vtools 0.0.32

Visual Tools - an object oriented approach to image processing and analysis.

vimg README rev.002 2017/6/02 This library is a project that is the result of my foray into the realm of computer vision. This project is a direct result of exploring and thinking about a highly simple and intuitive way to create an image object, and then easily be able to perform a powerful set of methodological analyses on that object, making routine tasks like thresholding and contouring a simple endeavor following an object oriented approach.

I want to pay complete homage to Dr. Adrian Rosebrock in many ways for the content of this package. His website is http://www.pyimagesearch.com/ . I’ve read his book and his blog posts about OpenCV for a long time and this package is a direct result from the knowledge that I have gained while and since doing so. This package borrows/adapts some of the work that Dr. Rosebrock has written in his ‘imutils’ package located here: https://pypi.python.org/pypi/imutils

The goal of this package is to integrate these tools into an object oriented interface that extends the np.ndarray class with methods and properties to create a simple image manipulation and analysis interface similar to the functional interface that Dr. Rosebrock’s imutils package provides.

Dependencies

OpenCV 3.0+ (required) Python 3.6+ (required) Mahotas (required) matplotlib (tested with 2+, required to visualize histograms)

Install vtools

From Source

You should be able to clone this repository in to a directory (ex: vtools) and run setup.py:

cd vtools && python setup.py install

From PyPI

pip install vtools

Getting Started

thresholding (simple binary) an image before vtools’ vImg class:

# Read in the image

image = cv2.imread(‘../images/trex.png’)

# Convert to grayscale and apply gaussian blur

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Set gaussian blur k (size of weighted mean area), # must be odd so there’s a center pixel

k = 3 gauss = cv2.GaussianBlur(gray, (k,k), 0)

# Now set the threshold level, T

T = 215

# Next, apply the threshold to the image

thresh = cv2.threshold(gauss, T, 255, cv2.THRESH_BINARY_INV)[1]

thresholding (simple binary) an image using vtools.vImg:

image = vImg(‘../images/trex.png’) thresh = image.threshold(215)

note: currently the only required variable is for T, but k (defaults to 5) and inverse (bool, defaults to True) are also available as named parameters.

The vContour class:

calculating contours and evaluating contour properties before vtools.vimg:

image = cv2.imread(‘quiz1.png’) _, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) hullImage = np.zeros(gray.shape[:2], dtype=”uint8”)

# loop over the contours

for (i, c) in enumerate(cnts):

# compute the area of the contour along with the bounding box # to compute the aspect ratio

print(f’Contour {i} type({type(c)})’) area = cv2.contourArea(c) (x, y, w, h) = cv2.boundingRect(c) x2, y2 = x + w, y + h

# compute the aspect ratio of the contour, which is simply the width # divided by the height of the bounding box

aspectRatio = w / float(h)

# use the area of the contour and the bounding box area to compute # the extent

extent = area / float(w * h)

# compute the convex hull of the contour, then use the area of the # original contour and the area of the convex hull to compute the # solidity

hull = cv2.convexHull(c) hullArea = cv2.contourArea(hull) solidity = area / float(hullArea)

# compute the center (tuple)

center = ((x + x2) / 2, (self. + y2) / 2)

# visualize the original contours and the convex hull and initialize # the name of the shape

cv2.drawContours(hullImage, [hull], -1, 255, -1) cv2.drawContours(image, [c], -1, (240, 0, 159), 3)

print(f’Shape #{i}: Aspect Ratio is {aspectRatio:.2f}, hull area is {hullArea:.2f}, ‘ f’solidity is {solidity:.2f}, extent is {extent:.2f}, center is {center}’)

Evaluating contours for usefulness with vtools’ vImg, vContour, and vContours classes:

img = vImg(“images/test.png”)

# outline each contour one by one and print simple and advanced contour properties # allowing you to easily determine whether contours may be useful to your CV application

img.gray().evalContours()

# the evalContours() method defaults to using the vImg simpleContours function with default parameters, # but you can also supply your own calculated contour values (in the form of a list of vContours)

Histograms with vtools’ vImg

* Coming Soon! *