def read_img(img_path: str,
scale: float = 0.0,
concat_gray: bool = False) -> None:
'''
Reading and showing image function
Args:
img_path: str - path to your image data
scale: float - in order to rescale your image
concat_gray: bool - if requires to show RGB and GRAY
in one window
'''
img = cv.imread(img_path)
if scale:
img = rescale_frame(img, scale)
if concat_gray:
# This method firstly transforms BGR to GRAY - 3D -> 1D
# and then from 1D -> 3D but remains still gray
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray_BGR = cv.cvtColor(gray, cv.COLOR_GRAY2BGR)
# Matrix concatenation
img = np.hstack((img, gray_BGR))
show_img(img, 'Image')
import cv2 as cv
import numpy as np
from basic_functions.helpers import show_img
if __name__ == "__main__":
img_path = "data/images/city.jpg"
# Reading image as gray scale
img = cv.imread(img_path)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Empty image to draw contours
blank = np.zeros(img.shape, dtype='uint8')
# Exerting Gaussian blur
blur = cv.GaussianBlur(gray, (5, 5), cv.BORDER_DEFAULT)
# Canny for edge detection
canny = cv.Canny(blur, 125, 175)
ret, thresh = cv.threshold(gray, 125, 255, cv.THRESH_BINARY)
cv.imshow('Thresh', thresh)
contours, hierarchies = cv.findContours(thresh, cv.RETR_LIST,
cv.CHAIN_APPROX_SIMPLE)
print(f"Founded countours: {len(contours)}")
cv.drawContours(blank, contours, -1, (0, 0, 255), 1)
show_img(blank)
import cv2 as cv
import numpy as np
from basic_functions.helpers import show_img
if __name__ == "__main__":
img_path = "data/images/cat.jpg"
img = cv.imread(img_path)
# Averaging
average = cv.blur(img, (3, 3))
img_average = np.hstack((img, average))
show_img(img_average, "Image & Averaging")
# Gaussian Blur
gauss = cv.GaussianBlur(img, (5, 5), 0)
img_gauss = np.hstack((img, gauss))
show_img(img_gauss, "Image & GaussianBlur")
# Median Blur
median = cv.medianBlur(img, 7)
img_median = np.hstack((img, median))
show_img(img_median, "Image & MedianBlur")
# Bilateral
bilateral = cv.bilateralFilter(img, 10, 35, 25)
img_bilateral = np.hstack((img, bilateral))
show_img(img_bilateral, "Image & Bilateral")
from basic_functions.read_data import read_img, read_video
from basic_functions.transformations import translate, rotate
from basic_functions.helpers import show_img
if __name__ == "__main__":
img_path = "data/images/city.jpg"
# Reading image
img = cv.imread(img_path)
# Splitting into 3 colors
b, g, r = cv.split(img)
# distinct_colors = np.hstack((b, g, r))
distinct_colors = cv.resize(distinct_colors, (1200, 400))
show_img(distinct_colors, "Distinct colors")
# Merging it back together
img_merged = cv.merge((b, g, r))
show_img(img_merged, "Merged")
# Adding to images
# Saturation of 2 images
bg_add = cv.add(b, g)
show_img(bg_add, "Blue and Green")
gr_add = cv.add(g, r)
show_img(gr_add, "Green and Red")
# Weighted adding
# result = img_1 * alpha + img_2 * beta + gamma
import cv2 as cv
import numpy as np
from basic_functions.read_data import read_img, read_video
from basic_functions.helpers import show_img
if __name__ == "__main__":
img_path = "data/images/city.jpg"
# Reading image
img = cv.imread(img_path)
# Blurring
blur = cv.GaussianBlur(img, (3, 3), cv.BORDER_DEFAULT)
img_blur = np.hstack((img, blur))
show_img("Image and Blur image", img_blur)
# Edge Cascade
canny_img = cv.Canny(img, 125, 175)
canny_blur = cv.Canny(blur, 125, 175)
canny = np.hstack((canny_img, canny_blur))
show_img("Canny edges on Image and Blur", canny)
# Image dilation
dilated = cv.dilate(canny_blur, (7, 7), iterations=3)
canny_dilated = np.hstack((canny_blur, dilated))
show_img("Dilated Canny edges", canny_dilated)
# Image erosion