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This repository was archived by the owner on Jun 29, 2024. It is now read-only.

Advanced Level #15

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CSEdgeOff merged 5 commits into CSEdgeOfficial:main from aarthikanettyam:main
May 8, 2024
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20 changes: 20 additions & 0 deletions Aarthika Nettyam/README.md
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Advance Level

Task 1 :Image Converter:

Where the program that accepts images in multiple formats that may be in JPEG, PNG, BMP, GIF and converts them into a desired format using Python Imaging Library (PIL) that may be any of the formats mentioned.


Task 2:Data Analysis with Pandas:

In the program on loading the "Iris" dataset from Seaborn and analyzing it using Pandas and performed exploratory data analysis, cleaning, aggregation, visualizations, and correlation calculations.
The output of the program will consits of calculations , graph which will be more understanding to users.


Task 3:Linear Regression with Scikit-learn:

Completed a program by applying linear regression to predict house prices from the Boston housing dataset using scikit-learn and compared train and test scores and plot residuals.

Task 4:Image Compression:

Developed a Python program for compressing images while maintaining quality by exploring compression techniques like RLE and DCT and allowing users to adjust compression quality, support various image formats, and provide output options. Optionally, include a user interface.
36 changes: 36 additions & 0 deletions Aarthika Nettyam/Task1
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from PIL import Image
import os

def convert_image(input_path, output_path, output_format):
try:
# Open the image
with Image.open(input_path) as img:
# Convert and save the image to the desired format
img.save(output_path, format=output_format)
print(f"Image converted successfully to {output_format} format.")
except Exception as e:
print(f"An error occurred: {e}")

def main():
input_path = input("Enter the path to the input image: ")
output_format = input("Enter the desired output format (e.g., JPEG, PNG, BMP, GIF): ").upper()

# Validate output format
if output_format not in ['JPEG', 'PNG', 'BMP', 'GIF']:
print("Invalid output format. Please choose from JPEG, PNG, BMP, or GIF.")
return

# Extract the file name and extension
file_name, file_extension = os.path.splitext(input_path)

# If the input file already has an extension, remove it
file_name_without_ext = file_name.split('.')[0]

# Set the output path
output_path = f"{file_name_without_ext}_converted.{output_format.lower()}"

# Convert the image
convert_image(input_path, output_path, output_format)

if __name__ == "__main__":
main()
43 changes: 43 additions & 0 deletions Aarthika Nettyam/Task2
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import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Load the Iris dataset from Seaborn
iris = sns.load_dataset("iris")
numeric_iris = iris.drop(columns='species')

# Display the first few rows of the dataset
print("First few rows of the dataset:")
print(iris.head())

# Summary statistics
print("\nSummary statistics:")
print(iris.describe())

# Checking for missing values
print("\nMissing values:")
print(iris.isnull().sum())

# Visualizations
# Pairplot
sns.pairplot(iris, hue="species")
plt.title("Pairplot of Iris Dataset")
plt.show()

# Boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(data=iris, orient="h")
plt.title("Boxplot of Iris Dataset")
plt.show()

# Histograms
plt.figure(figsize=(10, 6))
iris.hist()
plt.suptitle("Histograms of Iris Dataset")
plt.show()

# Correlation heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(numeric_iris.corr(), annot=True, cmap="coolwarm")
plt.title("Correlation Heatmap of Iris Dataset")
plt.show()
45 changes: 45 additions & 0 deletions Aarthika Nettyam/Task3
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import pandas as pd
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]

# Load the Boston housing dataset

X = data
y = target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the linear regression model
model = LinearRegression()

# Fit the model on the training data
model.fit(X_train, y_train)

# Predict on the training and testing data
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)

# Calculate the scores
train_score = model.score(X_train, y_train)
test_score = model.score(X_test, y_test)

print("Training score:", train_score)
print("Testing score:", test_score)

# Plot residuals
plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
plt.scatter(y_test_pred, y_test_pred - y_test, c='lightgreen', marker='s', label='Testing data')
plt.xlabel('Predicted values')
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=0, xmax=50, lw=2, color='red')
plt.title('Residual plot')
plt.show()
53 changes: 53 additions & 0 deletions Aarthika Nettyam/Task4
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from PIL import Image
import os

def get_size_format(b, factor=1024, suffix="B"):
"""
Scale bytes to its proper byte format.
e.g: 1253656 => '1.20MB', 1253656678 => '1.17GB'
"""
for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
if b < factor:
return f"{b:.2f}{unit}{suffix}"
b /= factor
return f"{b:.2f}Y{suffix}"

def compress_img(image_name, new_size_ratio=0.9, quality=90, width=None, height=None, to_jpg=True):
# Load the image into memory
img = Image.open(image_name)

# Print the original image shape
print("[*] Image shape:", img.size)

# Get the original image size in bytes
image_size = os.path.getsize(image_name)
print("[*] Size before compression:", get_size_format(image_size))

if new_size_ratio < 1.0:
# If resizing ratio is below 1.0, multiply width & height with this ratio to reduce image size
img = img.resize((int(img.size[0] * new_size_ratio), int(img.size[1] * new_size_ratio)), Image.ANTIALIAS)
elif width and height:
# If width and height are set, resize with them instead
img = img.resize((width, height), Image.ANTIALIAS)

# Split the filename and extension
filename, ext = os.path.splitext(image_name)

# Make a new filename appending "_compressed" to the original file name
if to_jpg:
# Change the extension to JPEG
new_filename = f"{filename}_compressed.jpg"
else:
# Retain the same extension of the original image
new_filename = f"{filename}_compressed{ext}"

# Save the compressed image
img.save(new_filename, optimize=True, quality=quality)

# Print the new image shape
print("[+] New Image shape:", img.size)
print(f"[*] Compressed image saved as: {new_filename}")

# Example usage:
Input=input()
compress_img(Input, new_size_ratio=0.8, quality=80, width=800, height=600)

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