Uwimg

Uwimg is a C package for images processing that is callable in Python. It's an assignment of The Ancient Secrets of Computer vision CSE 455

Table Content

Installation
Basic Level Of Image Processing
Middle Level Of Image Processing
Panorama
Optical Flow
Neural Network

Installation

$git clone https://github.com/DiaaZiada/Uwimg
$cd Uwimg
$make

Original Image

Basic Level Of Image Processing

Getting and setting pixels

Getting pixel value

from uwimg import *
im = load_image("data/dog.jpg")
pixel = get_pixel(im, 0, 0, 0)
print(pixel)

=> 0.0470588244497776

Setting pixel to a value

from uwimg import *
# remove all red colors from the image
im = load_image("data/dog.jpg")
for row in range(im.h):
    for col in range(im.w):
        set_pixel(im, col, row, 0, 0)
save_image(im, "output/no_red_dog")

Copy image

from uwimg import *
im = load_image("data/dog.jpg")
im2 = copy_image(im)

Grayscale image

from uwimg import *
im = load_image("data/dog.jpg")
graybar = rgb_to_grayscale(im)
save_image(graybar, "output/gray_dog")

Shifting the image colors

from uwimg import *
im = load_image("data/dog.jpg")
shift_image(im, 0, .4)
shift_image(im, 1, .4)
shift_image(im, 2, .4)
save_image(im, "output/shifted_dog")

Clamping the image values

from uwimg import *
im = load_image("data/dog.jpg")
shift_image(im, 0, .4)
shift_image(im, 1, .4)
shift_image(im, 2, .4)
clamp_image(im)
save_image(im, "output/ligth_fixed_dog")

RGB and HSV

from uwimg import *
im = load_image("data/dog.jpg")
rgb_to_hsv(im)
shift_image(im, 1, .2)
clamp_image(im)
hsv_to_rgb(im)
save_image(im, "output/rgb_hsv_rgb_dog")

Middle Level Of Image Processing

Image resizing

Maximizing

Nearest Neighbor

from uwimg import *
im = load_image("data/dog.jpg")
a = nn_resize(im, im.w*4, im.h*4)
save_image(a, "output/4x_nn_dog")

Bilinear

from uwimg import *
im = load_image("data/dog.jpg")
a = bilinear_resize(im, im.w*4, im.h*4)
save_image(a, "output/4x_bl_dog")

Minimizing

Nearest Neighbor

from uwimg import *
im = load_image("data/dog.jpg")
a = nn_resize(im, im.w//7, im.h//7)
save_image(a, "output/7th-nn_dog")

Bilinear

from uwimg import *
im = load_image("data/dog.jpg")
a = bilinear_resize(im, im.w//7, im.h//7)
save_image(a, "output/7th-bl_dog")

Image filtering with convolutions

Blurring

from uwimg import *
im = load_image("data/dog.jpg")
f = make_box_filter(7)
blur = convolve_image(im, f, 1)
save_image(blur, "output/box7_dog")

Blur and Resizing

from uwimg import *
im = load_image("data/dog.jpg")
f = make_box_filter(7)
blur = convolve_image(im, f, 1)
thumb = nn_resize(blur, blur.w//7, blur.h//7)
save_image(thumb, "output/thumb_dog")

Gaussian

from uwimg import *
im = load_image("data/dog.jpg")
f = make_gaussian_filter(2)
blur = convolve_image(im, f, 1)
save_image(blur, "output/gauss2_dog")

Frequency and Reconstruction

from uwimg import *
im = load_image("data/dog.jpg")
f = make_gaussian_filter(2)
lfreq = convolve_image(im, f, 1)
hfreq = im - lfreq
reconstruct = lfreq + hfreq
save_image(lfreq, "output/low_frequency_dog.jpg")
save_image(hfreq, "output/high_frequency_dog.jpg")
save_image(reconstruct, "output/reconstruct_dog.jpg")

low frequency

high frequency

reconstruct

Image Features

Sobel filters

from uwimg import *
im = load_image("data/dog.jpg")
res = sobel_image(im)
mag = res[0]
feature_normalize(mag)
save_image(mag, "output/magnitude")

Colorized Representation

from uwimg import *
im = load_image("data/dog.jpg")
res = sobel_image(im)
mag = res[0]
feature_normalize(mag)
img = colorize_sobel(im)
save_image(img, "output/colored_magnitude_dog")

Panorama

Harris detector

from uwimg import *
im = load_image("data/Rainier1.png")
detect_and_draw_corners(im, 2, 50, 3)
save_image(im, "output/corners")

Patch matching

from uwimg import *
a = load_image("data/Rainier1.png")
b = load_image("data/Rainier2.png")
m = find_and_draw_matches(a, b, 2, 50, 3)
save_image(m, "output/matches")

Combine the images with a homography

from uwimg import *
im1 = load_image("data/Rainier1.png")
im2 = load_image("data/Rainier2.png")
pan = panorama_image(im1, im2, thresh=50)
save_image(pan, "output/easy_panorama")

Final panorama image

Optical Flow

from uwimg import *
a = load_image("data/dog_a.jpg")
b = load_image("data/dog_b.jpg")
flow = optical_flow_images(b, a, 15, 8)
draw_flow(a, flow, 8)
save_image(a, "output/optical-flow")

image a

image b

optical flow

Neural Network

Download data

wget https://pjreddie.com/media/files/mnist_train.tar.gz
wget https://pjreddie.com/media/files/mnist_test.tar.gz
tar xzf mnist_train.tar.gz
tar xzf mnist_test.tar.gz

Train script

from uwimg import *

def softmax_model(inputs, outputs):

	l = [make_layer(inputs, outputs, SOFTMAX)]

	return make_model(l)

def neural_net(inputs, outputs):

	print(inputs)

	l = [ make_layer(inputs, 32, LOGISTIC),

	make_layer(32, outputs, SOFTMAX)]

	return make_model(l)

print("loading data...")

train = load_classification_data("mnist.train", "mnist.labels", 1)

test = load_classification_data("mnist.test", "mnist.labels", 1)

print("done")

print

print("training model...")

batch = 128

iters = 1000

rate = .01

momentum = .9

decay = .0

m = softmax_model(train.X.cols, train.y.cols)

train_model(m, train, batch, iters, rate, momentum, decay)

print("done")

print()

print("evaluating model...")

print("training accuracy: %f", accuracy_model(m, train))

print("test accuracy: %f", accuracy_model(m, test))

loading data...
done

training model...
000000: Loss: 2.370572
000001: Loss: 2.302090
000002: Loss: 2.287877
...
...
...
000997: Loss: 0.347353
000998: Loss: 0.387637
000999: Loss: 0.330449
done

evaluating model...
training accuracy: %f 0.90235
test accuracy:     %f 0.9077

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uwimg's Introduction