Functions and objects help to understand concepts from computer vision and neural networks.
- conv2d(img, kernel): use Fast Fourier Transform for Convolution operation
- rgb2hsv(img): return hsv image
- rgb2gray(img, method='avg', format='rgb'): return gray image
- sobel(img, return_direction=False): return sobel edge detection
- canny(img, k=11, sigma=1, alpha=0.1, beta=0.2, return_direction=False): return canny edge detection
- make_gaussian_kernel(size, sigma): return Gaussian kernel
- dilate(img, strel): return dilated image with structure element
- erose(img, strel): return erosed image with structure element
- histeq(img): return histogram equalized image
- hough_circle_accumulator(edge_img, R_min=3, R_max=None, center_inside=True): return accumulator of circle detection using Hough transform
- hough_line_accumulator(edge_img): return accumulator of line detection using Hough transform
- connected_component_labeling(bw): return objects label and their sizes
- imfill(bw): return objects filled image
- hog_feature(img): return Histogram of Gradient feature
- harris_corner_detector(img, threshold, kernel_size=3, p=0.5): return corner indices
- initializers: return an initializer function requires shape input as type of tuple
- variance_scaling_initializer
- xavier_initializer
- zeros_initializer
- normal_initializer
- uniform_initializer
- glorot_initializer
- layers: return a layer class
- Conv2D
- MaxPooling2D
- Dense
- ReLU
- PReLU
- Sigmoid
- Tanh
- losses: function return loss and its derivative
- softmax
- mean_squared_error
- optimizers: return an optimizer class
- SGD
- SGDMomentum
- RMSProp
- Adam
- Image processing package contains functions, please check the code to understand how the related functions work.
- Neural networks package is more object oriented and contains supportive objects. Check out test_nn.py to setup training process.
This project is developed and managed during my PhD study at the UNC Charlotte in my spare time.