diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/greyBlobDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour/greyBlobDetector.py
deleted file mode 100644
index 9d5b2823aaaae5b4adb98e6bcd63480187b1b528..0000000000000000000000000000000000000000
--- a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/greyBlobDetector.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import numpy as np
-import cv2 as cv
-import matplotlib.pyplot as plt
-
-PI = 3.141592653589793238
-
-class Colors:
- RED = "RED"
- BLUE = "BLUE"
- GREY = "GREY"
-
-class GreyBlobDetector:
- # we know that if an armor is either blue or red, it must be white except on the edges
- # since the shape does not change, we can try to find white blobs with the desired features
-
- # threshold to be considered white; 0 = black 255 = white, TBD
- WHITE_THRESHOLD = 235
- # threshold for circularity (circle = 1); TBD
- # circularity = 4 * PI * AREA / (PERIMETER ^ 2)
- CIRCLE_THRESHOLD = 0.5
- # threshold for concavity; the formula is area_of_blob / area_convexity_hull, TBD
- CONVEX_THRESHOLD = 0.99
- # threshold for how elongated it is (called intertia in opencv), TBD (circle = 1)
- INERTIA_THRESHOLD = 0.2
- # unless a robot got flipped, the lines should be pretty vertical
- ANGLE_THRESHOLD = 30
- # these parameters are for the shape of the image, not sure if needed or not
- WIDTH_RATIO = 0.2 # width LED / width image, TBD
- HEIGHT_RATIO = 0.67 # height LED / height image, TBD
- DIST_LEDS_RATIO = 0.67 # distance of the 2 LEDs / width of image, TBD
-
-
-
- def __init__(self):
- pass
-
- def calculate_max_area(self, image):
- # this basically calcultes the min / max area
- # false by default unless the ai that finds the armors is consistant enough with the
- # picture horizontal and vertical ratio
- x, y = image.shape
- return (x * GreyBlobDetector.WIDTH_RATIO)*(y * GreyBlobDetector.HEIGHT_RATIO)
-
- def calculate_min_area(self, image):
- # needed here becase part of the vertical bars may be hidden
- return self.calculate_max_area(image) / 1.5 # TBD
-
- def calculate_distance(self, image):
- # only use this if there are 2 or more blobs detected
- x, y = image.shape
- return y * GreyBlobDetector.DIST_LEDS_RATIO # TBD
-
- # finds the contours of the LEDs and returns them
- def detect_color(self, image, area = False, distance = False, already_grayscale = False):
- # transform the image into black and white
- # first go to grayscale then goto black white with a threshold
- grey = image
- if not already_grayscale:
- grey = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
- black_white = cv.threshold(grey, GreyBlobDetector.WHITE_THRESHOLD, 255, cv.THRESH_BINARY)[1]
-
- # do find countours on the black white image
- contours, hierarchy = cv.findContours(black_white, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
- # note : other versions of OpenCV will require image, contours, hierarchy
- # now for the characteristics of the contours
- retained_contours = []
- for contour in contours:
- area = cv.contourArea(contour)
- # circularity < threshold : is the contour NOT circular?
- circularity = 4*PI*area / (len(contour) * len(contour))
- # convexity > threashold : we want the shape to be convex
- convexity = area / cv.contourArea(cv.convexHull(contour))
- # opencv docs aren't very helpful on this but looks like inertia is aspect ratio
- (x,y),(width,height),theta = cv.minAreaRect(contour)
- # this is basically how e
- inertia = width / height
-
- if circularity < GreyBlobDetector.CIRCLE_THRESHOLD and \
- convexity > GreyBlobDetector.CONVEX_THRESHOLD and \
- inertia < GreyBlobDetector.INERTIA_THRESHOLD and \
- theta < GreyBlobDetector.ANGLE_THRESHOLD:
- retained_contours.append(contour)
-
- return retained_contours
-
-if __name__ == '__main__':
- image = cv.imread("test.png")
-
\ No newline at end of file
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/test.png b/robots-at-robots/research/robots_at_robots/armor_color/find_contour/test.png
deleted file mode 100644
index 55aa331ad1a426651e699d05f6b2f359fe0bc9fd..0000000000000000000000000000000000000000
Binary files a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/test.png and /dev/null differ
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/README b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/README
similarity index 100%
rename from robots-at-robots/research/robots_at_robots/armor_color/find_contour/README
rename to robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/README
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/blobAreaDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/blobAreaDetector.py
similarity index 100%
rename from robots-at-robots/research/robots_at_robots/armor_color/find_contour/blobAreaDetector.py
rename to robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/blobAreaDetector.py
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/blobEdgeDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/blobEdgeDetector.py
similarity index 93%
rename from robots-at-robots/research/robots_at_robots/armor_color/find_contour/blobEdgeDetector.py
rename to robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/blobEdgeDetector.py
index afc0cef94e7ea76bcc9d5fad25b4865beea81e83..8a54c39a58aed54978d28669e30653a039defdf8 100644
--- a/robots-at-robots/research/robots_at_robots/armor_color/find_contour/blobEdgeDetector.py
+++ b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/blobEdgeDetector.py
@@ -27,14 +27,14 @@ class ContourEdgeDetector:
else:
# we know that its either blue or red
# use the edges as a mask
- mask = np.zeros(image.shape[0], image.shape[1])
+ mask = np.zeros((image.shape[0], image.shape[1]))
cv.drawContours(mask, contours, -1, (255), 1)
# applies the mask to image
image[mask != 0] = (0,0,0)
# transform this to hsv
hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
# note : 0,0,0 will still be 0,0,0
- colors = hsv[:,:,0]
+ colors = hsv[:,:,0]
average = colors.sum() / (colors != 0).sum()
if abs(average - ContourEdgeDetector.RED_THRESHOLD) \
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/greyBlobDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/greyBlobDetector.py
new file mode 100644
index 0000000000000000000000000000000000000000..c02ae9db0c859bb4ea44a7ea4062728f1ccf78a7
--- /dev/null
+++ b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_blackwhite/greyBlobDetector.py
@@ -0,0 +1,127 @@
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+# reaplce with math.pi
+PI = 3.141592653589793238
+
+class Colors:
+ RED = "RED"
+ BLUE = "BLUE"
+ GREY = "GREY"
+
+def debug(image):
+ cv.imshow("debug", image)
+ cv.waitKey(0)
+def debug_contours(contours, image):
+ mask = np.zeros((image.shape[0], image.shape[1]))
+ cv.drawContours(mask, contours, -1, (255), 1)
+ cv.imshow("debug", mask)
+ cv.waitKey(0)
+
+class GreyBlobDetector:
+ # we know that if an armor is either blue or red, it must be white except on the edges
+ # since the shape does not change, we can try to find white blobs with the desired features
+
+ # threshold to be considered white; 0 = black 255 = white, TBD
+ # based on a rather dim image, might change if there's an algorithm to calculate this
+ WHITE_THRESHOLD = 70
+
+ # threshold for circularity (circle = 1); TBD
+ # circularity = 4 * PI * AREA / (PERIMETER ^ 2)
+ CIRCLE_THRESHOLD = 0.55 # based on test values of 0.72, 0.8, 0.68
+
+ # threshold for concavity; the formula is area_of_blob / area_convexity_hull, TBD
+ CONVEX_THRESHOLD = 0.85 # based on values around 0.95 and 0.98
+
+ # threshold for how elongated it is (called intertia in opencv), TBD (circle = 1)
+ MIN_INERTIA = 1.1
+ MAX_INERTIA = 3.5
+
+ # these parameters are for the shape of the image, not sure if needed or not
+ WIDTH_RATIO = 0.2 # width LED / width image, TBD
+ HEIGHT_RATIO = 0.67 # height LED / height image, TBD
+ DIST_LEDS_RATIO = 0.67 # distance of the 2 LEDs / width of image, TBD
+
+ MIN_AREA = None # to be initialized to 0.02 * area of image
+
+ def __init__(self):
+ pass
+
+ def calculate_max_area(self, image):
+ # this basically calcultes the min / max area
+ # false by default unless the ai that finds the armors is consistant enough with the
+ # picture horizontal and vertical ratio
+ x, y = image.shape
+ return (x * GreyBlobDetector.WIDTH_RATIO)*(y * GreyBlobDetector.HEIGHT_RATIO)
+
+ def calculate_min_area(self, image):
+ # needed here becase part of the vertical bars may be hidden
+ return self.calculate_max_area(image) / 1.5 # TBD
+
+ def calculate_distance(self, image):
+ # only use this if there are 2 or more blobs detected
+ x, y = image.shape
+ return y * GreyBlobDetector.DIST_LEDS_RATIO # TBD
+
+ # finds the contours of the LEDs and returns them
+ def find_contours(self, image, area = False, distance = False, already_grayscale = False):
+ # transform the image into black and white
+ # first go to grayscale then goto black white with a threshold
+ GreyBlobDetector.MIN_AREA = image.shape[0] * image.shape[1] * 0.02
+ grey = image
+ if not already_grayscale:
+ grey = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+ # 0 = noir; 255 = blanc; threshold = 180
+ # use HSV instead of cv.threshold
+ black_white = cv.threshold(grey, GreyBlobDetector.WHITE_THRESHOLD, 255, cv.THRESH_BINARY)[1]
+ # do find countours on the black white image
+ contours, hierarchy = cv.findContours(black_white, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+ # note : other versions of OpenCV will require image, contours, hierarchy
+ # now for the characteristics of the contours
+
+ retained_contours = []
+ for contour in contours:
+ # TODO : something wrong here, probably with area
+ area = cv.contourArea(contour)
+ if area >= GreyBlobDetector.MIN_AREA:
+ # circularity < threshold : is the contour NOT circular?
+ circularity = 4*PI*area / (cv.arcLength(contour, True) * cv.arcLength(contour, True))
+ # convexity > threashold : we want the shape to be convex
+ convexity = area / cv.contourArea(cv.convexHull(contour))
+ # opencv docs aren't very helpful on this but looks like inertia is aspect ratio
+ (x,y),(width,height),theta = cv.minAreaRect(contour)
+ # this is basically aspect ratio
+ inertia = width / height
+ # note : the angle is pretty much useless
+
+ print("calculated circularity : ", circularity)
+ print("threshold circularity : ", GreyBlobDetector.CIRCLE_THRESHOLD)
+ print()
+ print("calculated converxity : ", convexity)
+ print("threshold converxity : ", GreyBlobDetector.CONVEX_THRESHOLD)
+ print()
+ print("calculated inertia : ", inertia)
+ print("threshold inertia : ", GreyBlobDetector.MIN_INERTIA, GreyBlobDetector.MAX_INERTIA)
+ print()
+ print("minimal area : ", GreyBlobDetector.MIN_AREA)
+ print("calculated area : ", area)
+ # cv.SimpleBlobDetector
+ if circularity > GreyBlobDetector.CIRCLE_THRESHOLD and \
+ convexity > GreyBlobDetector.CONVEX_THRESHOLD and \
+ inertia > GreyBlobDetector.MIN_INERTIA and \
+ inertia < GreyBlobDetector.MAX_INERTIA:
+ retained_contours.append(contour)
+ print("retained !")
+ else:
+ print("rejected !")
+ print("----------------------------------")
+
+ # use HSV instead
+ # 1. color = red or blue
+ # use the other 2 to see if its gray
+ return retained_contours
+
+if __name__ == '__main__':
+ image = cv.imread("test6.PNG")
+ gbd = GreyBlobDetector()
+ print(len(gbd.find_contours(image)))
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