import streamlit as st

if "page" not in st.session_state:
    st.session_state.page = "Home"

def navigate_to(page_name):
    st.session_state.page = page_name

if st.session_state.page == "Home":
    st.title("   :red[Image Augmentation]  ")
    st.header("What is Augmented Image")
    st.markdown(""" It is a techinque by using which we can apply a geometrical transform to create a **Augmented Image**
                    **Image Augmentation** is a technique used in computer vision and deep learning to artificially expand the size and diversity of a training dataset by applying various transformations to existing images. 
                    This helps improve the robustness and generalization ability of models, especially when the amount of labeled data is limited.""")
    st.markdown("""
    - Transformation are of two types:
    1. Affine Transformation
    2. Non-Affine Transformation.
    """)
    st.header("🔁 Why Use Image Augmentation?")
    st.markdown("""
    - **Prevents overfitting** by exposing the model to varied inputs.
    - Improves generalization to unseen data.
    - Simulates real-world variability (e.g., rotation, lighting changes, noise).
    """)
    st.header("🌀📐🔀 What is Affine Transformation")
    st.markdown(""" 
    - Preserved parallelsim between the lines.
    - Sometimes the angles are preserved.
    """)
    st.header(":red[Types Of Affine Transformation]")

    descriptions = {
        '1.Translation(Shifting)': "**Translation** : Shifts the image along x or y axis.",
        '2.Rotating': "**Rotating** : Rotates the image by a certain angle.",
        '3.Scaling': "**Scaling** : Zooming in or out.",
        '4.Cropping': "**Cropping** : Random or center cropping.",
        '5.Stretching(shearing)': "**Stretching** : Vertical shearing slants the image along the y-axis,Horizontal shearing slants the image along the x-axis."
    }

    step = st.selectbox("Select the type of affine transformation :", list(descriptions.keys()))
    st.write(descriptions[step])

    if step == '1.Translation(Shifting)':
        if st.button("Learn More About Translation"):
            navigate_to("Translation")
    if step == '2.Rotating':
        if st.button("Learn More About Rotating"):
            navigate_to("Rotating")
    if step == '3.Scaling':
        if st.button("Learn More About Scaling"):
            navigate_to("Scaling")
    if step == '4.Cropping':
        if st.button("Learn More About Cropping"):
            navigate_to("Cropping")
    if step == '5.Stretching(shearing)':
        if st.button("Learn More About Shearing"):
            navigate_to("Shearing")

elif st.session_state.page == "Translation":
    st.title("1. 🚚 Translation (Shifting)")
    st.write('''
        Definition:
        Translation moves the entire image (or object in it) left, right, up, or down without rotating or changing its size or shape.
    ''')
    st.image("Images/translationform.jpg", caption="Mathmetical Form", width=500)
    st.write('''Example:
        Moving an image 10 pixels to the right and 5 pixels down.
    ''')
    st.subheader("Code:")
    st.code("""
import cv2
import numpy as np
tx = -50
ty = -50
tm = np.array([[1, 0, tx], [0, 1, ty]], dtype=np.float32)
trans_img = cv2.warpAffine(img, tm, dsize=(500, 700))
cv2.imshow("originalimg", img)
cv2.imshow("translateimg", trans_img)
cv2.waitKey()
cv2.destroyAllWindows()""", language="python")
    st.markdown("<hr>", unsafe_allow_html=True)
    if st.button("Back to Home Page"):
        navigate_to("Home")

elif st.session_state.page == "Rotating":
    st.title("2. 🔄 Rotation")
    st.write('''
        Definition:
        Rotation turns the image around a fixed center point (usually the center of the image) by a certain angle.
    ''')
    st.image("Images/RotationForm.jpg.png", caption="Mathmetical Form", width=500)
    st.write('''Example:
        Rotating an image 45° counterclockwise.
    ''')
    st.subheader("Code:")
    st.code("""
import cv2
import numpy as np
rm = cv2.getRotationMatrix2D((img.shape[0]//2, img.shape[1]//2), 90, 1)
rimg = cv2.warpAffine(img, rm, (480, 640))
cv2.imshow("originalimg", img)
cv2.imshow("Rotationimg", rimg)
cv2.waitKey()
cv2.destroyAllWindows()""", language="python")
    st.markdown("<hr>", unsafe_allow_html=True)
    if st.button("Back to Home Page"):
        navigate_to("Home")

elif st.session_state.page == "Scaling":
    st.title("3.🔍 Scaling")
    st.write('''
        Definition:
        Scaling resizes the image, either enlarging or shrinking it, along x and/or y axes.
    ''')
    st.image("Images/ScalingForm.jpg", caption="Mathmetical Form", width=500)
    st.write('''Example:
        Zooming in by a factor of 1.2 (i.e., 120% of original size).
    ''')
    st.subheader("Code:")
    st.code("""
import cv2
import numpy as np
sx = 2
sy = 2
tx = 50
ty = 50
sm = np.array([[sx, 0, tx], [0, sy, ty]], dtype=np.float32)
img1 = cv2.warpAffine(img, sm, (2*480, 2*640))
cv2.imshow("originalimg", img)
cv2.imshow("Scaling", img1)
cv2.waitKey()
cv2.destroyAllWindows()""", language="python")
    st.markdown("<hr>", unsafe_allow_html=True)
    if st.button("Back to Home Page"):
        navigate_to("Home")

elif st.session_state.page == "Cropping":
    st.title("4✂️ Cropping")
    st.write('''
        Definition:
        Cropping selects a smaller rectangular region from the original image and removes the rest.
    ''')
    st.image("Images/Croppingform.jpg", caption="Mathmetical Form", width=500)
    st.write('''Example:
        Extracting a 100×100 pixel region from the center of a 256×256 image.
    ''')
    st.subheader("Code:")
    st.code("""
import cv2
import numpy as np
img1 = img[40:450, 50:400]
cv2.imshow("originalimg", img)
cv2.imshow("Croppingimg", img1)
cv2.waitKey()
cv2.destroyAllWindows()""", language="python")
    st.markdown("<hr>", unsafe_allow_html=True)
    if st.button("Back to Home Page"):
        navigate_to("Home")

elif st.session_state.page == "Shearing":
    st.title("5. 📐Shearing (or stretching)")
    st.write('''
        Definition:
        Shearing transforms an image such that:
        - Vertical shearing slants the image along the y-axis.
        - Horizontal shearing slants the image along the x-axis.
        It changes the angles of the object but preserves parallel lines.
    ''')
    st.image("Images/Shearingform.jpg", caption="Mathmetical Form", width=500)
    st.subheader("Code:")
    st.code("""
import cv2
import numpy as np
sx = 1
sy = 1
tx = 0
ty = 0
shx = 1
shy = 0
shm = np.array([[sx, shx, tx], [shy, sy, ty]], dtype=np.float32)
img1 = cv2.warpAffine(img, shm, (480, 640))
cv2.imshow("originalimg", img)
cv2.imshow("Shearingimg", img1)
cv2.waitKey()
cv2.destroyAllWindows()""", language="python")
    st.markdown("<hr>", unsafe_allow_html=True)
    if st.button("Back to Home Page"):
        navigate_to("Home")

if st.button("Next Page"):
    st.switch_page("pages/Transforming_the_images.py")

st.markdown("<hr>", unsafe_allow_html=True)
st.markdown("<p style='text-align: center;'>🚧 Created with ❤️ using Streamlit</p>", unsafe_allow_html=True)