#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

###########

# Let us start by looking at basic image transformation tasks like

#resize and rescale.

#Then let's look at a few ways to do edge detection.

#And then sharpening using deconvolution method and finally

#Then let's take a real life scenario like scratch assay analysis.

#Resize, rescale

import matplotlib.pyplot as plt

from skimage import io, color

from skimage.transform import rescale, resize, downscale_local_mean

img = io.imread("images/Osteosarcoma_01.tif", as_gray=True)

#Rescale, resize image by a given factor. While rescaling image

#gaussian smoothing can performed to avoid anti aliasing artifacts.

img_rescaled = rescale(img, 1.0 / 4.0, anti_aliasing=False) #Check rescales image size in variable explorer

#Resize, resize image to given dimensions (shape)

img_resized = resize(img, (200, 200), #Check dimensions in variable explorer

anti_aliasing=True)

#Downscale, downsample using local mean of elements of each block defined by user

img_downscaled = downscale_local_mean(img, (4, 3))

plt.imshow(img_downscaled)

################################################

#A quick look at a few skimage functions

from skimage import io

from skimage.filters import gaussian, sobel

img = io.imread("images/Osteosarcoma_01_25Sigma_noise.tif")

plt.imshow(img)

gaussian_using_skimage = gaussian(img, sigma=1, mode='constant', cval=0.0)

plt.imshow(gaussian_using_skimage)

img_gray = io.imread("images/Osteosarcoma_01.tif", as_gray=True)

sobel_img = sobel(img_gray) #Works only on 2D (gray) images

plt.imshow(sobel_img, cmap='gray')