Update pages/3_Life Cycle Of ML Project.py
Browse files
pages/3_Life Cycle Of ML Project.py
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@@ -209,7 +209,7 @@ elif st.session_state.page == "Introduction_to_image":
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- **Graphic Design & Art**: Creating creative visual content for marketing and design.
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""")
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-
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from PIL import Image
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import numpy as np
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import matplotlib.pyplot as plt
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@@ -235,8 +235,8 @@ elif st.session_state.page == "Introduction_to_image":
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plt.show()
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""", language='python')
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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.
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Common Color Spaces:
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- **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.
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@@ -262,8 +262,8 @@ elif st.session_state.page == "Introduction_to_image":
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elif st.session_state.page == "operations_using_opencv":
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# Header and description for cv2.imread
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-
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-
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**`cv2.imread()` - Read an Image**
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**Purpose:** Load an image from a file and convert it to a NumPy array.
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**Syntax:**
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@@ -285,8 +285,8 @@ elif st.session_state.page == "operations_using_opencv":
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""")
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# Header and description for cv2.imshow
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-
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-
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**`cv2.imshow()` - Display an Image**
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**Purpose:** Show an image in a window.
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**Syntax:**
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""")
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# Header and description for cv2.imwrite
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-
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**`cv2.imwrite()` - Write/Save an Image**
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**Purpose:** Save an image to a file.
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**Syntax:**
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@@ -346,9 +346,9 @@ elif st.session_state.page == "operations_using_opencv":
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elif st.session_state.page == "operations_using_opencv":
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# Header for Image Conversion
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-
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-
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**OpenCV supports many color spaces for image processing.**
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**Common Conversions:**
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""")
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# Header for Splitting Channels
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-
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**Splitting an image into its individual color channels (B, G, R) allows you to analyze or modify each channel independently.**
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**Syntax:**
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```python
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""")
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# Header for Merging Channels
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-
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-
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**You can merge the individual channels back into a color image using `cv2.merge()`.**
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**Syntax:**
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```python
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""")
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# Header for Combining with Modifications
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-
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-
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**You can modify each channel (e.g., increase brightness in the red channel) before merging them back together.**
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**Example:**
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```python
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#---------VIDEO CAPTURE AND EXPLANATION OF CV2.WAITKEY-----------
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elif st.session_state.page == "Video_capture_and_explanation_page":
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st.header("π₯ Video Capture with `cv2.VideoCapture()`")
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st.markdown("""
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- **Graphic Design & Art**: Creating creative visual content for marketing and design.
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""")
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st.code("""
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from PIL import Image
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import numpy as np
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import matplotlib.pyplot as plt
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plt.show()
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""", language='python')
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st.header("Color Spaces in Machine Learning")
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st.markdown("""
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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.
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Common Color Spaces:
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- **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.
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elif st.session_state.page == "operations_using_opencv":
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# Header and description for cv2.imread
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st.header("ποΈ Reading an Image with cv2.imread()")
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st.markdown("""
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**`cv2.imread()` - Read an Image**
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**Purpose:** Load an image from a file and convert it to a NumPy array.
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**Syntax:**
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""")
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# Header and description for cv2.imshow
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st.header("πΌοΈ Displaying an Image with cv2.imshow()")
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st.markdown("""
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**`cv2.imshow()` - Display an Image**
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**Purpose:** Show an image in a window.
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**Syntax:**
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""")
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# Header and description for cv2.imwrite
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st.header("πΎ Saving an Image with cv2.imwrite()")
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st.markdown("""
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**`cv2.imwrite()` - Write/Save an Image**
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**Purpose:** Save an image to a file.
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**Syntax:**
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elif st.session_state.page == "operations_using_opencv":
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# Header for Image Conversion
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st.header("π Converting Images Between Different Color Spaces")
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st.markdown("""
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**OpenCV supports many color spaces for image processing.**
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**Common Conversions:**
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""")
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# Header for Splitting Channels
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st.header("πΉ Splitting Color Channels in an Image")
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st.markdown("""
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**Splitting an image into its individual color channels (B, G, R) allows you to analyze or modify each channel independently.**
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**Syntax:**
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```python
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""")
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# Header for Merging Channels
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st.header("πΉ Merging Color Channels in an Image")
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st.markdown("""
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**You can merge the individual channels back into a color image using `cv2.merge()`.**
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**Syntax:**
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```python
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""")
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# Header for Combining with Modifications
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st.header("π¨ Modifying Channels Before Merging")
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st.markdown("""
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**You can modify each channel (e.g., increase brightness in the red channel) before merging them back together.**
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**Example:**
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```python
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#---------VIDEO CAPTURE AND EXPLANATION OF CV2.WAITKEY-----------
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elif st.session_state.page == "Video_capture_and_explanation_page":
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st.header("π₯ Video Capture with `cv2.VideoCapture()`")
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st.markdown("""
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