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python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

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This commit is contained in:
Alexander Alekhin
2017-12-11 12:55:03 +03:00
parent 9665dde678
commit 5560db73bf
162 changed files with 2083 additions and 2084 deletions
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samples/python/tutorial_code/ImgTrans/Filter2D/filter2D.py
+5 -5
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@@ -3,7 +3,7 @@
@brief Sample code that shows how to implement your own linear filters by using filter2D function
"""
import sys
import cv2
import cv2 as cv
import numpy as np
@@ -14,7 +14,7 @@ def main(argv):
imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
# Loads an image
src = cv2.imread(imageName, cv2.IMREAD_COLOR)
src = cv.imread(imageName, cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
@@ -37,11 +37,11 @@ def main(argv):
## [update_kernel]
## [apply_filter]
# Apply filter
dst = cv2.filter2D(src, ddepth, kernel)
dst = cv.filter2D(src, ddepth, kernel)
## [apply_filter]
cv2.imshow(window_name, dst)
cv.imshow(window_name, dst)
c = cv2.waitKey(500)
c = cv.waitKey(500)
if c == 27:
break
samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py
+9 -9
View File
@@ -1,5 +1,5 @@
import sys
import cv2
import cv2 as cv
import numpy as np
@@ -9,7 +9,7 @@ def main(argv):
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv2.imread(filename, cv2.IMREAD_COLOR)
src = cv.imread(filename, cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
@@ -20,17 +20,17 @@ def main(argv):
## [convert_to_gray]
# Convert it to gray
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
## [convert_to_gray]
## [reduce_noise]
# Reduce the noise to avoid false circle detection
gray = cv2.medianBlur(gray, 5)
gray = cv.medianBlur(gray, 5)
## [reduce_noise]
## [houghcircles]
rows = gray.shape[0]
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, rows / 8,
circles = cv.HoughCircles(gray, cv.HOUGH_GRADIENT, 1, rows / 8,
param1=100, param2=30,
minRadius=1, maxRadius=30)
## [houghcircles]
@@ -41,15 +41,15 @@ def main(argv):
for i in circles[0, :]:
center = (i[0], i[1])
# circle center
cv2.circle(src, center, 1, (0, 100, 100), 3)
cv.circle(src, center, 1, (0, 100, 100), 3)
# circle outline
radius = i[2]
cv2.circle(src, center, radius, (255, 0, 255), 3)
cv.circle(src, center, radius, (255, 0, 255), 3)
## [draw]
## [display]
cv2.imshow("detected circles", src)
cv2.waitKey(0)
cv.imshow("detected circles", src)
cv.waitKey(0)
## [display]
return 0
samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py
+12 -12
View File
@@ -4,7 +4,7 @@
"""
import sys
import math
import cv2
import cv2 as cv
import numpy as np
@@ -14,7 +14,7 @@ def main(argv):
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
src = cv.imread(filename, cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
@@ -25,16 +25,16 @@ def main(argv):
## [edge_detection]
# Edge detection
dst = cv2.Canny(src, 50, 200, None, 3)
dst = cv.Canny(src, 50, 200, None, 3)
## [edge_detection]
# Copy edges to the images that will display the results in BGR
cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
cdstP = np.copy(cdst)
## [hough_lines]
# Standard Hough Line Transform
lines = cv2.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
## [hough_lines]
## [draw_lines]
# Draw the lines
@@ -49,29 +49,29 @@ def main(argv):
pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))
cv2.line(cdst, pt1, pt2, (0,0,255), 3, cv2.LINE_AA)
cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
## [draw_lines]
## [hough_lines_p]
# Probabilistic Line Transform
linesP = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
## [hough_lines_p]
## [draw_lines_p]
# Draw the lines
if linesP is not None:
for i in range(0, len(linesP)):
l = linesP[i][0]
cv2.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv2.LINE_AA)
cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)
## [draw_lines_p]
## [imshow]
# Show results
cv2.imshow("Source", src)
cv2.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv2.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
cv.imshow("Source", src)
cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
## [imshow]
## [exit]
# Wait and Exit
cv2.waitKey()
cv.waitKey()
return 0
## [exit]
samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py
+10 -10
View File
@@ -3,12 +3,12 @@
@brief Sample code showing how to detect edges using the Laplace operator
"""
import sys
import cv2
import cv2 as cv
def main(argv):
# [variables]
# Declare the variables we are going to use
ddepth = cv2.CV_16S
ddepth = cv.CV_16S
kernel_size = 3
window_name = "Laplace Demo"
# [variables]
@@ -16,7 +16,7 @@ def main(argv):
# [load]
imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
src = cv2.imread(imageName, cv2.IMREAD_COLOR) # Load an image
src = cv.imread(imageName, cv.IMREAD_COLOR) # Load an image
# Check if image is loaded fine
if src is None:
@@ -27,30 +27,30 @@ def main(argv):
# [reduce_noise]
# Remove noise by blurring with a Gaussian filter
src = cv2.GaussianBlur(src, (3, 3), 0)
src = cv.GaussianBlur(src, (3, 3), 0)
# [reduce_noise]
# [convert_to_gray]
# Convert the image to grayscale
src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
# [convert_to_gray]
# Create Window
cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)
cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
# [laplacian]
# Apply Laplace function
dst = cv2.Laplacian(src_gray, ddepth, kernel_size)
dst = cv.Laplacian(src_gray, ddepth, kernel_size)
# [laplacian]
# [convert]
# converting back to uint8
abs_dst = cv2.convertScaleAbs(dst)
abs_dst = cv.convertScaleAbs(dst)
# [convert]
# [display]
cv2.imshow(window_name, abs_dst)
cv2.waitKey(0)
cv.imshow(window_name, abs_dst)
cv.waitKey(0)
# [display]
return 0
samples/python/tutorial_code/ImgTrans/MakeBorder/copy_make_border.py
+9 -9
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@@ -4,20 +4,20 @@
"""
import sys
from random import randint
import cv2
import cv2 as cv
def main(argv):
## [variables]
# First we declare the variables we are going to use
borderType = cv2.BORDER_CONSTANT
borderType = cv.BORDER_CONSTANT
window_name = "copyMakeBorder Demo"
## [variables]
## [load]
imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
# Loads an image
src = cv2.imread(imageName, cv2.IMREAD_COLOR)
src = cv.imread(imageName, cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
@@ -33,7 +33,7 @@ def main(argv):
' ** Press \'r\' to set the border to be replicated \n'
' ** Press \'ESC\' to exit the program ')
## [create_window]
cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)
cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
## [create_window]
## [init_arguments]
# Initialize arguments for the filter
@@ -47,20 +47,20 @@ def main(argv):
value = [randint(0, 255), randint(0, 255), randint(0, 255)]
## [update_value]
## [copymakeborder]
dst = cv2.copyMakeBorder(src, top, bottom, left, right, borderType, None, value)
dst = cv.copyMakeBorder(src, top, bottom, left, right, borderType, None, value)
## [copymakeborder]
## [display]
cv2.imshow(window_name, dst)
cv.imshow(window_name, dst)
## [display]
## [check_keypress]
c = cv2.waitKey(500)
c = cv.waitKey(500)
if c == 27:
break
elif c == 99: # 99 = ord('c')
borderType = cv2.BORDER_CONSTANT
borderType = cv.BORDER_CONSTANT
elif c == 114: # 114 = ord('r')
borderType = cv2.BORDER_REPLICATE
borderType = cv.BORDER_REPLICATE
## [check_keypress]
return 0
samples/python/tutorial_code/ImgTrans/SobelDemo/sobel_demo.py
+14 -14
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@@ -3,7 +3,7 @@
@brief Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
"""
import sys
import cv2
import cv2 as cv
def main(argv):
@@ -12,7 +12,7 @@ def main(argv):
window_name = ('Sobel Demo - Simple Edge Detector')
scale = 1
delta = 0
ddepth = cv2.CV_16S
ddepth = cv.CV_16S
## [variables]
## [load]
@@ -24,7 +24,7 @@ def main(argv):
return -1
# Load the image
src = cv2.imread(argv[0], cv2.IMREAD_COLOR)
src = cv.imread(argv[0], cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
@@ -34,38 +34,38 @@ def main(argv):
## [reduce_noise]
# Remove noise by blurring with a Gaussian filter ( kernel size = 3 )
src = cv2.GaussianBlur(src, (3, 3), 0)
src = cv.GaussianBlur(src, (3, 3), 0)
## [reduce_noise]
## [convert_to_gray]
# Convert the image to grayscale
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
## [convert_to_gray]
## [sobel]
# Gradient-X
# grad_x = cv2.Scharr(gray,ddepth,1,0)
grad_x = cv2.Sobel(gray, ddepth, 1, 0, ksize=3, scale=scale, delta=delta, borderType=cv2.BORDER_DEFAULT)
# grad_x = cv.Scharr(gray,ddepth,1,0)
grad_x = cv.Sobel(gray, ddepth, 1, 0, ksize=3, scale=scale, delta=delta, borderType=cv.BORDER_DEFAULT)
# Gradient-Y
# grad_y = cv2.Scharr(gray,ddepth,0,1)
grad_y = cv2.Sobel(gray, ddepth, 0, 1, ksize=3, scale=scale, delta=delta, borderType=cv2.BORDER_DEFAULT)
# grad_y = cv.Scharr(gray,ddepth,0,1)
grad_y = cv.Sobel(gray, ddepth, 0, 1, ksize=3, scale=scale, delta=delta, borderType=cv.BORDER_DEFAULT)
## [sobel]
## [convert]
# converting back to uint8
abs_grad_x = cv2.convertScaleAbs(grad_x)
abs_grad_y = cv2.convertScaleAbs(grad_y)
abs_grad_x = cv.convertScaleAbs(grad_x)
abs_grad_y = cv.convertScaleAbs(grad_y)
## [convert]
## [blend]
## Total Gradient (approximate)
grad = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)
grad = cv.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)
## [blend]
## [display]
cv2.imshow(window_name, grad)
cv2.waitKey(0)
cv.imshow(window_name, grad)
cv.waitKey(0)
## [display]
return 0