def hough_transform_image(image, img):
"""
:param image: pass the canny iamge
:param img: pass the original image
:return: it will mark the lines in the canny image and passes it back to the original image
"""
lines = cv2.HoughLinesP(image, 1, np.pi / 180, 50, maxLineGap=200)
for line in lines:
print(line)
x1, x2, y1, y2 = line[0]
cv2.line(img, (x1, x2), (y1, y2), (0, 255, 0), 3)
show_image(img)
return lines
"""
Here you will find the code for low-pas-filter
"""
import cv2
from helpers.image_reader import image_reader, show_image
def low_pass_filter(img):
"""
:param img:
:return:
"""
fil = cv2.boxFilter(img, -1, (57, 57))
return fil
if __name__ == "__main__":
path = "/home/joemarshal/image_processing_opencv/images/shapes.jpg"
mode = 1
ima = image_reader(path, mode)
show_image(ima)
con = cv2.cvtColor(ima, cv2.COLOR_BGR2RGB)
show = low_pass_filter(con)
show_image(show)
"""
Here is the code to detect the shape in an imaage
"""
import cv2
from helpers.Shapedetector import Shapedetector
from helpers.image_reader import image_reader, show_image
import imutils
if __name__ == "__main__":
im1 = "/home/joemarshal/image_processing_opencv/images/shapes.jpg"
mode = 1
img = image_reader(im1, mode)
show_image(img)
cv2.imwrite("/home/joemarshal/shapes.png", img)
resized = imutils.resize(img, width=300)
ratio = img.shape[0] / float(resized.shape[0])
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
edges = cv2.Canny(thresh, 75, 150)
show_image(edges)
show_image(thresh)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = Shapedetector()
for c in cnts:
M = cv2.moments(c)
cx = int((M["m10"] / M["m00"]) * ratio)
cy = int((M["m01"] / M["m00"]) * ratio)
"""
Code to compute the histogram of an image
"""
import cv2
from helpers.image_reader import image_reader, show_image
import matplotlib.pyplot as plt
if __name__ == "__main__":
img = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = 0
im = image_reader(img, mode)
show_image(im)
cv2.imwrite("/home/joemarshal/grayronaldo.jpg", im)
hist = cv2.calcHist([im], [0], None, [256], [0, 256])
print(type(hist))
show_image(hist)
plt.show()
"""
Here you can find how to add to images using the cv2.add() function
"""
import cv2
from helpers.image_reader import image_reader, show_image
def add_image(image1, image2):
"""
:param image1: passing image1
:param image2: passing image 2
:return: adds both the images
"""
add = cv2.add(image1, image2)
return add
if __name__ == "__main__":
img1 = "/home/joemarshal/image_processing/images/resizedronaldo.jpg"
img2 = "/home/joemarshal/image_processing/images/resizedmessi.jpg"
mode = 1
img1 = image_reader(img1, mode)
img2 = image_reader(img2, mode)
adding = add_image(img1, img2)
show_image(adding)
"""
Here you can run the threshold of an image without the threshold function
"""
import cv2
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
img = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = cv2.IMREAD_COLOR
im = image_reader(img, 0)
print(im)
print(im.shape)
height, width = im.shape
for h in range(height):
for w in range(width):
v = (im[h][w])
if v < 255:
im[h][w] = 127
else:
im[h][w] = 0
show_image(im)
from __future__ import print_function
import cv2
import numpy as np
from helpers.image_reader import image_reader, show_image
im1 = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = 1
img = image_reader(im1, mode)
for alpha in np.arange(0, 1.1, 0.1)[::-1]:
if alpha > 0.0:
print("transparnet image", alpha)
else:
print("Not Transparent", alpha)
overlay = img.copy()
output = img.copy()
cv2.rectangle(overlay, (420, 205), (595, 385), (0, 0, 255), -1)
cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
print("alpha{}, beta{}".format(alpha, 1 - alpha))
show_image(output)
result_morphological_tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT,
kernel)
return result_morphological_tophat
def morphological_blackhat(img):
kernel = np.ones((5, 5), np.uint8)
result_morphological_blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT,
kernel)
return result_morphological_blackhat
if __name__ == "__main__":
im = "/home/joemarshal/Downloads/watershed_coins_01.jpg"
mode = 0
img = image_reader(im, mode)
show_image(img)
erotion = eroding_image(img)
dilation = dilating_image(img)
opening_image = opening_image_morphology(img)
closing_image = closing_image_morphology(img)
gradient = morphological_gradient(img)
tophat = morphological_tophat(img)
blackhat = morphological_blackhat(img)
show_image(erotion)
show_image(dilation)
show_image(opening_image)
show_image(closing_image)
show_image(gradient)
show_image(tophat)
show_image(blackhat)
"""
Code for Grabcut Algorithm
"""
import cv2
import numpy as np
from helpers.image_reader import image_reader, show_image
import matplotlib.pyplot as plt
if __name__ == "__main__":
im = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = 1
img = image_reader(im, mode)
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (50, 50, 400, 267)
show_image(rect)
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
image = img * mask2[:, :, np.newaxis]
plt.imshow(image), plt.colorbar(), plt.show()
show_image(image)
cv2.imwrite(
"/home/joemarshal/image_processing_opencv/images/grabronaldo.jpg",
image)
"""
Here you can find the code for the brightness of the image
"""
import cv2
import numpy as np
from helpers.image_reader import show_image
if __name__ == "__main__":
img = cv2.imread("/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg", 1)
alpha = 2
beta = 10
result = cv2.addWeighted(img, alpha, np.zeros(img.shape, img.dtype), 0, beta)
show_image(result)
cv2.imwrite("/home/joemarshal/Documents/bc.jpg", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
Here you will see how to add two images without the cv2.add() function
"""
from helpers.image_reader import image_reader, show_image
def adding_images_without_add_function(imag1, imag2):
"""
:param imag1: passing image1
:param imag2: passing image 2
:return: adds both the images
"""
add1 = imag1 + imag2
return add1
if __name__ == "__main__":
im1 = "/home/joemarshal/image_processing/images/resizedronaldo.jpg"
im2 = "/home/joemarshal/image_processing/images/resizedmessi.jpg"
mode = 1
img1 = image_reader(im1, mode)
img2 = image_reader(im2, mode)
add = adding_images_without_add_function(img1, img2)
show_image(add)
import cv2
import matplotlib.pyplot as plt
import numpy as np
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
im = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
img = image_reader(im, 0)
show_image(img)
mask = np.zeros(img.shape[:2], np.uint8)
mask[82:328, 31:338] = 255
masked_img = cv2.bitwise_and(img, img, mask=mask)
show_image(masked_img)
hist_full = plt.hist(img.ravel(), 256, [0, 256])
hist_mask = plt.hist(masked_img.ravel(), 256, [0, 256])
plt.xlim([0, 256])
plt.show()
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import cv2
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
im = "/home/joemarshal/image_processing_opencv/images/watershed_coins_01.jpg"
mode = 1
img = image_reader(im, mode)
show_image(img)
shifted = cv2.pyrMeanShiftFiltering(img, 21, 51)
show_image(shifted)
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
show_image(thresh)
D = ndimage.distance_transform_edt(thresh)
local_max = peak_local_max(D,
indices=False,
min_distance=20,
labels=thresh)
markers = ndimage.label(local_max, structure=np.ones((3, 3)))[0]
labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))
for label in np.unique(labels):
if label == 0:
continue
mask = np.zeros(gray.shape, dtype="uint8")
"""
Here you will see how to change the color image to black and white image using cv2.inRange() function
"""
import cv2
import numpy as np
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
img = "/home/joemarshal/image_processing/images/resizedronaldo.jpg"
mode = cv2.IMREAD_COLOR
# Reading the image without any change
im = image_reader(img, mode)
# Setting the lower threshold
lower_color = np.array([[0, 60, 0]])
# setting the upper threshold
upper_color = np.array([255, 150, 255])
mask = cv2.inRange(im, lower_color, upper_color)
i = show_image(mask)
import cv2
import numpy as np
from helpers.image_reader import image_reader, show_image
def hough_transform_image(image, img):
"""
:param image: pass the canny iamge
:param img: pass the original image
:return: it will mark the lines in the canny image and passes it back to the original image
"""
lines = cv2.HoughLinesP(image, 1, np.pi / 180, 50, maxLineGap=200)
for line in lines:
print(line)
x1, x2, y1, y2 = line[0]
cv2.line(img, (x1, x2), (y1, y2), (0, 255, 0), 3)
show_image(img)
return lines
if __name__ == "__main__":
img1 = "/home/joemarshal/image_processing_opencv/images/lines.png"
mode = 0
image1 = image_reader(img1, 1)
grey = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(grey, 75, 150)
show_image(edges)
hough = hough_transform_image(edges, image1)
import cv2
from helpers.image_reader import image_reader, show_image
im = "/home/joemarshal/Downloads/himan.png"
mode = -1
img = image_reader(im, mode)
# print(img.shape)
height, width, *no_of_channels = img.shape
print(height, width, no_of_channels)
for h in range(height):
for w in range(width):
k = img[h][w]
k = k[:3]
img1[h][w] = k
show_image(img)
cv2.imwrite("trying.png", img)
"""
Code to equalise images with histogram
"""
import numpy as np
import cv2
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
im = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = 0
img = image_reader(im, mode)
show_image(img)
mask = np.zeros(img.shape[:2], np.uint8)
mask[82:328, 31:338] = 255
masked_img = cv2.bitwise_and(img, img, mask=mask)
equal = cv2.equalizeHist(img)
mask1 = cv2.equalizeHist(masked_img)
res = np.hstack((img, equal, mask1))
show_image(res)
cv2.imwrite("/home/joemarshal/image_processing_opencv/images/contrast_ronaldo.png", equal)
cv2.imwrite("/home/joemarshal/image_processing_opencv/images/equal_hist.png", res)
import cv2
from helpers.image_reader import image_reader, show_image
if __name__ == "__main__":
img = "/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg"
mode = cv2.WINDOW_NORMAL
im = image_reader(img, mode)
rows, columns = im.shape[:2]
print(rows, columns)
center = (rows / 2, columns / 2)
rotating_image = cv2.getRotationMatrix2D(center, 570, 1.0)
m = cv2.warpAffine(im, rotating_image, (columns, rows))
i = show_image(m)
h, w, *no_of_channels = cvt3.shape
for y in range(h):
for x in range(w):
cvt3[y][x][2] = cvt3[y][x][2] + factor
print(cvt3[y][x][2])
return cvt3
if __name__ == "__main__":
im = "/home/joemarshal/image_processing_opencv/images/circles.png"
mode = 1
img = image_reader(im, mode)
cvt = bgr2hsv(img)
factor = 100
show_image(cvt)
# print(cvt[:, :, 0])
#
res = increase_hue_value(cvt, factor)
show_image(res)
#
res1 = increase_saturation_value(cvt, factor)
# pprint(cvt)
# print("-------------")
# pprint(res1)
show_image(res1)
res2 = increase_value(cvt, factor)
show_image(res2)
#
"""
Here you will see the code for thresholding an image
"""
import cv2
from helpers.image_reader import show_image
if __name__ == "__main__":
img = cv2.imread(
'/home/joemarshal/image_processing_opencv/images/resizedronaldo.jpg',
0)
ret, thresh_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
i = show_image(thresh_img)
cv2.imwrite(
"/home/joemarshal/image_processing_opencv/images/thresholdronaldo.jpg",
thresh_img)
"""
:param img1: pass image 1
:param img2: pass image 2
:return: returns the division of both the images
"""
return cv2.divide(img1, img2)
if __name__ == "__main__":
im1 = "/home/joemarshal/image_processing/images/resizedronaldo.jpg"
im2 = "/home/joemarshal/image_processing/images/resizedmessi.jpg"
mode = cv2.WINDOW_NORMAL
image1 = image_reader(im1, mode)
image2 = image_reader(im2, mode)
show_image(image1)
show_image(image2)
bit_and = bitwise_and(image1, image2)
bit_or = bitwise_or(image1, image2)
bit_not = bitwise_not(image1, image2)
bit_xor = bitwise_xor(image1, image2)
subt = subtract(image1, image2)
mul = multiplication(image1, image2)
div = division(image1, image2)
show_image(bit_and)
show_image(bit_or)
show_image(bit_not)
show_image(bit_xor)
show_image(subt)
show_image(mul)
show_image(div)
