sight 0.1.3

A library for Vision Processing

sight

A library for Vision Processing

What is sight?

---

sight is really just a library built on top of OpenCV. It abstracts away all of the awful C-like garbage, and makes it look more rust-like. Here's what I mean.

If I want to read an image from a file, show the original image, convert it to HSV, apply a mask, apply a gaussian blur, draw a circle around the largest blob, convert it back to BGR, and output the image, that can be done very concisely, like this:

import cv2
from sight import *

image = cv2.imread("fuel.png", 1)


# show untouched image
# convert to HSV
original = Image(image).show("original").convert_to_hsv()

# HSV mask for neon yellow
mask = original.get_mask(
    [27, 100, 100],
    [40, 255, 255]
)

# apply mask
# blur 100%
# draw circle around the largest blob
# convert back to BGR
# show image
original.mask(mask).blur(1).draw_target(mask).convert_to_bgr().show("output")


wait_for_keypress()
exit()

See how easy that was?

You might be thinking that it's too high level to be useful. This is not so.

You can use methods like get_blobs, get_largest_blob, and several others to get the data you're looking for.

Documentation

---

# returns whether or not escape key pressed (True or False)
def escape_key_pressed()

# waits for keypress
def wait_for_keypress()

# destroys windows
def exit()


class Window:
    # name the window
    def __init__(self, name)

    # adds a slider to the window with a given name
    # set to the default value
    # and with a max value of max
    def add_slider(self, name, default=0, max=255)

    # gets the value of a slider by name
    def get_slider(self, name)

    # gets a list of all slider values in the order you
    # created the sliders
    def get_sliders(self)


class Image:    
    # Takes an opencv image (a numpy ndarray)
    def __init__(self, image)

    # returns a copy of the image that you
    # can edit without affecting the original
    def clone(self)

    # Shows this image under a given window name and a size
    def show(self, window_name, size=None)

    # Resizes the image to a given size
    def resize(self, size)

    # gets rid of specs and closes holes in image
    def smooth(self)

    # gaussian blur on image using a percentage
    def blur(self, blur_percentage)

    # filter image with mask
    def mask(self, mask)

    # draw text with bottom_corner location
    # (x and y are percentages of the screen width and height)
    # color and size are optional
    def draw_text(self, bottom_corner, text, color=(0, 255, 255), size=1)

    # draw circle with center location
    # (x and y are percentages of the screen width and height)
    # and radius on image
    # color and thickness are optional
    def draw_circle(self, center, radius, color=(0, 255, 255), thickness=10)

    # draws circle around the largest blob using a mask
    # color and thickness are optional
    def draw_target(self, mask, color=None, thickness=None)

    # draws circles around each blob using a mask
    # color and thickness are optional
    def draw_targets(self, mask, color=None, thickness=None)

    # returns image width
    def get_width(self)

    # returns image height
    def get_height(self)

    # returns image (width, height)
    def get_size(self)

    # get mask for values range a to b
    # a and b are both lists 3 values long
    # a is the lower limit for each channel in the image
    # b is the upper limit for each channel in the image
    def get_mask(self, a, b)

    # returns a list of (x, y, radius) for each blob using a mask
    def get_blobs(self, mask)

    # returns a (x, y, radius) for the largest blob using a mask
    def get_largest_blob(self, mask)

    # converts image to HSV image
    def convert_to_hsv(self)

    # converts image to Gray image
    def convert_to_gray(self)

    # converts image to BGR image
    def convert_to_bgr(self)

Install Dependencies

---

python3 -m pip install opencv-python
python3 -m pip install numpy

Future

---

I plan to develop this further to add features such as multithreaded, multiscaling template matching, and haarcascades. I also plan to add support for sliders.

Other than that there's not much else to add, the base code is super small yet powerful, and flexible to change.