Delete pages/video.py

pages/video.py

@@ -1,139 +0,0 @@
-import streamlit as st
-import numpy as np
-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
-  """,unsafe_allow_html=True)
-
-
-
-from PIL import Image
-
-# Set the title of the app
-
-# Function to generate Black & White and Grayscale Images
-def generate_bw_and_grayscale():
-    # Create Black & White binary image
-    bw_image = np.array([[0, 1, 0, 1],
-                         [1, 0, 1, 0],
-                         [0, 1, 0, 1],
-                         [1, 0, 1, 0]])
-
-    # Create Grayscale image
-    grayscale_image = np.array([[0, 64, 128, 255],
-                                 [255, 128, 64, 0],
-                                 [0, 64, 128, 255],
-                                 [255, 128, 64, 0]])
-
-    # Plot and save the Black & White and Grayscale images
-    fig, axes = plt.subplots(1, 2, figsize=(10, 5))
-
-    # Black & White
-    axes[0].imshow(bw_image, cmap='binary', interpolation='nearest')
-    axes[0].set_title("Black & White Image")
-    axes[0].axis('off')
-
-    # Grayscale
-    axes[1].imshow(grayscale_image, cmap='gray', interpolation='nearest')
-    axes[1].set_title("Grayscale Image")
-    axes[1].axis('off')
-
-    plt.tight_layout()
-    return fig
-
-# Function to generate RGB Color Space Images
-def generate_rgb_color_space():
-    # Create RGB arrays
-    red = np.zeros((4, 4, 3), dtype=int)
-    green = np.zeros((4, 4, 3), dtype=int)
-    blue = np.zeros((4, 4, 3), dtype=int)
-
-    # Add intensity to respective channels
-    red[:, :, 0] = 255  # Red channel
-    green[:, :, 1] = 255  # Green channel
-    blue[:, :, 2] = 255  # Blue channel
-
-    # Plot RGB components
-    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
-
-    # Red
-    axes[0].imshow(red)
-    axes[0].set_title("Red Component")
-    axes[0].axis('off')
-
-    # Green
-    axes[1].imshow(green)
-    axes[1].set_title("Green Component")
-    axes[1].axis('off')
-
-    # Blue
-    axes[2].imshow(blue)
-    axes[2].set_title("Blue Component")
-    axes[2].axis('off')
-
-    plt.tight_layout()
-    return fig
-
-# Generate Black & White and Grayscale Images
-st.subheader("Black & White and Grayscale Images")
-bw_grayscale_fig = generate_bw_and_grayscale()
-st.pyplot(bw_grayscale_fig)
-
-# Generate RGB Color Space Images
-st.subheader("RGB Color Space Visualization")
-rgb_fig = generate_rgb_color_space()
-st.pyplot(rgb_fig)