Update pages/3_Life Cycle Of ML Project.py

pages/3_Life Cycle Of ML Project.py

@@ -133,56 +133,116 @@ elif st.session_state.page == "unstructured_data":
     - Audio files (e.g., .mp3, .wav)
     """)
 
-    st.header("📄 Handling Text Data")
-    st.markdown("""
-    Text data can be analyzed using Natural Language Processing (NLP) techniques.
-    """)
-    st.code("""
+    #st.header("📄 Handling Text Data")
+   # st.markdown("""
+   # Text data can be analyzed using Natural Language Processing (NLP) techniques.
+   # st.code("""
 # Reading text data
-with open('sample.txt', 'r') as file:
-    text = file.read()
-    print(text)
+#with open('sample.txt', 'r') as file:
+   # text = file.read()
+   # print(text)
 # Basic text processing using NLTK
-import nltk
-from nltk.tokenize import word_tokenize
-nltk.download('punkt')
-tokens = word_tokenize(text)
-print(tokens)
-    """, language='python')
-
-
-    st.header("🎥 Handling Video Data")
-    st.markdown("""
-    Videos can be processed frame by frame using OpenCV.
-    """)
-    st.code("""
-import cv2
+#import nltk
+#from nltk.tokenize import word_tokenize
+##print(tokens)
+   # """, language='python')
+
+
+   # st.header("🎥 Handling Video Data")
+   # st.markdown("""
+   # Videos can be processed frame by frame using OpenCV.
+   # """)
+   # st.code("""
+#import cv2
 # Capture video
-video = cv2.VideoCapture('sample_video.mp4')
-while video.isOpened():
-    ret, frame = video.read()
-    if not ret:
-        break
-    cv2.imshow('Frame', frame)
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-video.release()
-cv2.destroyAllWindows()
-    """, language='python')
+#video = cv2.VideoCapture('sample_video.mp4')
+#while video.isOpened():
+    #ret, frame = video.read()
+    #if not ret:
+      #  break
+    #cv2.imshow('Frame', frame)
+    #if cv2.waitKey(1) & 0xFF == ord('q'):
+       # break
+#video.release()
+#cv2.destroyAllWindows()
+    #""", language='python')
     
-    st.header("🖼️ Handling Image Data")
-    st.markdown("""
-    Image data can be processed using libraries like OpenCV and PIL (Pillow).
-    """)
-    st.code("""
-from PIL import Image
+   # st.header("🖼️ Handling Image Data")
+   # st.markdown("""
+   # Image data can be processed using libraries like OpenCV and PIL (Pillow).
+    #""")
+   # st.code("""
+#from PIL import Image
 # Open an image file
-image = Image.open('sample_image.jpg')
-image.show()
+#image = Image.open('sample_image.jpg')
+#image.show()
 # Convert image to grayscale
-gray_image = image.convert('L')
-gray_image.show()
-    """, language='python')
+#gray_image = image.convert('L')
+#gray_image.show()
+  #  """, language='python')
+
+if st.button("Introduction to Image"):
+        st.session_state.page = "introduction_to_image"
+# ----------------- Introduction to Image ----------------- 
+def introduction_to_image_page(): 
+    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: - JPEG 
+    - PNG 
+    - GIF
+    - **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. """) 
+    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) r
+    otated_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. """) 
+    # Button to Navigate to Operations Using OpenCV 
+    if st.button("Operations Using OpenCV"): 
+        st.session_state.page = "operations_using_opencv" 
+        # Navigation Button 
 
     if st.button(":red[Back to Data Collection]"):
         st.session_state.page = "data_collection"