This document outlines Prajwal Deshmukh's practical assignment to implement and deploy four image transformation algorithms using affine transformations. It includes an overview of translation, rotation, scaling, and shearing transformations using OpenCV in Python. The overall model design builds a Streamlit web app that allows users to upload an image and apply each transformation type, with adjustable parameters, displaying the original and transformed images. The deployed app is accessible at the provided URL.

1. Name: Prajwal Deshmukh
Roll no: 237
Div: B
PRN: 0120200484
Practical Assignment 3: Design and develop the program to implement any
four Image transformation (Affine transformation) and deploy on cloud.
1) Image Transformation Algorithms: Implement the four affine transformation
algorithms, which include translation, rotation, scaling, and shearing. You can use
Python and OpenCV to perform these transformations. Here's a brief overview of
each:
a. Translation: translation_matrix = np.float32([[1, 0, tx], [0, 1, ty]]) # tx and ty are
translation values translated_image = cv2.warpAffine(image, translation_matrix,
(width, height))
b. Rotation: rotation_matrix = cv2.getRotationMatrix2D((width / 2, height / 2),
angle, scale) rotated_image = cv2.warpAffine(image, rotation_matrix, (width,
height))
c. Scaling: scaling_matrix = np.float32([[sx, 0, 0], [0, sy, 0]]) # sx and sy are scaling
factors scaled_image = cv2.warpAffine(image, scaling_matrix, (width, height))
d. Shearing: shearing_matrix = np.float32([[1, shx, 0], [shy, 1, 0]) # shx and shy are
shearing values sheared_image = cv2.warpAffine(image, shearing_matrix, (width,
height))

2. 2) Overall Model Design model:
import streamlit as st
import cv2
import numpy as np
# Streamlit setup
#st.title("Image Transformations with Affine Transformations")
st.markdown(
"""
<style>
body {
background-image: linear-gradient(rgba(255, 165, 0, 0.5), rgba(0, 0, 128, 0.5));
}
</style>
""",
unsafe_allow_html=True
)
st.title("Image Transformations with Affine Transformations")
st.write("Name: Prajwal Deshmukh")
st.write("PRN: 0120200484")
# Upload an image
st.header("Upload an image")
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png",
"jpeg"])

3. if uploaded_image is not None:
image = cv2.imdecode(np.fromstring(uploaded_image.read(), np.uint8), 1)
# Display the original image
st.image(image, caption="Original Image", use_column_width=True)
# Transformation selection
st.header("Select Transformation")
transformation_type = st.selectbox(
"Choose a transformation:",
["Translation", "Rotation", "Scaling", "Shearing"]
)
if transformation_type == "Translation":
st.subheader("Translation Parameters")
tx = st.slider("X-axis Translation", -200, 200, 0)
ty = st.slider("Y-axis Translation", -200, 200, 0)
# Apply the translation transformation
translation_matrix = np.float32([[1, 0, tx], [0, 1, ty]])
transformed_image = cv2.warpAffine(image, translation_matrix,
(image.shape[1], image.shape[0]))
st.image(transformed_image, caption="Transformed Image",
use_column_width=True)
elif transformation_type == "Rotation":
st.subheader("Rotation Parameters")
angle = st.slider("Angle (degrees)", -180, 180, 0)

4. # Apply the rotation transformation
rotation_matrix = cv2.getRotationMatrix2D((image.shape[1] / 2,
image.shape[0] / 2), angle, 1)
transformed_image = cv2.warpAffine(image, rotation_matrix,
(image.shape[1], image.shape[0]))
st.image(transformed_image, caption="Transformed Image",
use_column_width=True)
elif transformation_type == "Scaling":
st.subheader("Scaling Parameters")
scale_factor = st.slider("Scale Factor", 0.1, 2.0, 1.0)
# Apply the scaling transformation
transformed_image = cv2.resize(image, None, fx=scale_factor, fy=scale_factor)
st.image(transformed_image, caption="Transformed Image",
use_column_width=True)
elif transformation_type == "Shearing":
st.subheader("Shearing Parameters")
shear_x = st.slider("X-axis Shear", -2, 2, 0)
shear_y = st.slider("Y-axis Shear", -2, 2, 0)
# Apply the shearing transformation
shear_matrix = np.float32([[1, shear_x, 0], [shear_y, 1, 0]])
transformed_image = cv2.warpAffine(image, shear_matrix, (image.shape[1],
image.shape[0]))
st.image(transformed_image, caption="Transformed Image",
use_column_width=True)

5. Output:
1. Uploading of image:
2. Translation:

6. 3. Rotation:
4. Scaling:

7. 5. Shearing:
3) Deployment link:
https://imagetransformationalgorithms-
prajwaldeshmukh.streamlit.app/