grip_red.py

import cv2
import numpy
import math
from enum import Enum

class GripPipeline:
    """
    An OpenCV pipeline generated by GRIP.
    """
    
    def __init__(self):
        """initializes all values to presets or None if need to be set
        """

        self.__rgb_threshold_red = [144.46942446043167, 190.05942275042446]
        self.__rgb_threshold_green = [55.03597122302158, 114.29541595925298]
        self.__rgb_threshold_blue = [66.50179856115108, 112.13073005093379]

        self.rgb_threshold_output = None

        self.__find_contours_input = self.rgb_threshold_output
        self.__find_contours_external_only = True

        self.find_contours_output = None

        self.__filter_contours_contours = self.find_contours_output
        self.__filter_contours_min_area = 60.0
        self.__filter_contours_min_perimeter = 80.0
        self.__filter_contours_min_width = 0
        self.__filter_contours_max_width = 50.0
        self.__filter_contours_min_height = 40.0
        self.__filter_contours_max_height = 1000
        self.__filter_contours_solidity = [0, 100]
        self.__filter_contours_max_vertices = 1000000
        self.__filter_contours_min_vertices = 0
        self.__filter_contours_min_ratio = 0
        self.__filter_contours_max_ratio = 1000

        self.filter_contours_output = None


    def process(self, source0):
        """
        Runs the pipeline and sets all outputs to new values.
        """
        # Step RGB_Threshold0:
        self.__rgb_threshold_input = source0
        (self.rgb_threshold_output) = self.__rgb_threshold(self.__rgb_threshold_input, self.__rgb_threshold_red, self.__rgb_threshold_green, self.__rgb_threshold_blue)

        # Step Find_Contours0:
        self.__find_contours_input = self.rgb_threshold_output
        (self.find_contours_output) = self.__find_contours(self.__find_contours_input, self.__find_contours_external_only)

        # Step Filter_Contours0:
        self.__filter_contours_contours = self.find_contours_output
        (self.filter_contours_output) = self.__filter_contours(self.__filter_contours_contours, self.__filter_contours_min_area, self.__filter_contours_min_perimeter, self.__filter_contours_min_width, self.__filter_contours_max_width, self.__filter_contours_min_height, self.__filter_contours_max_height, self.__filter_contours_solidity, self.__filter_contours_max_vertices, self.__filter_contours_min_vertices, self.__filter_contours_min_ratio, self.__filter_contours_max_ratio)


    @staticmethod
    def __rgb_threshold(input, red, green, blue):
        """Segment an image based on color ranges.
        Args:
            input: A BGR numpy.ndarray.
            red: A list of two numbers the are the min and max red.
            green: A list of two numbers the are the min and max green.
            blue: A list of two numbers the are the min and max blue.
        Returns:
            A black and white numpy.ndarray.
        """
        out = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
        return cv2.inRange(out, (red[0], green[0], blue[0]),  (red[1], green[1], blue[1]))

    @staticmethod
    def __find_contours(input, external_only):
        """Sets the values of pixels in a binary image to their distance to the nearest black pixel.
        Args:
            input: A numpy.ndarray.
            external_only: A boolean. If true only external contours are found.
        Return:
            A list of numpy.ndarray where each one represents a contour.
        """
        if(external_only):
            mode = cv2.RETR_EXTERNAL
        else:
            mode = cv2.RETR_LIST
        method = cv2.CHAIN_APPROX_SIMPLE
        im2, contours, hierarchy =cv2.findContours(input, mode=mode, method=method)
        return contours

    @staticmethod
    def __filter_contours(input_contours, min_area, min_perimeter, min_width, max_width,
                        min_height, max_height, solidity, max_vertex_count, min_vertex_count,
                        min_ratio, max_ratio):
        """Filters out contours that do not meet certain criteria.
        Args:
            input_contours: Contours as a list of numpy.ndarray.
            min_area: The minimum area of a contour that will be kept.
            min_perimeter: The minimum perimeter of a contour that will be kept.
            min_width: Minimum width of a contour.
            max_width: MaxWidth maximum width.
            min_height: Minimum height.
            max_height: Maximimum height.
            solidity: The minimum and maximum solidity of a contour.
            min_vertex_count: Minimum vertex Count of the contours.
            max_vertex_count: Maximum vertex Count.
            min_ratio: Minimum ratio of width to height.
            max_ratio: Maximum ratio of width to height.
        Returns:
            Contours as a list of numpy.ndarray.
        """
        output = []
        for contour in input_contours:
            x,y,w,h = cv2.boundingRect(contour)
            if (w < min_width or w > max_width):
                continue
            if (h < min_height or h > max_height):
                continue
            area = cv2.contourArea(contour)
            if (area < min_area):
                continue
            if (cv2.arcLength(contour, True) < min_perimeter):
                continue
            hull = cv2.convexHull(contour)
            solid = 100 * area / cv2.contourArea(hull)
            if (solid < solidity[0] or solid > solidity[1]):
                continue
            if (len(contour) < min_vertex_count or len(contour) > max_vertex_count):
                continue
            ratio = (float)(w) / h
            if (ratio < min_ratio or ratio > max_ratio):
                continue
            output.append(contour)
        return output