Here you can find several small (and slightly larger) subprojects dedicated to the well-known
Computer Vision approaches and algorithms. These subprojects are developed as part of the
Udacity Computer Vision Nanodegree Program.
Several projects are devoted to Deep Learning Arcitecture of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN):
- FashionMNIST Convolution Network
- YOLOv3 notebook implementation
- CharacterLevel RNN with LSTM
- Facial Key Points CNN (Project 1)
- Image Captioning CNN+RNN (Project 2)
Your can view and launch the following Python projects:
Fast detector of facial keypoints (ORB)
Hough Transform: Detect Lines and Circles
Binary Classifier for Day-Night Images
Edge Detection with Sobel Filters
Find contours and their directions
AdaBoost frontal face detector
K-means clustering and Masking
FashionMNIST Convolution Network
YOLOv3 notebook implementation
High-Pass and Low-Pass Filters
Project 1: Facial Key Points CNN
Project 2: Image Captioning CNN+RNN
Project 3: Graph-SLAM algorithm
- cv2.imread
- cv2.cvtColor
- cv2.ORB_create
- cv2.BFMatcher
- cv2.drawMatches
- cv2.GaussianBlur
- cv2.circle
- cv2.HoughCircles
- cv2.Canny
- cv2.HoughLinesP
- cv2.line
- cv2.inRange
- cv2.bitwise_or
- cv2.bitwise_and
- cv2.resize
- cv2.filter2D
- cv2.threshold
- cv2.findContours
- cv2.drawContours
- cv2.fitEllipse
- cv2.boundingRect
- cv2.rectangle
- cv2.CascadeClassifier
- cv2.kmeans
Make sure that Python and libraries numpy, cv2, torch, matplotlab, os, utils, darknet are installed.
The additional info about every project can be found in the README file related to the specific project.
Most of the code in this repository is based on Udacity's code.