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convenience.py
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convenience.py
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# author: Adrian Rosebrock
# website: http://www.pyimagesearch.com
# import the necessary packages
import numpy as np
import cv2
import sys
# import any special Python 2.7 packages
if sys.version_info.major == 2:
from urllib import urlopen
# import any special Python 3 packages
elif sys.version_info.major == 3:
from urllib.request import urlopen
def translate(image, x, y):
# define the translation matrix and perform the translation
M = np.float32([[1, 0, x], [0, 1, y]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
# return the translated image
return shifted
def rotate(image, angle, center=None, scale=1.0):
# grab the dimensions of the image
(h, w) = image.shape[:2]
# if the center is None, initialize it as the center of
# the image
if center is None:
center = (w // 2, h // 2)
# perform the rotation
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h))
# return the rotated image
return rotated
def rotate_bound(image, angle):
# grab the dimensions of the image and then determine the
# center
(h, w) = image.shape[:2]
(cX, cY) = (w / 2, h / 2)
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# perform the actual rotation and return the image
return cv2.warpAffine(image, M, (nW, nH))
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation=inter)
# return the resized image
return resized
def skeletonize(image, size, structuring=cv2.MORPH_RECT):
# determine the area (i.e. total number of pixels in the image),
# initialize the output skeletonized image, and construct the
# morphological structuring element
area = image.shape[0] * image.shape[1]
skeleton = np.zeros(image.shape, dtype="uint8")
elem = cv2.getStructuringElement(structuring, size)
# keep looping until the erosions remove all pixels from the
# image
while True:
# erode and dilate the image using the structuring element
eroded = cv2.erode(image, elem)
temp = cv2.dilate(eroded, elem)
# subtract the temporary image from the original, eroded
# image, then take the bitwise 'or' between the skeleton
# and the temporary image
temp = cv2.subtract(image, temp)
skeleton = cv2.bitwise_or(skeleton, temp)
image = eroded.copy()
# if there are no more 'white' pixels in the image, then
# break from the loop
if area == area - cv2.countNonZero(image):
break
# return the skeletonized image
return skeleton
def opencv2matplotlib(image):
# OpenCV represents images in BGR order; however, Matplotlib
# expects the image in RGB order, so simply convert from BGR
# to RGB and return
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def url_to_image(url, readFlag=cv2.IMREAD_COLOR):
# download the image, convert it to a NumPy array, and then read
# it into OpenCV format
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, readFlag)
# return the image
return image
def auto_canny(image, sigma=0.33):
# compute the median of the single channel pixel intensities
v = np.median(image)
# apply automatic Canny edge detection using the computed median
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edged = cv2.Canny(image, lower, upper)
# return the edged image
return edged
def grab_contours(cnts):
# if the length the contours tuple returned by cv2.findContours
# is '2' then we are using either OpenCV v2.4, v4-beta, or
# v4-official
if len(cnts) == 2:
cnts = cnts[0]
# if the length of the contours tuple is '3' then we are using
# either OpenCV v3, v4-pre, or v4-alpha
elif len(cnts) == 3:
cnts = cnts[1]
# otherwise OpenCV has changed their cv2.findContours return
# signature yet again and I have no idea WTH is going on
else:
raise Exception(("Contours tuple must have length 2 or 3, "
"otherwise OpenCV changed their cv2.findContours return "
"signature yet again. Refer to OpenCV's documentation "
"in that case"))
# return the actual contours array
return cnts
def is_cv2(or_better=False):
# grab the OpenCV major version number
major = get_opencv_major_version()
# check to see if we are using *at least* OpenCV 2
if or_better:
return major >= 2
# otherwise we want to check for *strictly* OpenCV 2
return major == 2
def is_cv3(or_better=False):
# grab the OpenCV major version number
major = get_opencv_major_version()
# check to see if we are using *at least* OpenCV 3
if or_better:
return major >= 3
# otherwise we want to check for *strictly* OpenCV 3
return major == 3
def is_cv4(or_better=False):
# grab the OpenCV major version number
major = get_opencv_major_version()
# check to see if we are using *at least* OpenCV 4
if or_better:
return major >= 4
# otherwise we want to check for *strictly* OpenCV 4
return major == 4
def get_opencv_major_version(lib=None):
# if the supplied library is None, import OpenCV
if lib is None:
import cv2 as lib
# return the major version number
return int(lib.__version__.split(".")[0])
def check_opencv_version(major, lib=None):
# this function may be removed in a future release as we now
# use the get_opencv_major_function to obtain the current OpenCV
# version and then perform the actual version check *within* the
# respective function
import warnings
message = """
The check_opencv_version function is deprecated and may be
removed in a future release. Use at your own risk.
"""
warnings.warn(message, DeprecationWarning, stacklevel=2)
# if the supplied library is None, import OpenCV
if lib is None:
import cv2 as lib
# return whether or not the current OpenCV version matches the
# major version number
return lib.__version__.startswith(major)
def build_montages(image_list, image_shape, montage_shape):
"""
---------------------------------------------------------------------------------------------
author: Kyle Hounslow
---------------------------------------------------------------------------------------------
Converts a list of single images into a list of 'montage' images of specified rows and columns.
A new montage image is started once rows and columns of montage image is filled.
Empty space of incomplete montage images are filled with black pixels
---------------------------------------------------------------------------------------------
:param image_list: python list of input images
:param image_shape: tuple, size each image will be resized to for display (width, height)
:param montage_shape: tuple, shape of image montage (width, height)
:return: list of montage images in numpy array format
---------------------------------------------------------------------------------------------
example usage:
# load single image
img = cv2.imread('lena.jpg')
# duplicate image 25 times
num_imgs = 25
img_list = []
for i in xrange(num_imgs):
img_list.append(img)
# convert image list into a montage of 256x256 images tiled in a 5x5 montage
montages = make_montages_of_images(img_list, (256, 256), (5, 5))
# iterate through montages and display
for montage in montages:
cv2.imshow('montage image', montage)
cv2.waitKey(0)
----------------------------------------------------------------------------------------------
"""
if len(image_shape) != 2:
raise Exception('image shape must be list or tuple of length 2 (rows, cols)')
if len(montage_shape) != 2:
raise Exception('montage shape must be list or tuple of length 2 (rows, cols)')
image_montages = []
# start with black canvas to draw images onto
montage_image = np.zeros(shape=(image_shape[1] * (montage_shape[1]), image_shape[0] * montage_shape[0], 3),
dtype=np.uint8)
cursor_pos = [0, 0]
start_new_img = False
for img in image_list:
if type(img).__module__ != np.__name__:
raise Exception('input of type {} is not a valid numpy array'.format(type(img)))
start_new_img = False
img = cv2.resize(img, image_shape)
# draw image to black canvas
montage_image[cursor_pos[1]:cursor_pos[1] + image_shape[1], cursor_pos[0]:cursor_pos[0] + image_shape[0]] = img
cursor_pos[0] += image_shape[0] # increment cursor x position
if cursor_pos[0] >= montage_shape[0] * image_shape[0]:
cursor_pos[1] += image_shape[1] # increment cursor y position
cursor_pos[0] = 0
if cursor_pos[1] >= montage_shape[1] * image_shape[1]:
cursor_pos = [0, 0]
image_montages.append(montage_image)
# reset black canvas
montage_image = np.zeros(shape=(image_shape[1] * (montage_shape[1]), image_shape[0] * montage_shape[0], 3),
dtype=np.uint8)
start_new_img = True
if start_new_img is False:
image_montages.append(montage_image) # add unfinished montage
return image_montages
def adjust_brightness_contrast(image, brightness=0., contrast=0.):
"""
Adjust the brightness and/or contrast of an image
:param image: OpenCV BGR image
:param contrast: Float, contrast adjustment with 0 meaning no change
:param brightness: Float, brightness adjustment with 0 meaning no change
"""
beta = 0
# See the OpenCV docs for more info on the `beta` parameter to addWeighted
# https://docs.opencv.org/3.4.2/d2/de8/group__core__array.html#gafafb2513349db3bcff51f54ee5592a19
return cv2.addWeighted(image,
1 + float(contrast) / 100.,
image,
beta,
float(brightness))