From 61ca9d8a4c93bb8700189e1797a63efe3d19c9bc Mon Sep 17 00:00:00 2001
From: Rui Jie Li <rui-jie.li@polymtl.ca>
Date: Thu, 25 Feb 2021 17:14:02 -0500
Subject: [PATCH] separated the files in black white, edges, and color
detection
---
.../find_contour_kmeans/black_and_white.py | 47 ++++++
.../find_contour_kmeans/edgeDetector.py | 134 ------------------
.../find_contour_kmeans/edge_detector.py | 103 ++++++++++++++
.../{hsvDetector.py => hsv_detector.py} | 37 ++---
4 files changed, 170 insertions(+), 151 deletions(-)
create mode 100644 robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/black_and_white.py
delete mode 100644 robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edgeDetector.py
create mode 100644 robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edge_detector.py
rename robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/{hsvDetector.py => hsv_detector.py} (65%)
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/black_and_white.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/black_and_white.py
new file mode 100644
index 0000000..7106586
--- /dev/null
+++ b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/black_and_white.py
@@ -0,0 +1,47 @@
+import cv2 as cv
+import numpy as np
+
+
+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 BlackWhiteTransform:
+ # For iterations with kmeans
+ def __init__(self, percentage_led_area = 0.01, nb_color = 4, max_iter = 1, accuracy = 1):
+ self.percetage_led_area = percentage_led_area
+ self.nb_color = nb_color
+ self.max_iter = max_iter
+ self.accuracy = accuracy
+
+ def convert_to_black_white(self, image, already_grayscale = False):
+ # Transform the image into grascale, then use kmeans to group the different shades of
+ # Grey
+
+ min_area = image.shape[0] * image.shape[1] * self.percetage_led_area
+
+ grey = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+ shape = grey.shape
+
+ grey = grey.reshape(grey.shape[0] * grey.shape[1], 1)
+ grey = np.float32(grey)
+
+ # Criteria is the number of iterations
+ # Format : (type, max_iteration, accuracy)
+ criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER,\
+ self.max_iter, self.accuracy)
+
+ ret,label,center=cv.kmeans(grey,self.nb_color,None,\
+ criteria,1,cv.KMEANS_RANDOM_CENTERS)
+
+ center = np.uint8(center)
+ res = center[label.flatten()]
+ kmeans = res.reshape(shape)
+
+ black_white = cv.threshold(kmeans, np.average(grey), 255, cv.THRESH_BINARY)[1]
+ return black_white
\ No newline at end of file
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edgeDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edgeDetector.py
deleted file mode 100644
index 3fca25f..0000000
--- a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edgeDetector.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import cv2 as cv
-import matplotlib.pyplot as plt
-from math import pi as PI
-# for debugging purposes
-gsize = None
-ginertia = None
-ls = []
-
-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 EdgeDetector:
- # threshold for circularity (circle = 1); TBD
- # circularity = 4 * PI * AREA / (PERIMETER ^ 2)
- # TODO: change this according to the size of the image
- # in general, decrease this more bigger images
- CIRCLE_THRESHOLD = 0.4 # 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)
- # TODO : change this according to the size of the image
- # in general, inertia is bigger
- MIN_INERTIA = 3 # 1.1 1.3 pour les petits images
- MAX_INERTIA = 5.5
-
- MIN_AREA = None # to be initialized to 0.02 * area of image
- # for iterations with kmeans
- NB_COLOR = 4 # allows for : black, white, blue, red
- MAX_ITER = 1 # maximum of 1 iterations
- ACCURACY = 1 # not sure what this does ...
-
- def __init__(self):
- pass
-
- def calculate_inertia(self, image, shape) -> int:
- pass
-
- # 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
- EdgeDetector.MIN_AREA = image.shape[0] * image.shape[1] * 0.01
- grey = image
- if not already_grayscale:
- grey = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
- shape = grey.shape
- debug(grey)
- # O(N) --> loop over every pixel
- grey = grey.reshape(grey.shape[0] * grey.shape[1], 1)
- # O(N) --> loop over every pixel
- grey = np.float32(grey)
-
- # criteria is the number of iterations
- # format : (type, max_iteration, accuracy)
- criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER,\
- EdgeDetector.MAX_ITER, EdgeDetector.ACCURACY)
-
- ret,label,center=cv.kmeans(grey,EdgeDetector.NB_COLOR,None,\
- criteria,1,cv.KMEANS_RANDOM_CENTERS)
-
- center = np.uint8(center)
- res = center[label.flatten()]
- kmeans = res.reshape(shape)
-
- # O(N) --> iterate over every pixels
- black_white = cv.threshold(kmeans, np.average(grey), 255, cv.THRESH_BINARY)[1]
- 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 >= EdgeDetector.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
- if inertia < 1:
- inertia = 1/inertia
- # note : the angle is pretty much useless
- print("calculated circularity : ", circularity)
- print("threshold circularity : ", EdgeDetector.CIRCLE_THRESHOLD)
- print()
- print("calculated converxity : ", convexity)
- print("threshold converxity : ", EdgeDetector.CONVEX_THRESHOLD)
- print()
- print("calculated inertia : ", inertia)
- print("threshold inertia : ", EdgeDetector.MIN_INERTIA, EdgeDetector.MAX_INERTIA)
- print()
- print("image area : ", image.shape[0] * image.shape[1])
- print("calculated area : ", area)
- print()
- print("detected angle : ", theta)
- print("displaying contour, press any key to continue ... ")
- debug_contours([contour], image)
- if circularity > EdgeDetector.CIRCLE_THRESHOLD and \
- convexity > EdgeDetector.CONVEX_THRESHOLD and \
- inertia > EdgeDetector.MIN_INERTIA and \
- inertia < EdgeDetector.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__':
- import os
- image = cv.imread("test9.PNG")
- gbd = EdgeDetector()
- print(len(gbd.find_contours(image)))
\ No newline at end of file
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edge_detector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edge_detector.py
new file mode 100644
index 0000000..1c94e7c
--- /dev/null
+++ b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/edge_detector.py
@@ -0,0 +1,103 @@
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from math import pi as PI
+from black_and_white import BlackWhiteTransform
+import logging
+
+logger = logging.getLogger(__name__)
+
+# For debugging purposes
+gsize = None
+ginertia = None
+ls = []
+
+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 EdgeDetector:
+ # Threshold for circularity (circle = 1); to be determined
+ # Circularity = 4 * PI * AREA / (PERIMETER ^ 2)
+ # In general, decrease this more bigger images
+ # Based on test values of 0.72, 0.8, 0.68 seems like 0.4 is a good value
+ # Threshold for concavity; the formula is area_of_blob / area_convexity_hull
+ # Based on values around 0.95 and 0.98
+
+ def __init__(self, circle_threshold = 0.4, convex_threshold = 0.85, min_inertia = 3, max_intertia = 5.5, percentage_led_area = 0.01):
+ self.circle_threshold = circle_threshold
+ self.convex_threshold = convex_threshold
+ self.min_inertia = min_inertia
+ self.max_inertia = max_intertia
+ self.percentage_led_area = percentage_led_area
+
+ # Finds the contours of the LEDs and returns them
+ def get_edges(self, black_white_image, area = False, distance = False, already_grayscale = False):
+
+ contours, hierarchy = cv.findContours(black_white_image, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+ min_area = black_white_image.shape[0] * black_white_image.shape[1] * self.percentage_led_area
+ # 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 >= 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
+ if inertia < 1:
+ inertia = 1/inertia
+ # Note : the angle is pretty much useless
+ logger.debug("calculated circularity : ", circularity)
+ logger.debug("threshold circularity : ", self.circle_threshold)
+ logger.debug("\n")
+ logger.debug("calculated converxity : ", convexity)
+ logger.debug("threshold converxity : ", self.convex_threshold)
+ logger.debug("\n")
+ logger.debug("calculated inertia : ", inertia)
+ logger.debug("threshold inertia : ", self.min_inertia, self.max_inertia)
+ logger.debug("\n")
+ logger.debug("image area : ", black_white_image.shape[0] * black_white_image.shape[1])
+ logger.debug("calculated area : ", area)
+ logger.debug("\n")
+ logger.debug("detected angle : ", theta)
+ logger.debug("displaying contour, press any key to continue ... ")
+ debug_contours([contour], black_white_image)
+ # Make it able to switch on and off (ex. do not consider circularity)
+ if circularity > self.circle_threshold and \
+ convexity > self.convex_threshold and \
+ inertia > self.min_inertia and \
+ inertia < self.max_inertia:
+ retained_contours.append(contour)
+ logger.debug("retained !")
+ else:
+ logger.debug("rejected !")
+ logger.debug("----------------------------------")
+ # Use HSV instead
+ # 1. color = red or blue
+ # Use the other 2 to see if its gray
+ return retained_contours
+
+if __name__ == '__main__':
+ import os
+ logger.setLevel("DEBUG") # Debug, info, warning, erreur
+ image = cv.imread("test11.PNG")
+ gbd = EdgeDetector()
+ logger.debug(len(gbd.find_contours(image)))
\ No newline at end of file
diff --git a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsvDetector.py b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsv_detector.py
similarity index 65%
rename from robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsvDetector.py
rename to robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsv_detector.py
index 3caec7d..6f0cdbe 100644
--- a/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsvDetector.py
+++ b/robots-at-robots/research/robots_at_robots/armor_color/find_contour_kmeans/hsv_detector.py
@@ -1,6 +1,10 @@
import cv2 as cv
import numpy as np
-import edgeDetector
+from edge_detector import EdgeDetector
+from black_and_white import BlackWhiteTransform
+import logging
+
+logger = logging.getLogger(__name__)
def debug(image):
cv.imshow("debug", image)
@@ -8,15 +12,12 @@ def debug(image):
class HSVColorDetector:
- RED = 180
- BLUE = 45
-
- MIN_GRAY = 60 # not enough testing, but around 100 for normal, 40 for gray
+ def __init__(self, red = 180, blue = 45, min_gray = 60):
+ self.red = red
+ self.blue = blue
+ self.min_gray = min_gray
- def __init__(self):
- pass
-
- def detect_color(self, image, is_hsv = False):
+ def detect_color(self, image, contours, shape, is_hsv = False):
# WARNING: greyscale is NOT the same as the value in HSV
# the formula for BGR to grayscale is
# Gray = 0.299*Red + 0.587*Green + 0.114*Blue
@@ -32,10 +33,7 @@ class HSVColorDetector:
# note : do not use sine or cosine for this, its like 10x slower than
# just doing adding or substracting
hue = np.abs(180 - hue) # due to angles looping back
- edge = edgeDetector.EdgeDetector()
-
- contours = edge.find_contours(greyscale, already_grayscale=True)
- mask = np.zeros(greyscale.shape)
+ mask = np.zeros(shape)
mask = cv.drawContours(mask, contours, -1, (255), 1)
hue_mask = np.ma.array(data=hue, mask=mask)
sat_mask = np.ma.array(data=saturation, mask=mask)
@@ -44,19 +42,24 @@ class HSVColorDetector:
satvalue = sat_mask.mean()
print(huevalue)
print(satvalue)
- if (abs(huevalue - HSVColorDetector.BLUE) < abs(huevalue - HSVColorDetector.RED)):
+ if (abs(huevalue - self.blue) < abs(huevalue - self.red)):
print("this is probably blue")
else:
print("this is probably red")
- if satvalue < HSVColorDetector.MIN_GRAY:
+ if satvalue < self.min_gray:
print("this is probably gray")
else:
print("this is probably not gray")
if __name__ == '__main__':
- image = cv.imread("test8.PNG")
+ logger.setLevel("DEBUG")
+ image = cv.imread("test11.PNG")
+ bwt = BlackWhiteTransform()
+ black_white_image = bwt.convert_to_black_white(image)
+ ed = EdgeDetector()
+ edges = ed.get_edges(black_white_image)
hsv = HSVColorDetector()
- hsv.detect_color(image)
+ hsv.detect_color(image, edges, black_white_image.shape)
--
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