Update pages/video.py

pages/video.py

@@ -4,56 +4,56 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 
     # Title
-    st.title("Understanding Video Data and Processing with OpenCV")
-    
-    # Subheader 1: What is Video Data?
-    st.markdown("### What is Video Data?")
-    st.markdown("""
-    - **Video** is a collection of images known as **frames**.
-    - Frames are visualized sequentially, creating the perception of motion.
-    - **Frame Rate (FPS)** determines smoothness:
-      - **30 FPS**: 30 frames per second.
-      - **60 FPS**: 60 frames per second.
-      - More FPS = smoother video.
-    """)
-    
-    # Subheader 2: How to Play a Video Using OpenCV
-    st.markdown("### :blue[How to Play a Video Using OpenCV]")
-    st.markdown("""
-    1. **Break the Video into Frames**:
-       - A video is essentially a sequence of images (frames).
-       - Use a `while` loop to iterate through frames, as the total frame count may not be known.
-    2. **Key Functions**:
-       - `cv2.VideoCapture(path)`: Captures the video and extracts frames.
-       - `cv2.imshow()`: Displays a frame in a pop-up window.
-       - `cv2.waitKey(1)`: Waits for 1 millisecond before moving to the next frame.
-    3. **Exit Condition**:
-       - Exit the loop when frames are exhausted or a specific key is pressed.
-    """)
-    
-    # Subheader 3: Frame Handling Details
-    st.markdown("### Frame Handling Details")
-    st.markdown("""
-    - Each frame contains:
-      1. **Images**: Represented as 3D arrays (height, width, and color channels).
-      2. **Boolean Values**: 
-        - `True`: Frame is present.
-        - `False`: No frame, signaling the loop to terminate.
-    """)
-    
-    # Subheader 4: Converting Video to Tabular Data
-    st.markdown("### :green[Converting Video to Tabular Data]")
-    st.markdown("""
-    1. Use `cv2.VideoCapture()` to capture frames.
-    2. Convert the frames into tabular data, where each frame's pixel values are rows or columns.
-    """)
-    
-    # Subheader 5: Color Space Conversion
-    st.markdown("### Color Space Conversion")
-    st.markdown("""
-    - OpenCV allows color space changes with `cv2.cvtColor()`.
-    - **Syntax**: `cv2.cvtColor(img, cv2.COLOR_<source>2<destination>)`
-    - Example:
-      ```python
-      img = cv2.imread("path/to/image")
-      gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+st.title("Understanding Video Data and Processing with OpenCV")
+
+# Subheader 1: What is Video Data?
+st.markdown("### What is Video Data?")
+st.markdown("""
+- **Video** is a collection of images known as **frames**.
+- Frames are visualized sequentially, creating the perception of motion.
+- **Frame Rate (FPS)** determines smoothness:
+  - **30 FPS**: 30 frames per second.
+  - **60 FPS**: 60 frames per second.
+  - More FPS = smoother video.
+""")
+
+# Subheader 2: How to Play a Video Using OpenCV
+st.markdown("### :blue[How to Play a Video Using OpenCV]")
+st.markdown("""
+1. **Break the Video into Frames**:
+   - A video is essentially a sequence of images (frames).
+   - Use a `while` loop to iterate through frames, as the total frame count may not be known.
+2. **Key Functions**:
+   - `cv2.VideoCapture(path)`: Captures the video and extracts frames.
+   - `cv2.imshow()`: Displays a frame in a pop-up window.
+   - `cv2.waitKey(1)`: Waits for 1 millisecond before moving to the next frame.
+3. **Exit Condition**:
+   - Exit the loop when frames are exhausted or a specific key is pressed.
+""")
+
+# Subheader 3: Frame Handling Details
+st.markdown("### Frame Handling Details")
+st.markdown("""
+- Each frame contains:
+  1. **Images**: Represented as 3D arrays (height, width, and color channels).
+  2. **Boolean Values**: 
+    - `True`: Frame is present.
+    - `False`: No frame, signaling the loop to terminate.
+""")
+
+# Subheader 4: Converting Video to Tabular Data
+st.markdown("### :green[Converting Video to Tabular Data]")
+st.markdown("""
+1. Use `cv2.VideoCapture()` to capture frames.
+2. Convert the frames into tabular data, where each frame's pixel values are rows or columns.
+""")
+
+# Subheader 5: Color Space Conversion
+st.markdown("### Color Space Conversion")
+st.markdown("""
+- OpenCV allows color space changes with `cv2.cvtColor()`.
+- **Syntax**: `cv2.cvtColor(img, cv2.COLOR_<source>2<destination>)`
+- Example:
+  ```python
+  img = cv2.imread("path/to/image")
+  gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)