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diff --git a/imutils/contours.py b/imutils/contours.py
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+++ b/imutils/contours.py
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+# author:	Adrian Rosebrock
+# website:	http://www.pyimagesearch.com
+
+# import the necessary packages
+import cv2
+
+def sort_contours(cnts, method="left-to-right"):
+	# initialize the reverse flag and sort index
+	reverse = False
+	i = 0
+
+	# handle if we need to sort in reverse
+	if method == "right-to-left" or method == "bottom-to-top":
+		reverse = True
+
+	# handle if we are sorting against the y-coordinate rather than
+	# the x-coordinate of the bounding box
+	if method == "top-to-bottom" or method == "bottom-to-top":
+		i = 1
+
+	# construct the list of bounding boxes and sort them from top to
+	# bottom
+	boundingBoxes = [cv2.boundingRect(c) for c in cnts]
+	(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
+		key=lambda b:b[1][i], reverse=reverse))
+
+	# return the list of sorted contours and bounding boxes
+	return (cnts, boundingBoxes)
+
+def label_contour(image, c, i, color=(0, 255, 0), thickness=2):
+	# compute the center of the contour area and draw a circle
+	# representing the center
+	M = cv2.moments(c)
+	cX = int(M["m10"] / M["m00"])
+	cY = int(M["m01"] / M["m00"])
+ 
+	# draw the contour and label number on the image
+	cv2.drawContours(image, [c], -1, color, thickness)
+	cv2.putText(image, "#{}".format(i + 1), (cX - 20, cY), cv2.FONT_HERSHEY_SIMPLEX,
+		1.0, (255, 255, 255), 2)
+ 
+	# return the image with the contour number drawn on it
+	return image
\ No newline at end of file
diff --git a/imutils/contours.pyc b/imutils/contours.pyc
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diff --git a/imutils/convenience.py b/imutils/convenience.py
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--- /dev/null
+++ b/imutils/convenience.py
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+# author:	Adrian Rosebrock
+# website:	http://www.pyimagesearch.com
+
+# import the necessary packages
+import numpy as np
+import urllib
+import cv2
+
+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 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 = urllib.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
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diff --git a/imutils/convenience.pyc b/imutils/convenience.pyc
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diff --git a/imutils/perspective.py b/imutils/perspective.py
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+++ b/imutils/perspective.py
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+# author:	Adrian Rosebrock
+# website:	http://www.pyimagesearch.com
+
+# import the necessary packages
+import numpy as np
+import cv2
+
+def order_points(pts):
+	# initialize a list of coordinates that will be ordered
+	# such that the first entry in the list is the top-left,
+	# the second entry is the top-right, the third is the
+	# bottom-right, and the fourth is the bottom-left
+	rect = np.zeros((4, 2), dtype="float32")
+
+	# the top-left point will have the smallest sum, whereas
+	# the bottom-right point will have the largest sum
+	s = pts.sum(axis=1)
+	rect[0] = pts[np.argmin(s)]
+	rect[2] = pts[np.argmax(s)]
+
+	# now, compute the difference between the points, the
+	# top-right point will have the smallest difference,
+	# whereas the bottom-left will have the largest difference
+	diff = np.diff(pts, axis=1)
+	rect[1] = pts[np.argmin(diff)]
+	rect[3] = pts[np.argmax(diff)]
+
+	# return the ordered coordinates
+	return rect
+
+def four_point_transform(image, pts):
+	# obtain a consistent order of the points and unpack them
+	# individually
+	rect = order_points(pts)
+	(tl, tr, br, bl) = rect
+
+	# compute the width of the new image, which will be the
+	# maximum distance between bottom-right and bottom-left
+	# x-coordiates or the top-right and top-left x-coordinates
+	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
+	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
+	maxWidth = max(int(widthA), int(widthB))
+
+	# compute the height of the new image, which will be the
+	# maximum distance between the top-right and bottom-right
+	# y-coordinates or the top-left and bottom-left y-coordinates
+	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
+	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
+	maxHeight = max(int(heightA), int(heightB))
+
+	# now that we have the dimensions of the new image, construct
+	# the set of destination points to obtain a "birds eye view",
+	# (i.e. top-down view) of the image, again specifying points
+	# in the top-left, top-right, bottom-right, and bottom-left
+	# order
+	dst = np.array([
+		[0, 0],
+		[maxWidth - 1, 0],
+		[maxWidth - 1, maxHeight - 1],
+		[0, maxHeight - 1]], dtype="float32")
+
+	# compute the perspective transform matrix and then apply it
+	M = cv2.getPerspectiveTransform(rect, dst)
+	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+
+	# return the warped image
+	return warped
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