pages/The Image Augumentation.py

import streamlit as st
import numpy as np
import cv2

# Streamlit header
st.markdown('<h1><center>Image Augumentation</center></h1>', unsafe_allow_html=True)
st.write("""Image Augumentation used to manipulate and alter images by applying operations such as translation, rotation, scaling, and shearing. 
These Image Augumentations help to generate new images from the existing ones, thereby increasing the dataset diversity.""")
# Image augmentation functions
def rotate_image(image, angle):
    (h, w) = image.shape[:2]  # Get image dimensions
    center = (w // 2, h // 2)  # Define the center of the image
    M = cv2.getRotationMatrix2D(center, angle, 1.0)  # Rotation matrix
    rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REFLECT)  # Apply rotation
    return rotated

def shifting(image, tx, ty):
    t_matrix = np.array([[1, 0, tx],
                          [0, 1, ty]], dtype=np.float32)
    t_img = cv2.warpAffine(image, t_matrix, (image.shape[1], image.shape[0]))  # Adjust size to original
    return t_img

def scaling(image,fx,fy):
    scaled_image=cv2.resize(image,None,fx=fx, fy=fy)
    return scaled_image

def shearing(image,shx,shy,tsx,tsy):
    shearing_matrix = np.array([[1,shx,tsx],[shy,1,tsy]],dtype= np.float32)
    sheared_image=cv2.warpAffine(image,shearing_matrix,(image.shape[1], image.shape[0]))
    return sheared_image

def cropping(image,x1,x2,y1,y2):
    cropped_image=image[x1:x2,y1:y2]
    return cropped_image
    
    

# Streamlit app
st.markdown("<b>Upload an image and apply various augmentation techniques.</b>", unsafe_allow_html=True)

# File uploader for images
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    # Read the image using OpenCV directly (ensure it's in BGR format)
    image_bgr = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    image_bgr = cv2.imdecode(image_bgr, cv2.IMREAD_COLOR)  # Read as BGR image

    # Display the uploaded image (converted to RGB for Streamlit)
    image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)  # Convert to RGB for display
    st.image(image_rgb, caption="Uploaded Image", use_container_width=True)

    st.subheader('Rotation')

    # Rotation slider
    rotate = st.slider("Rotate Angle", 0, 180, 45)

    # Apply rotation based on the slider value
    if rotate != 0:
        # Apply rotation
        rotated_image_bgr = rotate_image(image_bgr, rotate)

        # Convert back to RGB for Streamlit
        rotated_image_rgb = cv2.cvtColor(rotated_image_bgr, cv2.COLOR_BGR2RGB)
        
        # Display the rotated image
        st.image(rotated_image_rgb, caption=f"Rotated Image ({rotate}°)", use_container_width=True)
    else:
        st.write("No rotation applied.")

    st.subheader('Translation')
    # Shifting sliders
    Tx = st.slider('tx distance', -100, 100, 0)  # Allow both positive and negative values
    Ty = st.slider('ty distance', -100, 100, 0)

    # Apply shifting based on the sliders
    if Tx != 0 or Ty != 0:
        shifted_image = shifting(image_bgr, Tx, Ty)
        shifted_image_rgb = cv2.cvtColor(shifted_image, cv2.COLOR_BGR2RGB)
        st.image(shifted_image_rgb, caption=f"Translated Image (Tx: {Tx}, Ty: {Ty})", use_container_width=True)
    else:
        st.write("No Translation applied.")

    st.subheader('Scaling')
    
    #scaling
    fx=st.slider('fx distance', 0.5,1.5, 0.0,)
    fy=st.slider('fy distance', 0.5, 1.5, 0.0)
    # apply scaling based on sliders
    if fx!=0 or fy!=0:
        scaling_image = scaling(image_bgr,fx,fy)
        scaling_image_rgb=cv2.cvtColor(scaling_image, cv2.COLOR_BGR2RGB)
        st.image(scaling_image_rgb, caption=f'Scaled Image (fx:{fx},fy:{fy})',use_container_width=True)
    else:
        st.write('No scaling Applied')


    st.subheader('Shearing')
    
    # Shearing sliders
    shx = st.slider('Shearing on x-axis', 0.0, 3.0, 0.0, key='slider_shx')
    shy = st.slider('Shearing on y-axis', 0.0, 3.0, 0.0, key='slider_shy')
    tsx = st.slider('Translation on x-axis (tx)', -100, 100, 0, key='slider_tsx')
    tsy = st.slider('Translation on y-axis (ty)', -100, 100, 0, key='slider_tsy')
    
    # Apply shearing on the image
    if shx != 0 or shy != 0:
        # Assuming 'shearing' is a function that applies the shear transformation
        shearing_image = shearing(image_bgr, shx, shy, tsx, tsy)
        
        # Convert the sheared image to RGB for display in Streamlit
        shearing_image_rgb = cv2.cvtColor(shearing_image, cv2.COLOR_BGR2RGB)
        
        # Display the sheared image
        st.image(
            shearing_image_rgb,
            caption=f'Sheared image (sx: {shx}, sy: {shy})',
            use_container_width=True
        )
    else:
        st.write("No Shearing applied.")

    st.subheader('Cropping')
    
    # Cropping sliders
    x1 = st.slider('x1 value', 0, 1000, 0, key='slider_x1')
    x2 = st.slider('x2 value', 0, 1000, 0, key='slider_x2')
    y1 = st.slider('y1 value', 0, 1000, 0, key='slider_y1')
    y2 = st.slider('y2 value', 0, 1000, 0, key='slider_y2')
    
    if x1 != 0 or x2 != 0 or y1 != 0 or y2 != 0:
        cropping_image = cropping(image_bgr, x1, x2, y1, y2)
        cropping_img_rgb = cv2.cvtColor(cropping_image, cv2.COLOR_BGR2RGB)
        # Display the cropped image with proper caption formatting
        st.image(cropping_img_rgb, caption=f'cropped image, image[{x1}:{x2}, {y1}:{y2}]', use_container_width=True)
    else:
        st.write('No Cropping Applied')