Top 18 Projects for Image Processing in Python to Boost Your Skills

Have you ever imagined transforming your car into a high-performance machine built to conquer both the open road and rugged terrains? Image processing in Python works similarly. It takes a raw image and, using various techniques, reshapes it into something smarter, more detailed, and highly functional.

You might be wondering why Python? Well, just like you need the right tools to customize your car, Python—the most popular programming language in the world—provides all the necessary tools to reshape images to suit your needs. 

Ready to start customizing your images? Explore these top image processing in Python projects that can help you build a career as a Python developer. Let’s dive in!

What is Image Processing and Why is it Important?

Image processing is the process of manipulating and analyzing digital images using algorithms to extract, improve, or modify information. 

Image processing can be as simple as adjusting an image's contrast or as complex as recognizing patterns or objects within the image.

Here are different techniques used in image processing using algorithms.

• Filtering: It improves or reduces certain features of the image (ex, sharpening or blurring).
• Edge detection: it involves identifying boundaries of objects or regions in the image.
• Segmentation: It divides an image into meaningful segments, such as objects, regions, or textures.
• Pattern recognition: This technique recognizes patterns or objects within the image (ex: facial features).
• Compression: This technique reduces the size of the image data for storage or transmission.

Also Read: Image Recognition in Machine Learning

After a brief overview of the concept, let’s understand how image processing in Python works. 

Top 18 Image Processing In Python Projects

Python’s libraries (such as NumPy and OpenCV) have made it a popular language for building image processing applications. 

Whether you're a beginner learning to manipulate pixels or an advanced user developing a facial recognition system, Python offers the tools and frameworks to create powerful image processing solutions.

Here’s an overview of the top image processing in Python projects.

Now, let’s explore all the image processing projects in Python in detail.

Beginner-Level Projects For Image Processing in Python

The beginner-level projects for image processing in Python are ideal for those just starting to explore the field. These projects will introduce you to the foundational concepts of image manipulation, enhancement, and analysis.

Here are the different image processing in Python projects for beginners.

1. Grayscale Image Converter

This project converts colored images into grayscale images. It will help you understand how colors are represented in an image and how to manipulate pixel values.

The project takes an input image, processes each pixel to convert its RGB values into a single intensity value, and then saves the grayscale image.

Key features:

• Converts any color image to grayscale using simple algorithms.
• Allows saving the processed image in multiple formats (JPEG, PNG).
• Supports batch processing for multiple images.

Tools and technologies:

• Pillow for image manipulation
• NumPy array
• OpenCV for image processing

Challenges:

• Ensuring proper image size and aspect ratio are maintained.
• Understanding matrix manipulations.
• Dealing with different file formats.
• Optimizing the algorithm for faster processing.

Applications:

• Used in preprocessing for computer vision tasks.
• Medical imaging (ex: MRI scans).
• Image compression algorithms.

2. Image Resizing and Cropping Tool

The project involves resizing images to the desired dimensions. It involves adjusting the dimensions of an image without distorting its aspect ratio.

You can input the desired dimensions for resizing or specify a rectangular region to crop. The tool will use the nearest neighbor algorithm for resizing and simple coordinate manipulation for cropping.

Key features:

• Resizing images based on use inputs.
• Can handle multiple file formats.
• Aspect ratio preservation. 

Tools and technologies:

• Tkinter GUI library
• OpenCV image processing library
• NumPy for array handling

Challenges:

• Ensuring image quality is maintained after resizing.
• Managing aspect ratios and avoiding image distortion.
• Handling edge cases 
• Addressing memory usage

Applications:

• Optimizing social media images
• Web design
• Photography editing tools

Also Read: Python Tkinter Projects

3. Basic Image Filters (Blur, Sharpen)

The system uses simple filters like blur and sharpen for images. The blur filter smooths the image, while the sharpening filter increases the contrast of edges.

The project uses convolutional filters to perform mathematical operations on the image pixels and their neighbors.

Key features:

• Allows real-time preview of changes
• Works for both small and large images
• User-friendly interface

Tools and technologies:

• SciPy  for image filtering
• OpenCV for image processing
• Pillow for image manipulation

Challenges:

• Optimizing performance for processing large images.
• Balancing filter strength to avoid over-sharpening or over-blurring.
• Implementing filters without significant lag.

Applications:

• Photo editing software.
• Image preprocessing for computer vision tasks.
• Object detection by enhancing features.

4. Edge Detection using Canny

The system identifies points in an image where the brightness changes sharply. The tool uses the Canny Edge Detector, one of the most popular edge detection algorithms.

The algorithm uses a Gaussian filter to smoothen the image, followed by gradient calculation, non-maximum suppression, and edge tracing by hysteresis.

Key features:

• Allows setting parameters like threshold values.
• Outputs both binary edge map and the original image.
• Provides real-time visual feedback to users

Tools and technologies:

• Matplotlib to visualize edges
• OpenCV for edge detection
• NumPy for array handling

Challenges:

• Handling noisy images.
• Understanding the multi-step Canny algorithm.
• Performance issues with large images.

Applications:

• Object recognition and segmentation.
• Detecting anomalies in CT scans.
• Computer vision in robotics and navigation systems.

Source code:

import cv2
import numpy as np

def canny_edge_detection(image_path):
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    edges = cv2.Canny(img, 100, 200)
    cv2.imwrite("edges_detected.jpg", edges)
    cv2.imshow("Edges", edges)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Example usage
canny_edge_detection("input_image.jpg")

5. Color Detection in Images

The system can detect and isolate specific colors in an image. It's useful in applications where identifying certain colors is necessary for object detection or analysis.

The algorithm isolates the image from the RGB color space to the HSV color space, and then applies a mask to isolate the desired color range.

Key features:

• Provides real-time feedback on color range selection.
• Allows extraction of colored regions or entire objects based on color.
• Easily customizable for various color ranges

Tools and technologies:

• Matplotlib for visualization 
• OpenCV for image processing 
• NumPy for array handling

Challenges:

• Handling varying lighting conditions in the image.
• Ensuring the color mask accurately isolates the target color.
• Managing color variations and image noise.

Applications:

• Object tracking in robotics.
• Color-based object recognition in manufacturing.
• Self-driving cars for recognizing traffic lights

Source code:

import cv2
import numpy as np

def detect_color(image_path, lower_color_range, upper_color_range):
    img = cv2.imread(image_path)
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # Creating a mask for the specified color range
    mask = cv2.inRange(hsv, lower_color_range, upper_color_range)
    
    # Mask the image to highlight the color
    result = cv2.bitwise_and(img, img, mask=mask)
    
    cv2.imshow("Original Image", img)
    cv2.imshow("Detected Color", result)
    
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Define color ranges for red (example)
lower_red = np.array([0, 120, 70])
upper_red = np.array([10, 255, 255])

# Test the function
detect_color("input_image.jpg", lower_red, upper_red)

Also Read: Top 10 Exciting OpenCV Project Ideas & Topics for Freshers & Experienced

6. Emotion Detection from Facial Expressions

The emotion detection system processes facial features and classifies human emotions (such as happiness or anger) based on the features.

Emotions are detected using a deep learning model (such as FER-2013). Once detected, facial features are extracted, and the emotion is classified.

Key features:

• Real-time emotion detection
• Display emotion labels in live video
• Integrate with the camera system

Tools and technologies:

• TensorFlow for training deep learning models
• Haar Cascade Classifier
• CNN deep learning model

Challenges:

• The accuracy of emotion classification can vary
• Handling facial features of different ethnicities
• High false positive rate

Applications:

• Mental health monitoring
• Security systems
• Sentiment analysis

Source code:

import cv2
from keras.models import load_model
import numpy as np

# Load pre-trained model for emotion detection
model = load_model('emotion_model.h5')

# Define emotion labels
emotion_labels = ['Anger', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']

def detect_emotion(image_path):
    # Load the image
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # Load Haar cascade for face detection
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    for (x, y, w, h) in faces:
        # Crop and resize face
        face = gray[y:y+h, x:x+w]
        face = cv2.resize(face, (48, 48))
        face = np.expand_dims(face, axis=0)
        face = np.expand_dims(face, axis=-1)
        face = face / 255.0
        
        # Predict emotion
        prediction = model.predict(face)
        max_index = np.argmax(prediction[0])
        emotion = emotion_labels[max_index]
        
        # Draw bounding box and label
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
        cv2.putText(img, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
    cv2.imshow("Emotion Detection", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Test the function
detect_emotion('face_image.jpg')

7. Image Super-Resolution

The system enhances the resolution of an image, turning a low-resolution image into a high-resolution one. 

The model used in the project is trained on a large dataset of high- and low-resolution image pairs. The training allows it to predict the missing high-frequency details in the low-resolution image.

Key features:

• Use of deep learning models like SRCNN.
• Real-time image super-resolution.
• Option to save the upscaled image in various formats.

Tools and technologies:

• Keras for training super-resolution model
• OpenCV for image processing
• NumPy for array handling

Challenges:

• Training deep learning models requires a large amount of computational resources.
• Balancing quality and speed during the process.
• The model may struggle with certain types of images. 

Applications:

• Enhancing CT scans for clearer diagnostics.
• Satellite imagery for better analysis of remote areas.
• Restoring old, low-quality photographs.

Source code:

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model

# Load the pre-trained model for image super-resolution (e.g., ESRGAN or SRCNN)
model = load_model('super_resolution_model.h5')

def super_resolve_image(image_path):
    # Load the image
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # Normalize the image
    img = np.expand_dims(img, axis=0) / 255.0

    # Predict the high-resolution image
    high_res_img = model.predict(img)

    # Convert the image back to BGR format and show the results
    high_res_img = np.squeeze(high_res_img) * 255.0
    high_res_img = np.clip(high_res_img, 0, 255).astype(np.uint8)
    
    # Display the result
    cv2.imshow("Low Resolution", img[0])
    cv2.imshow("Super-Resolved", high_res_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Test the function
super_resolve_image('low_res_image.jpg')

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After exploring image processing in Python projects for beginners, let’s look at some intermediate level projects.

Intermediate-Level Projects For Image Processing in Python

Intermediate level image processing projects in Python will deepen your understanding of object detection, image processing, and real-time applications. You’ll need knowledge of libraries such as Pillow and OpenCV, as well as machine learning frameworks like TensorFlow or PyTorch.

Here are the top image processing in Python projects for intermediate learners.

8. Barcode and QR Code Scanner

The scanner project allows you to read and decode barcodes and QR codes from images. It is especially useful for applications like inventory management.

The system can take an image or video stream, detect the barcodes or QR codes, and decode the information embedded in them.

Key features:

• Can detect QR codes and barcodes from images or video
• Handles various image qualities
• Supports multiple barcode formats like Code 128

Tools and technologies:

• pyzbar for QR and barcode decoding
• OpenCV for image processing
• NumPy for array handling 

Challenges:

• Accurate detection in low-resolution images
• Handling tilted barcodes
• Handling damaged barcodes or QR codes

Applications:

• Access control systems like passes
• Inventory management in supermarkets
• Contactless payment using QR codes

Source code:

import cv2
from pyzbar.pyzbar import decode

def scan_barcode(image_path):
    img = cv2.imread(image_path)
    decoded_objects = decode(img)
    
    for obj in decoded_objects:
        # Draw a rectangle around detected barcode/QR code
        points = obj.polygon
        if len(points) == 4:
            pts = points
        else:
            pts = cv2.convexHull(np.array([point for point in points], dtype=np.float32))

        cv2.polylines(img, [np.int32(pts)], True, (0, 0, 255), 3)
        
        # Decode and display the information
        barcode_data = obj.data.decode('utf-8')
        barcode_type = obj.type
        cv2.putText(img, f"{barcode_data} ({barcode_type})", (obj.rect[0], obj.rect[1]-10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    cv2.imshow("Barcode Scanner", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Test the function
scan_barcode('barcode_image.png')

Also Read: Keras vs. PyTorch

9. Face Detection with OpenCV

The system uses OpenCV to locate faces in an image or real-time video stream and mark the detected faces with bounding boxes.

The tool highlights the detected faces with bounding boxes, and their positions are saved for further processing.

Key features:

• Real-time face detection from webcam
• Using Haar Cascade Classifiers for accurate face detection
• Ability to detect multiple faces in a single frame

Tools and technologies:

• TensorFlow for face detection
• Haar Cascade for face detection
• NumPy for array handling

Challenges:

• Performance in real-time applications
• Handling partially-visible faces
• Accuracy in varying lighting conditions

Applications:

• Gesture recognition during human-computer interaction
• Surveillance security systems
• Tagging friends on social media

Source code:

import cv2

def detect_faces(image_path):
    # Load the pre-trained Haar cascade classifier
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # Detect faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Draw bounding boxes around faces
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

    cv2.imshow("Detected Faces", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Test the function
detect_faces('face_image.jpg')

10. Handwritten Digit Recognition using MNIST

The project recognizes digits (0-9) from images. It trains the model using the MNIST dataset, which contains thousands of labeled handwritten digits.

The MNIST dataset is preprocessed and then used to train the CNN model to predict the digit on new, unseen images.

Key features:

• Training a CNN model on the MNIST dataset.
• Real-time digit recognition from handwritten input.
• Evaluating model accuracy on test data.
• Visualizing the model performance.

Tools and technologies:

• TensorFlow for neural networks-based model
• MNIST Dataset 
• Matplotlib for data visualization

Challenges:

• Overfitting due to limited data.
• Achieving high accuracy with minimal pre-processing.
• Fine-tuning the model parameters for optimal performance

Applications:

• Optical Character Recognition (OCR) systems.
• Postal sorting systems
• Banking, for reading handwritten cheques.

Source code:

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist

# Load the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Preprocess the data
train_images = train_images.reshape((train_images.shape[0], 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((test_images.shape[0], 28, 28, 1)).astype('float32') / 255

# Build a CNN model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Compile and train the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

# Evaluate on test data
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test Accuracy: {test_acc}")

11. Background Removal

The tool is capable of separating the foreground (usually a person or object) from the background in an image.

The project uses techniques like image segmentation or edge detection to differentiate the foreground from the background.

Key features:

• Remove background from static images or objects.
• Handle images with varied lighting and backgrounds.
• Replace or blur the background.

Tools and technologies:

• Deep Learning (CNNs)
• TensorFlow for segmentation models
• OpenCV for image processing 

Challenges:

• Handling dynamic backgrounds.
• Accuracy in separating foreground and background.
• Maintaining image quality.

Applications:

• Social media filters to blur the background.
• Video conferencing tools for background replacement.
• Video production for visual effects.

Source code:

import cv2
import numpy as np

def remove_background(image_path):
    # Read image
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Thresholding to create a mask
    _, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)
    mask = cv2.bitwise_not(thresh)

    # Mask out the background
    result = cv2.bitwise_and(img, img, mask=mask)

    cv2.imshow('Original', img)
    cv2.imshow('Result', result)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Test the function
remove_background('image_with_background.jpg')

12. Image Stitching (Panorama Creation)

The tool can combine multiple images to create a larger image or a panorama. It can create wide-angle photos that cannot be captured in a single shot.

The project takes a series of overlapping images and uses feature detection algorithms like SIFT to match keypoints between images. The images are then blended to create a seamless panorama.

Key features:

• Multi-image stitching
• Blending images smoothly
• Handling lighting and exposure

Tools and technologies:

• OpenCV  for feature detection
• Scikit-image for image stitching
• NumPy for array handling

Challenges:

• Distortion while handling images with perspective differences
• Accuracy in feature matching
• Color inconsistencies 

Applications:

• Geospatial applications like satellite imagery
• Augmented reality
• Surveillance systems

Source code:

import cv2
import numpy as np

def stitch_images(image_paths):
    # Read all images
    images = [cv2.imread(image) for image in image_paths]

    # Initialize OpenCV stitcher
    stitcher = cv2.createStitcher()
    status, stitched_image = stitcher.stitch(images)
    
    if status == cv2.Stitcher_OK:
        cv2.imshow('Stitched Image', stitched_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    else:
        print("Error during stitching")

# Test the function with multiple images
stitch_images(['image1.jpg', 'image2.jpg', 'image3.jpg'])

13. License Plate Recognition

License Plate Recognition (LPR) tool can detect and read vehicle license plates from images or video streams.

You can use tools like YOLO to detect the license plate and then apply OCR to extract the text from the plate.

Key features:

• Handle different plate formats
• Real-time recognition from live video
• Handle varied lighting

Tools and technologies:

• OpenCV for image detection
• Tesseract  for OCR
• TensorFlow for deep learning

Challenges:

• Noise in the image
• Character processing
• Real-time processing of image or video

Applications:

• Toll collection
• Traffic surveillance
• Vehicle tracking

Source code:

import cv2
import pytesseract
from matplotlib import pyplot as plt

# Load the image
image = cv2.imread('vehicle_image.jpg')

# Convert the image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply GaussianBlur to reduce noise
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)

# Use edge detection (Canny)
edges = cv2.Canny(blurred_image, 30, 150)

# Find contours which may represent license plate
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Filter contours to find license plate region
for contour in contours:
    epsilon = 0.04 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)

    # If the contour has 4 points, it may be a license plate
    if len(approx) == 4:
        x, y, w, h = cv2.boundingRect(approx)
        license_plate = image[y:y+h, x:x+w]

        # Use OCR to read the text on the plate
        plate_text = pytesseract.image_to_string(license_plate, config='--psm 8')
        print(f"License Plate: {plate_text.strip()}")

        # Draw bounding box around the license plate
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, plate_text.strip(), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

# Display the output image
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()

Advanced-Level Projects For Image Processing in Python

If you’re familiar with complex image processing and machine learning concepts, you can use advanced image processing in Python projects involving AI, deep learning, and working with large datasets.

Here are the top image processing in Python projects for advanced learners.

14. Object Detection with YOLO

YOLO is a popular real-time object detection system that identifies multiple objects in a single image or video frame. It's well-known for its speed and accuracy in detecting objects.

YOLO uses a single neural network to predict both the class and the bounding box coordinates of objects. It outputs boxes with associated class labels and confidence scores for each detected object.

Key features:

• Identify and localize multiple objects in real-time
• Predict the precise location of each object 
• Faster image processing compared to traditional models

Tools technologies:

• YOLO object detection model
• TensorFlow for fine-tuning YOLO
• OpenCV for frame processing

Challenges:

• Struggles to detect smaller objects
• Fine-tuning the model for higher accuracy
• Real-time image processing

Applications:

• Use in autonomous vehicles to detect obstacles
• Automatic checkout systems in retail stores
• To track suspicious objects in security surveillance

Source code:

import cv2
import numpy as np

# Load YOLO model
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load image
img = cv2.imread('test_image.jpg')
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# Process output and draw bounding boxes
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x, center_y, w, h = (detection[0:4] * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])).astype('int')
            cv2.rectangle(img, (center_x, center_y), (center_x + w, center_y + h), (255, 0, 0), 2)

cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

15. Image Segmentation with U-Net

The system uses a deep learning model called U-Net for image segmentation. It is widely used for medical image analysis, where the objective is to segment specific regions from an image.

The U-Net model is trained on labeled images, where each pixel is classified into categories (e.g., foreground or background). The result is a segmented image in which the target areas are highlighted.

Key features:

• Works well even with a small dataset
• Skipping connection helps to retain high-resolution features
• Symmetric encoder-decoder architecture

Tools technologies:

• U-Net Model for segmentation tasks
• TensorFlow for training U-Net model
• OpenCV for image processing

Challenges:

• High computational resources needed
• Selecting the optimal hyperparameters for tuning
• Ensuring the model generalizes

Applications:

• Used in medical image analysis to segment tumors 
• Used in satellite imaging to detect changes
• Used in agriculture to detect diseases in crops

Source code:

from keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import cv2

# Load a pre-trained U-Net model
model = load_model('unet_model.h5')

# Load and preprocess image
img = cv2.imread('test_image.jpg')
img = cv2.resize(img, (256, 256))
img = np.expand_dims(img, axis=0) / 255.0

# Predict segmentation mask
pred = model.predict(img)

# Visualize the result
pred_mask = pred[0] > 0.5  # Binarize output for segmentation
plt.imshow(pred_mask)
plt.show()

16. Real-Time Face Mask Detection

The system can check whether a person is wearing a face mask or not in real-time using deep learning models.

The model detects faces in a webcam video and classifies each face as wearing a mask or not. It then shows the result on the video feed with labels and bounding boxes.

Key features:

• Real-time face mask detection
• Real-time feedback of the test
• Detect multiple faces in the video feed

Tools technologies:

• OpenCV for webcam input 
• Haar Cascade Classifier for face detection
• TensorFlow for training mask detection

Challenges:

• The type of mask may affect detection accuracy
• Obstruction may affect face detection
• Reducing latency process for live video feeds

Applications:

• Monitoring Compliance in workplace
• Mask detection in secure facilities
• Including mask detection in surveillance systems

Source code:

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# Load the pre-trained mask detection model
model = load_model('mask_detector_model.h5')  # Load your own pre-trained model

# Load the Haar Cascade face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Start the webcam feed
cap = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame from the webcam
    ret, frame = cap.read()

    # Convert the image to grayscale (for face detection)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale image
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Loop over all detected faces
    for (x, y, w, h) in faces:
        # Draw a rectangle around the face
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # Extract the region of interest (ROI) - the face area
        face = frame[y:y + h, x:x + w]
        face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
        face = cv2.resize(face, (224, 224))  # Resize the image to the input size for the model
        face = np.expand_dims(face, axis=0)  # Add batch dimension
        face = face / 255.0  # Normalize the image

        # Predict the mask status (0: No Mask, 1: Mask)
        prediction = model.predict(face)
        label = "Mask" if prediction[0][0] > 0.5 else "No Mask"

        # Set label text and color based on prediction
        color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
        cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)

    # Display the resulting frame
    cv2.imshow('Real-Time Face Mask Detection', frame)

    # Break the loop when the user presses 'q'
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture and close the window
cap.release()
cv2.destroyAllWindows()

17. Style Transfer using GANs

Style transfer is a deep learning technique where the style of one image is applied to the content of another image. The project generates new images by combining the stylistic elements of one image with the content of another.

The project uses a generative adversarial network (GAN) to learn the content and style from two input images. It combines the content image with the style image, producing a stylized image that keeps the content's structure but adopts the style’s aesthetics.

Key features:

• You can choose the amount of style to apply.
• Transforms normal image into an artistic image
• Generate high-quality results using GANs

Tools technologies:

• TensorFlow/Keras for GAN models 
• PyTorch for training GAN
• NumPy for array handling

Challenges:

• GANs are difficult to train
• It is computationally expensive to train the model
• Balancing style and maintaining the originality of image is difficult

Applications:

• Social media filters
• Virtual reality applications
• Movies and animation

Source code:

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg19 import VGG19, preprocess_input

# Load images
content_image = image.load_img('content.jpg', target_size=(256, 256))
style_image = image.load_img('style.jpg', target_size=(256, 256))

# Preprocess images
content_array = preprocess_input(image.img_to_array(content_image)[None, :, :, :])
style_array = preprocess_input(image.img_to_array(style_image)[None, :, :, :])

# Load VGG19 model (pre-trained)
vgg_model = VGG19(include_top=False, weights='imagenet')

18. Optical Character Recognition (OCR)

OCR technology converts different types of documents into editable and searchable data. The project uses deep learning models to extract text from images.

Preprocessing techniques, such as image thresholding, are applied to enhance text visibility. After processing, the detected text is returned as editable content.

Key features:

• Convert printed or handwritten text from images into machine-readable text.
• Can recognize different languages and fonts.
• Can detect and extract text in real-time.

Tools technologies:

• OpenCV for pre-processing images 
• Tesseract OCR Engine
• Pillow for image manipulation

Challenges:

• Poor image quality can affect accuracy
• A model can struggle to recognize irregular handwriting
• Time-consuming to make the model recognize different languages

Applications:

• Automated data entry
• Digitizing old books
• Text-to-speech recognition technology

Source code:

import pytesseract
import cv2

# Load image
img = cv2.imread('text_image.jpg')

# Convert image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply thresholding for better accuracy
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Use pytesseract to extract text
text = pytesseract.image_to_string(thresh)

print("Detected text:", text)

What Tools Should You Know for Image Processing in Python Libraries?

The evolution of robust libraries and tools allows developers to perform complex image processing tasks. Whether you're working with image resizing or text recognition, several specialized libraries are tailored to different use cases. 

Here are some of the most popular libraries for image processing in Python.

OpenCV

OpenCV provides a wide range of tools for both basic and advanced image processing, including real-time computer vision tasks. 

Features:

• Performs operations like resizing, rotating, and cropping images
• Edge detection in images
• Face detection and feature matching
• Works efficiently with real-time video feeds.

Common use cases:

• Face detection in images and videos
• Object tracking in video streams
• Tasks like sharpening and blurring

Pillow (PIL)

Pillow is a user-friendly library for simple image manipulation tasks. You can use it for tasks that don't require real-time processing due to its ability to open, manipulate, and save many image formats.

Features:

• Opening and saving images in formats like JPG
• Perform basic transformations like flip
• Adding shapes on the images

Common use cases:

• Image resizing for thumbnails
• Watermarking images
• Automated image manipulation tasks

Scikit-image

Built on top of SciPy, scikit-image is used in scientific and research applications. Its algorithms are suitable for image processing tasks like edge detection, filtering, and segmentation.

Features:

• Can reduce noises in images
• Can perform geometric transformations like rotations
• Can divide images into meaningful segments

Common use cases:

• It is used to segment  MRI or CT scan images
• Used to extract features from microscope images
• Can process satellite images for object detection

TensorFlow and Keras

TensorFlow is an open-source framework for machine learning, and Keras is its high-level API for building and training deep learning models. 

Together, they can build deep learning models that can perform tasks like image classification, object detection, and image generation.

Features:

• Can augment training images to improve model generalization
• It can fine-tune models on new tasks with less data
• Speeds up model training and inference using GPUs

Common use cases:

• Can classify images into predefined categories
• Identifies and locates objects within an image
• Divide the image into multiple parts based on its content

After learning how to use popular tools for image processing in Python tools, let's share your projects with the world.

How to Showcase Your Image Processing Projects?

Showcasing your skills as a Python developer provides visibility to potential employers and can lead to new opportunities. How you present your projects is crucial, whether you're looking to secure a job, collaborate on open-source projects, or share your work with a broader community.

Here‘s how you can showcase your image processing in Python projects.

Hosting Projects on GitHub

GitHub is a popular platform for storing and sharing code. By hosting your image processing projects on GitHub, you can gain access to version control and showcase your work to the world.

Here are the tips on writing READMEs and adding project demos.

• Write a README file that is clear, informative, and easy to understand. The file must contain the project description, features, execution, and methods to contribute.
• Use images, screen recordings, or Jupyter Notebook images to demonstrate how the project works.
• Properly document your code so that others can understand it. This will make it easier for users to follow and modify the code.

Building a Portfolio Website

A personal portfolio website allows you to present your image-processing projects in a more user-friendly manner. A portfolio website can showcase your skills and projects to a wider audience.

Here’s how you can use a portfolio website to showcase your projects.

• GitHub Pages offers a free way to host a simple static website. You can create a personal website that displays your projects. 
• For more customization, use website builders like WordPress, Wix, or Squarespace. You can create professional-looking portfolios without using advanced web programming.
• For a better experience, consider embedding interactive elements like Jupyter notebooks on your portfolio site to allow visitors to try out your image processing algorithms in real-time.

Contributing to Open Source

You can also contribute to open source communities. This will help you collaborate with others, gain practical knowledge, and build a reputation within the community. Contributing to these communities is a good way to showcase your skills.

Here’s how contributing to the open source community will be beneficial.

• It opens up opportunities for you to collaborate with experienced developers and data scientists.
• Open source contributions will improve your problem-solving skills, debugging, and handling larger codebases.
• Regular contributions will improve your visibility in the developer and research communities.
• You can get an opportunity to work on advanced projects in the future and learn from experts in the field.

Here’s how to get involved in the open source community

• Get familiar with contribution guidelines
• Begin with small modifications like addressing bugs
• Participate in discussions
• Interact with experts to resolve your doubts

Want to build your own website? Join upGrad’s full-stack development bootcamp to learn web development skills.

After learning about image processing in Python projects, here’s how you can build a career using Python.

How Can upGrad Help You?

Python provides the necessary tools and resources needed to tackle any image processing project. It’s this versatility that makes Python a must-know language for anyone looking to work with visual data.

If you want to learn Python, upGrad can help you. upGrad’s Python courses are tailored to provide hands-on learning that ensures that you gain the expertise to implement concepts in real-world scenarios.

Here are some of the Python courses offered by upGrad.

• Learn Basic Python Programming
• Programming with Python
• Learn Python Libraries: NumPy, Matplotlib, and Pandas

Do you need help deciding which course to take to advance your career as a Python developer? Contact upGrad for personalized counseling and valuable insights.

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