adding shit

cueballfinder.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 # import cv2
 # import numpy
 # import math
@@ -164,6 +165,186 @@ def worker():
 #                 continue
 #             output.append(contour)
 #         return output
+=======
+import cv2
+import numpy as np
+import math
+from enum import Enum
+from flask import Flask
+import imutils
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    cue =  CueBallFinder();
+    image = 'images/pool'
+    cue.process(image)
+
+    for contour in output:
+        (x,y,w,h) = cv2.boundingRect(contour)
+        cv2.rectangle(image, (x,y), (x+w,y+h), (255, 0, 0), 2)
+
+    cv2.imshow("image", image)
+    cv2.waitKey(0)
+    return "Hello World"
+
+
+if __name__ == '__main__':
+    #app.run(host='130.215.218.85', debug=True)
+    cue =  CueBallFinder();
+    image = 'images/pool'
+    cue = cue.process(image)
+
+    for contour in cue:
+        (x,y,w,h) = cv2.boundingRect(contour)
+        cv2.rectangle(image, (x,y), (x+w,y+h), (255, 0, 0), 2)
+
+    cv2.imshow("image", image)
+    cv2.waitKey(0)
+    #return "Hello World"
+
+class CueBallFinder:
+    """
+    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 = [0.0, 255.0]
+        self.__rgb_threshold_green = [208.67805755395685, 255.0]
+        self.__rgb_threshold_blue = [0.0, 255.0]
+
+        self.rgb_threshold_output = None
+
+        self.__find_contours_input = self.rgb_threshold_output
+        self.__find_contours_external_only = False
+
+        self.find_contours_output = None
+
+        self.__filter_contours_contours = self.find_contours_output
+        self.__filter_contours_min_area = 250.0
+        self.__filter_contours_min_perimeter = 0
+        self.__filter_contours_min_width = 0
+        self.__filter_contours_max_width = 1000
+        self.__filter_contours_min_height = 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(source0, 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)
+
+        filtered= cv2.inRange(out, (red[0], green[0], blue[0]),  (red[1], green[1], blue[1]))
+
+        return filtered
+    @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(source0, 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 = []
+        # print input_contours
+        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)
+            # compute the center of the contour
+>>>>>>> 3a63603ef294b4d95432b0c69821c45b942067a5
 
+            M = cv2.moments(contour)
+            cX = int(M["m10"] / M["m00"])
+            cY = int(M["m01"] / M["m00"])
 
+        	# draw the contour and center of the shape on the
+            cv2.drawContours(source0, contour, -1, (0, 255, 0), 2)
+            cv2.circle(source0, (cX, cY), 3, (255, 0, 255), -1)
+            cv2.putText(source0, "x ="+str(cX)+", y ="+str(cY), (cX - 20, cY - 20),
+        		cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+            # cv2.drawContours(source0, output, 0, (0,255,0), 3)
 
+        return output

cueballfinder2.py

@@ -0,0 +1,149 @@
+import cv2
+import numpy
+import math
+from enum import Enum
+
+class CueBallFinder:
+    """
+    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 = [82.55395683453237, 255.0]
+        self.__rgb_threshold_green = [176.57374100719426, 255.0]
+        self.__rgb_threshold_blue = [0.0, 183.5653650254669]
+
+        self.rgb_threshold_output = None
+
+        self.__find_contours_input = self.rgb_threshold_output
+        self.__find_contours_external_only = False
+
+        self.find_contours_output = None
+
+        self.__filter_contours_contours = self.find_contours_output
+        self.__filter_contours_min_area = 250.0
+        self.__filter_contours_min_perimeter = 0
+        self.__filter_contours_min_width = 0
+        self.__filter_contours_max_width = 1000
+        self.__filter_contours_min_height = 0
+        self.__filter_contours_max_height = 1000
+        self.__filter_contours_solidity = [60.25179856115107, 100.0]
+        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(source0,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)
+        return self.__filter_contours(source0,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(source0,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 = []
+        center= []
+        x =0
+        y=0
+        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)
+
+            M = cv2.moments(contour)
+            cX = int(M["m10"] / M["m00"])
+            cY = int(M["m01"] / M["m00"])
+
+            cv2.drawContours(source0, contour, -1, (0, 255, 0), 2)
+            cv2.circle(source0, (cX, cY), 3, (255, 0, 255), -1)
+            cv2.putText(source0, "x ="+str(cX)+", y ="+str(cY), (cX - 20, cY - 20),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+            x=cX
+            y=cY
+        center = [x,y]
+        return center

main.py

@@ -0,0 +1,37 @@
+#from cueballfinder import CueBallFinder
+from cueballfinder2 import CueBallFinder
+import cv2
+import numpy as np
+def main():
+    # cue =  CueBallFinder();
+    #
+    #
+    # output = cue.process(img)
+    # # for contour in output:
+    # #     (x,y,w,h) = cv2.boundingRect(contour)
+    # #     cv2.rectangle(img, (x,y), (x+w,y+h), (255, 0, 0), 2)
+    #
+    # cv2.imshow("image", img)
+    # cv2.waitKey(0)
+
+
+
+    pipeline = CueBallFinder()
+    cap = cv2.VideoCapture('testVid.avi')
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        # import pdb;pdb.set_trace()
+        output= pipeline.process(frame)
+        cv2.imshow('frame',frame)
+        print output
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+
+if __name__=='__main__':
+    while True:
+        main()