Coat
Coat is small wrapper that sits on numpy's ndarray (subclassed) and opencv
Coat's only purpouse is for rapid prototyping
Style
Coat is using method cascading
return from every method is Coat's HigherCoating instance
Only exception is method classic() which returns back numpy instance\
Install
preferably use virtual env
pip install coat
Demo
function Coat is a proxy function that handles different types of args.
It handles str(url), list/generator of images and ndarray.
from coat import Coat
url = "https://natgeo.imgix.net/subjects/headers/shutterstock_276228476.jpg?auto=compress,format&w=1920&h=960&fit=crop"
Coat(url).thresh(125,255).show()
Usage
Content
Core functionalities
Dominance
Array manipulation
Image downloading
Labeling
Color spaces
Helper functions
Montage
Contours
Color filtering
Motion difference
Core functionalities
Lets define two arrays of different type, size and dimension
# Import Coat(proxy function) and Montage
from coat import Coat, Montage
import numpy as np
array1 = Coat(np.zeros(shape=[40,60,3],dtype=np.uint8))
array2 = Coat(np.zeros(shape=[8,8],dtype=np.float32))Auto resolver
Coating the arrays you let Coat resolve array operations whenever there is conflict
res = array1 + array2
res.shape,res.dtype
>>> (40, 60, 3) uint8Coated arrays can be dominant or non-dominant. General rules are as follows:
A(non-dominant) + B(non-dominant) -> A is prioritized
A(non-dominant) + B(dominant) -> B is prioritized
A(dominant) + B(non-dominant) -> A is prioritized
A(dominant) + B(dominant) -> A is prioritizedDominance setting
You can set which array is dominant Be default each array is not dominant.
res = array1 * array2.host()
res.shape,res.dtype
>>> (8, 8) float32If have arrays of different size and dimension( gray & colored), you can simply transform to common standard as follows
list_of_images = [img1,img2, img3, .., .., imgN]
# define common standard
grayscale_template = Coat(np.zeros(shape=[100,100],dtype=np.uint8))
colored_template = Coat(np.zeros(shape=[100,100],dtype=np.uint8))
grascaled = [grayscale_template.host() + image for image in list_of_images]
colored = [colored_template.host() + image for image in list_of_images]interpolation
You can set interpolation algorithm (default is LINEAR - BILINEAR)
res = array1 - array2.host('CUBIC')Removing dominance
res = array1 * array2.host('CUBIC').guest()
res.shape,res.dtype
>>> (40, 60, 3) uint8Array manipulation
osize as objective size
array1.shape, array2.shape
>>> (40, 60, 3) , (8,8)
array1.osize(array2.shape).shape
>>> (8,8,3)
array2.osize(array1.shape).shape
>>> (40, 60)rsize as relative size
res = array2.rsize(fx = 2, fy=0.5)
res.shape
>>> (16, 4)ndarray compatibility
res = array1 + np.zeros(shape=array1.shape)
np.uint8(array1) # -> returns back Coat instance with changed datatype
array1.classic() # -> returns back numpy instanceLeveraging OpenCV
Download image
pass url to Coat and show it
url = "https://natgeo.imgix.net/subjects/headers/shutterstock_276228476.jpg?auto=compress,format&w=1920&h=960&fit=crop"
image = Coat(url).show()
image = image.rsize(fx=0.25,fy=0.25)
Supports labeling
# Use int for objective coordinates
image.labelbox("Home",(0,136),(230,340), bcolor = [0,255,0]).show()
# Use float for relative coordinates
image.labelbox("Home",(0.0,0.3),(0.9,0.7), bcolor = [0,255,0]).show()
Colorspace change
supported color transformation
BGR2GRAY
BGR2HLS
BGR2HSV
HSV2BGR
GRAY2RGB
GRAY2BGR
RGB2GRAY
RGB2HSV
RGB2HLS
image.color_to('BGR2GRAY')NOTE
OpenCV is using BGR as default color scheme
Image processing helpers
Threshold
image.thresh(125,255,'thresh_binary').show()
Convolution filtering
image.blur_median
image.filter_bilateral
image.blur_gauss
image.blur_averageConvolution 2D
img.conv(kernel)Montage
See orignal next to processed image
we add host (turn on dominance of first image) so we get result in RGB colorspace as our original image is rgb
image.rsize(fx=0.3,fy=0.3).host().join(image.thresh(127,255)).show()
Montage of different color spaces
img = image.rsize(fx=0.3,fy=0.3)
color_spaces = ["BGR2GRAY","BGR2HLS","BGR2HSV",]Dominant is the first image if template is not defined
all_images = [img] + [img.to_color(cspace) for cspace in color_spaces]
montage = Montage(all_images).grid(2,2).show()
resize montage based on template
montage = Montage(all_images).template(np.zeros(shape=[50,50,3],dtype=np.uint8)).grid(2,2)Remove stars with morphological opening
image.morphologyEx('open',3).show()
Contours
# Draw quick countours
thr = image.thresh(200,255)
# copy
contoured = image.copy().contours(thr,min_size=5, max_size = 9999999,thickness=2,color = [0,125,255]).show()
Replace particular color
present = [0,125,255]
future = [255,0,0]
contoured.replace(present,future).show()
Color filtering
# [36,0,0] --> green color interval in HSV <--[70,255,255]
image.filterHsv([36,0,0],[70,255,255],passband=True).show()
image.filterHsv([36,0,0],[70,255,255],passband=False).show()
# Passband False: -------|++|-----
# Passband True: ++++++|--|++++++ passband True
passband False
Motion difference
box1 = Coat(np.zeros(shape=[400,400,3])).box((30,30),(250,250),color=[255,125,0])
box2 = Coat(np.zeros(shape=[400,400,3])).box((30,150),(250,350),color=[255,125,0])
motion_diff = box1.motion_difference(box2,val=30).show()
