vtools 0.0.35

Dependencies

OpenCV 3.0+ (required)

Python 3.6+ (required)

Mahotas (required)

scikit-image (required)

matplotlib (required, tested with 2+)

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! *