellite imagery.
+    - **Graphic Design & Art**: Creating creative visual content for marketing and design.
+        """)
 
     st.code("""
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-# Open an image file
-image = Image.open('sample_image.jpg')
-image.show()
-# Convert image to grayscale
-gray_image = image.convert('L')
-gray_image.show()
-# Resize the image
-resized_image = image.resize((200, 200))
-resized_image.show()
-# Rotate the image by 90 degrees
-rotated_image = image.rotate(90)
-rotated_image.show()
-# Convert the image to a NumPy array and display its shape
-image_array = np.array(image)
-print(image_array.shape)
-# Display the image array as a plot
-plt.imshow(image)
-plt.title("Original Image")
-plt.axis('off')
-plt.show()
+    from PIL import Image
+    import numpy as np
+    import matplotlib.pyplot as plt
+    # Open an image file
+    image = Image.open('sample_image.jpg')
+    image.show()
+    # Convert image to grayscale
+    gray_image = image.convert('L')
+    gray_image.show()
+    # Resize the image
+    resized_image = image.resize((200, 200))
+    resized_image.show()
+    # Rotate the image by 90 degrees
+    rotated_image = image.rotate(90)
+    rotated_image.show()
+    # Convert the image to a NumPy array and display its shape
+    image_array = np.array(image)
+    print(image_array.shape)
+    # Display the image array as a plot
+    plt.imshow(image)
+    plt.title("Original Image")
+    plt.axis('off')
+    plt.show()
     """, language='python')
 
     st.header("Color Spaces in Machine Learning")
     st.markdown("""
     A color space is a mathematical model for representing colors. In machine learning, different color spaces can be used for preprocessing and analyzing image data, depending on the task.
-Common Color Spaces:
-- **RGB (Red, Green, Blue)**: The most common color space for digital images. Each pixel is represented by a combination of three values corresponding to the red, green, and blue channels.
-  - **Use Cases**: Image classification, general-purpose image analysis.
-- **HSV (Hue, Saturation, Value)**: Separates color information (hue) from intensity (value), making it useful for tasks where distinguishing between color variations and intensity is important.
-  - **Use Cases**: Color-based object detection, image segmentation, color tracking.
-- **CMYK (Cyan, Magenta, Yellow, Black)**: Primarily used for printing, not commonly used in machine learning, but useful for preparing images for printers.
-  - **Use Cases**: Printing applications.
-- **LAB (Lightness, A, B)**: Designed to be perceptually uniform, meaning that the perceptual difference between colors is consistent across the space.
-  - **Use Cases**: Color correction, image processing tasks requiring color consistency.
-""")
+    Common Color Spaces:
+    - **RGB (Red, Green, Blue)**: The most common color space for digital images. Each pixel is represented by a combination of three values corresponding to the red, green, and blue channels.
+      - **Use Cases**: Image classification, general-purpose image analysis.
+    - **HSV (Hue, Saturation, Value)**: Separates color information (hue) from intensity (value), making it useful for tasks where distinguishing between color variations and intensity is important.
+      - **Use Cases**: Color-based object detection, image segmentation, color tracking.
+    - **CMYK (Cyan, Magenta, Yellow, Black)**: Primarily used for printing, not commonly used in machine learning, but useful for preparing images for printers.
+      - **Use Cases**: Printing applications.
+    - **LAB (Lightness, A, B)**: Designed to be perceptually uniform, meaning that the perceptual difference between colors is consistent across the space.
+      - **Use Cases**: Color correction, image processing tasks requiring color consistency.
+    """)
 
     # Button to Navigate to Operations Using OpenCV
     if st.button("Operations Using OpenCV"):