tasks.md

100 Days of Computer Vision - Tasks

Days 1-6: Working with OpenCV in a Local Environment

Day 1: Camera Image Capture

• Load an image from the camera and save it to the local directory.

Day 2: Image Loading and Resizing

• Load an image from a file and resize it to a specific dimension.

Day 3: Loading and Grayscale Conversion

• Load an image from the URL https://aibauchi.com.ng/logo.png and convert it to grayscale.

Day 4: Image Background Removal

• Load the grayscale image from Day 3 and remove the image background.

Day 5: Drawing Circle on Live Camera Feed

• Draw a circle on the live camera feed in real-time.

Day 6: Real-time Annotation on CV Camera Frame

• Annotate your username and the date on the lower-left corner of the CV camera frame and display it in real-time.

Days 7-17: Advanced OpenCV Operations on Images

Day 7: Blob Analysis by Convexity

• Perform Blob analysis using the convexity feature on an image from 'images/day6.jpg'.

Day 8: Canny Edge Detection on Live Video Feed

• Apply Canny edge detection to the live video feed from the camera.

Day 9: OpenCV Averaging

• Perform image smoothing using OpenCV averaging.

Day 10: OpenCV Median Blur

• Implement the median blur operation using OpenCV.

Day 11: OpenCV Gaussian Blur

• Apply Gaussian blur to an image using OpenCV.

Day 12: OpenCV Bilateral Filter

• Implement a bilateral filter on an image using OpenCV.

Day 13: Adaptive Thresholding for Pencil Sketch

• Utilize adaptive thresholding to create a pencil sketch effect on the live camera feed.

Day 14: Contours and Shape Detection

• Implement contour detection and shape identification on any image. Refer to OpenCV documentation for implementation details.
• Detect basic shapes like circles, rectangles, and triangles using contour properties and annotate them

Day 15: Mouse Event as a Paint Brush

• Create a program that allows the mouse to be used as a paintbrush on a frame from the camera feed. Refer to OpenCV documentation for implementation details.

Day 16: Live Face Detection

• Implement live face detection using OpenCV.
• Refer to YouTube - OpenCV Face Detection tutorial for guidance.

Day 17: Facial Recognition Implementation

• Implement facial recognition using only the opencv and face_recognition Python libraries.
• Refer to MyGreatLearning - Face Recognition tutorial for guidance.

Days 18-20: Deep Learning Model Building and Hyperparameter Tuning

Day 18: Handwritten Digit Recognition with Keras

• Load and display the first 20 digits from the Keras Handwriting MNIST dataset.
• Include demo code to load the dataset.

from keras.datasets import mnist

# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Display the first 20 images
# Add code snippet here

Day 19: Deep Learning Model Building on TensorFlow

• Build a deep learning model with the architecture (32x64x128x32x10) using TensorFlow.
• Train the model on the MNIST dataset from Day 18 and save the model.

Day 20: Model Hyperparameter Tuning for Improved Accuracy

• Load the model built on Day 19 and perform hyperparameter tuning to improve the model's accuracy. Use grid search or random search techniques to find the best hyperparameters.