diff --git a/.gitignore b/.gitignore index f1de5fe..a1ddd43 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,168 @@ -imutils/.DS_Store +# Created by https://www.gitignore.io + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover + +# Translations +*.mo +*.pot + +# Django stuff: +*.log + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm + +*.iml + +## Directory-based project format: +.idea/ +# if you remove the above rule, at least ignore the following: + +# User-specific stuff: +# .idea/workspace.xml +# .idea/tasks.xml +# .idea/dictionaries + +# Sensitive or high-churn files: +# .idea/dataSources.ids +# .idea/dataSources.xml +# .idea/sqlDataSources.xml +# .idea/dynamic.xml +# .idea/uiDesigner.xml + +# Gradle: +# .idea/gradle.xml +# .idea/libraries + +# Mongo Explorer plugin: +# .idea/mongoSettings.xml + +## File-based project format: +*.ipr +*.iws + +## Plugin-specific files: + +# IntelliJ +/out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties + + +### OSX ### .DS_Store -MANIFEST -dist +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +### Windows ### +# Windows image file caches +Thumbs.db +ehthumbs.db + +# Folder config file +Desktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msm +*.msp + +# Windows shortcuts +*.lnk + + +### Linux ### +*~ + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* diff --git a/demos/image_basics.py b/demos/image_basics.py index eb0d498..0d5e88a 100644 --- a/demos/image_basics.py +++ b/demos/image_basics.py @@ -1,5 +1,5 @@ -# author: Adrian Rosebrock -# website: http://www.pyimagesearch.com +# author: Adrian Rosebrock +# website: http://www.pyimagesearch.com # USAGE # BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND @@ -34,9 +34,9 @@ # 2. ROTATION # loop over the angles to rotate the image for angle in xrange(0, 360, 90): - # rotate the image and display it - rotated = imutils.rotate(bridge, angle=angle) - cv2.imshow("Angle=%d" % (angle), rotated) + # rotate the image and display it + rotated = imutils.rotate(bridge, angle=angle) + cv2.imshow("Angle=%d" % (angle), rotated) # wait for a keypress, then close all the windows cv2.waitKey(0) @@ -45,9 +45,9 @@ # 3. RESIZING # loop over varying widths to resize the image to for width in (400, 300, 200, 100): - # resize the image and display it - resized = imutils.resize(workspace, width=width) - cv2.imshow("Width=%dpx" % (width), resized) + # resize the image and display it + resized = imutils.resize(workspace, width=width) + cv2.imshow("Width=%dpx" % (width), resized) # wait for a keypress, then close all the windows cv2.waitKey(0) @@ -87,4 +87,4 @@ edgeMap = imutils.auto_canny(gray) cv2.imshow("Original", logo) cv2.imshow("Automatic Edge Map", edgeMap) -cv2.waitKey(0) \ No newline at end of file +cv2.waitKey(0) diff --git a/demos/image_paths.py b/demos/image_paths.py index c57620e..0afc0b9 100644 --- a/demos/image_paths.py +++ b/demos/image_paths.py @@ -4,4 +4,4 @@ # loop over the image paths in the previous 'demo_images' # directory and print the paths to the terminal for imagePath in paths.list_images("../demo_images"): - print imagePath \ No newline at end of file + print imagePath diff --git a/demos/perspective_transform.py b/demos/perspective_transform.py index f7772e4..7ad8c92 100644 --- a/demos/perspective_transform.py +++ b/demos/perspective_transform.py @@ -18,7 +18,7 @@ # loop over the points and draw them on the cloned image for (x, y) in pts: - cv2.circle(clone, (x, y), 5, (0, 255, 0), -1) + cv2.circle(clone, (x, y), 5, (0, 255, 0), -1) # apply the four point tranform to obtain a "birds eye view" of # the notecard @@ -27,4 +27,4 @@ # show the original and warped images cv2.imshow("Original", clone) cv2.imshow("Warped", warped) -cv2.waitKey(0) \ No newline at end of file +cv2.waitKey(0) diff --git a/demos/sorting_contours.py b/demos/sorting_contours.py index db33002..4ddd37e 100644 --- a/demos/sorting_contours.py +++ b/demos/sorting_contours.py @@ -1,5 +1,5 @@ -# author: Adrian Rosebrock -# website: http://www.pyimagesearch.com +# author: Adrian Rosebrock +# website: http://www.pyimagesearch.com # USAGE # BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND @@ -19,27 +19,27 @@ # find contours in the edge map (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, - cv2.CHAIN_APPROX_SIMPLE) + cv2.CHAIN_APPROX_SIMPLE) # loop over the (unsorted) contours and label them for (i, c) in enumerate(cnts): - orig = contours.label_contour(orig, c, i, color=(240, 0, 159)) + orig = contours.label_contour(orig, c, i, color=(240, 0, 159)) # show the original image cv2.imshow("Original", orig) # loop over the sorting methods for method in ("left-to-right", "right-to-left", "top-to-bottom", "bottom-to-top"): - # sort the contours - (cnts, boundingBoxes) = contours.sort_contours(cnts, method=method) - clone = image.copy() + # sort the contours + (cnts, boundingBoxes) = contours.sort_contours(cnts, method=method) + clone = image.copy() - # loop over the sorted contours and label them - for (i, c) in enumerate(cnts): - sortedImage = contours.label_contour(clone, c, i, color=(240, 0, 159)) + # loop over the sorted contours and label them + for (i, c) in enumerate(cnts): + sortedImage = contours.label_contour(clone, c, i, color=(240, 0, 159)) - # show the sorted contour image - cv2.imshow(method, sortedImage) + # show the sorted contour image + cv2.imshow(method, sortedImage) # wait for a keypress -cv2.waitKey(0) \ No newline at end of file +cv2.waitKey(0) diff --git a/imutils/contours.py b/imutils/contours.py index eaa36ab..22e54b4 100644 --- a/imutils/contours.py +++ b/imutils/contours.py @@ -1,43 +1,45 @@ -# author: Adrian Rosebrock -# website: http://www.pyimagesearch.com +# 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 + # 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 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 - # 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)) - # 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 - # 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 + # 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 diff --git a/imutils/convenience.py b/imutils/convenience.py index 0c677a2..345c3ff 100644 --- a/imutils/convenience.py +++ b/imutils/convenience.py @@ -1,5 +1,5 @@ -# author: Adrian Rosebrock -# website: http://www.pyimagesearch.com +# author: Adrian Rosebrock +# website: http://www.pyimagesearch.com # import the necessary packages import numpy as np @@ -7,114 +7,114 @@ 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])) + # 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 + # 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] + # 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) + # 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)) + # perform the rotation + M = cv2.getRotationMatrix2D(center, angle, scale) + rotated = cv2.warpAffine(image, M, (w, h)) - # return the rotated image - return rotated + # 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 + # 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 + # 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) + # 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) + # 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 + # 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) + # 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) + # 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 \ No newline at end of file + # return the edged image + return edged diff --git a/imutils/paths.py b/imutils/paths.py index 140fe7b..fd4eb3e 100644 --- a/imutils/paths.py +++ b/imutils/paths.py @@ -2,24 +2,24 @@ import os def list_images(basePath, contains=None): - # return the set of files that are valid - return list_files(basePath, validExts=(".jpg", ".jpeg", ".png"), contains=contains) + # return the set of files that are valid + return list_files(basePath, validExts=(".jpg", ".jpeg", ".png"), contains=contains) def list_files(basePath, validExts=(".jpg", ".jpeg", ".png"), contains=None): - # loop over the directory structure - for (rootDir, dirNames, filenames) in os.walk(basePath): - # loop over the filenames in the current directory - for filename in filenames: - # if the contains string is not none and the filename does not contain - # the supplied string, then ignore the file - if contains is not None and filename.find(contains) == -1: - continue + # loop over the directory structure + for (rootDir, dirNames, filenames) in os.walk(basePath): + # loop over the filenames in the current directory + for filename in filenames: + # if the contains string is not none and the filename does not contain + # the supplied string, then ignore the file + if contains is not None and filename.find(contains) == -1: + continue - # determine the file extension of the current file - ext = filename[filename.rfind("."):] + # determine the file extension of the current file + ext = filename[filename.rfind("."):] - # check to see if the file is an image and should be processed - if ext.endswith(validExts): - # construct the path to the image and yield it - imagePath = os.path.join(rootDir, filename).replace(" ", "\\ ") - yield imagePath \ No newline at end of file + # check to see if the file is an image and should be processed + if ext.endswith(validExts): + # construct the path to the image and yield it + imagePath = os.path.join(rootDir, filename).replace(" ", "\\ ") + yield imagePath diff --git a/imutils/perspective.py b/imutils/perspective.py index faacc29..f2f1322 100644 --- a/imutils/perspective.py +++ b/imutils/perspective.py @@ -1,67 +1,67 @@ -# author: Adrian Rosebrock -# website: http://www.pyimagesearch.com +# 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") + # 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)] + # 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)] + # 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 + # 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 + # 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 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)) + # 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") + # 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)) + # 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 \ No newline at end of file + # return the warped image + return warped diff --git a/setup.py b/setup.py index eac4d87..c22e8ba 100644 --- a/setup.py +++ b/setup.py @@ -1,14 +1,14 @@ from distutils.core import setup setup( - name = 'imutils', - packages = ['imutils'], - version = '0.2.1', - description = 'A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much more easier with OpenCV and Python.', - author = 'Adrian Rosebrock', - author_email = 'adrian@pyimagesearch.com', - url = 'https://github.com/jrosebr1/imutils', - download_url = 'https://github.com/jrosebr1/imutils/tarball/0.1', - keywords = ['computer vision', 'image processing', 'opencv', 'matplotlib'], - classifiers = [], + name='imutils', + packages=['imutils'], + version='0.2.1', + description='A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much more easier with OpenCV and Python.', + author='Adrian Rosebrock', + author_email='adrian@pyimagesearch.com', + url='https://github.com/jrosebr1/imutils', + download_url='https://github.com/jrosebr1/imutils/tarball/0.1', + keywords=['computer vision', 'image processing', 'opencv', 'matplotlib'], + classifiers=[], )