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/blobAreaDetector.py
index 8884c8e117908aaa00c1182b89319430a9e4bf3e..08829979e65356ac70e76ab6a902471c645b28ce 100644
--- 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/blobAreaDetector.py
@@ -2,11 +2,11 @@ import numpy as np
import cv2 as cv
import importlib
-Colors = importlib.import_module(".greyBlobDetector", "Colors")
-GreyBlobDetector = importlib.import_module(".greyBlobDetector", "GreyBlobDetector")
+Colors = importlib.import_module("greyBlobDetector", "Colors").Colors
+GreyBlobDetector = importlib.import_module("greyBlobDetector", "GreyBlobDetector").GreyBlobDetector
-class BlobAreaHSVDetector:
+class ContourAreaDetector:
# HSV value for red
RED_THRESHOLD = 8
@@ -22,14 +22,29 @@ class BlobAreaHSVDetector:
# gaussian blur is basically there to reduce the noise
if gaussian_blur:
# apply gaussian blur here
- pass
+ image = cv.GaussianBlur(image,(5,5),0)
- blobs = GreyBlobDetector.detect_color(image)
+ gd = GreyBlobDetector()
+ contours = gd.detect_color(image)
- if blobs == None:
+ if contours == None:
return Colors.GREY
else:
# since the picture with the blobs is black and white, it can act as a mask
# we also know that its either red or blue, so we can just check whether its colser to red
- # or to blue
- pass
\ No newline at end of file
+ # use the area since it would lessen the intergerance of neibouring pixels
+ mask = np.zeros(image.shape[0], image.shape[1], 1) # mask m x n x 1
+ # mask with all the points
+ cv.drawContours(mask, contours, 0, (255), cv.FILLED)
+ # 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]
+ average = colors.sum() / (colors != 0).sum()
+ if abs(average - ContourAreaDetector.RED_THRESHOLD) \
+ < abs(average - ContourAreaDetector.BLUE_THRESHOLD):
+ return Colors.RED
+ else:
+ return Colors.BLUE
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/blobEdgeDetector.py
index 3894a35393ced3ca391a76a550f17346f26c890c..afc0cef94e7ea76bcc9d5fad25b4865beea81e83 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/blobEdgeDetector.py
@@ -2,9 +2,8 @@ import numpy as np
import cv2 as cv
import importlib
-Colors = importlib.import_module(".greyBlobDetector", "Colors")
-GreyBlobDetector = importlib.import_module(".greyBlobDetector", "GreyBlobDetector")
-
+Colors = importlib.import_module("greyBlobDetector", "Colors").Colors
+GreyBlobDetector = importlib.import_module("greyBlobDetector", "GreyBlobDetector").GreyBlobDetector
class ContourEdgeDetector:
# HSV value for RED
@@ -23,7 +22,7 @@ class ContourEdgeDetector:
gd = GreyBlobDetector()
contours = gd.detect_color(image)
- if contours == None:
+ if len(contours) == 0:
return Colors.GREY
else:
# we know that its either blue or red
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
index 6fd5b46b0d61d6da88e2a5d3254cf5f0f698ca8a..9d5b2823aaaae5b4adb98e6bcd63480187b1b528 100644
--- 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
@@ -24,7 +24,6 @@ class GreyBlobDetector:
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
@@ -67,24 +66,22 @@ class GreyBlobDetector:
retained_contours = []
for contour in contours:
area = cv.contourArea(contour)
- circularity = 4*PI*area / (len(contour) * len(contour)) # there gotta be a better way to write this ...
+ # 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)
- if len(retained_contours) == 0:
- # no contour should be retained --> all LEDs are off
- # the other detectors will return Grey
- return None
- else:
- # these are the contours that supposedly represents the LEDs
- return retained_contours
+
+ return retained_contours
if __name__ == '__main__':
image = cv.imread("test.png")