def _detectAndEstimateDistance(self):
self._grayImage = cv2.cvtColor(self._image, cv2.COLOR_BGR2GRAY,
self._grayImage)
blobs = self._detector.detect(self._grayImage)
blobsForColors = {}
for blob in blobs:
centerXAsInt, centerYAsInt = \
(int(n) for n in blob.pt)
radiusAsInt = int(blob.size)
minX = max(0, centerXAsInt - radiusAsInt)
maxX = min(self._imageWidth, centerXAsInt + radiusAsInt)
minY = max(0, centerYAsInt - radiusAsInt)
maxY = min(self._imageHeight, centerYAsInt + radiusAsInt)
region = self._image[minY:maxY, minX:maxX]
# Get the region's dimensions, which may
# differ from the blob's diameter if the blob
# extends past the edge of the image.
h, w = region.shape[:2]
meanColor = region.reshape(w * h, 3).mean(0)
meanHue = ColorUtils.hueFromBGR(meanColor)
meanSaturation = ColorUtils.saturationFromBGR(meanColor)
if meanHue < 22.5 or meanHue > 337.5:
color = COLOR_Red
elif meanHue < 67.5:
if meanSaturation < 25.0:
color = COLOR_YellowWhite
else:
color = COLOR_AmberYellow
elif meanHue < 172.5:
color = COLOR_Green
elif meanHue < 277.5:
if meanSaturation < 25.0:
color = COLOR_BlueWhite
else:
color = COLOR_BluePurple
else:
color = COLOR_Pink
if color in blobsForColors:
blobsForColors[color] += [blob]
else:
blobsForColors[color] = [blob]
self._processBlobsForColors(blobsForColors)
self._enableOrDisableCalibrationButton()
def _updateSquareData(self, i, j):
x0 = i * self._squareWidth
x1 = x0 + self._squareWidth
y0 = j * self._squareWidth
y1 = y0 + self._squareWidth
# Find the two dominant colors in the square.
self._clusterer.fit(
self._board[y0:y1, x0:x1].reshape(
self._squareArea, 3))
# Find the proportion of the square's area that is
# occupied by the less dominant color.
freq = numpy.mean(self._clusterer.labels_)
if freq > 0.5:
freq = 1.0 - freq
# Find the distance between the dominant colors.
dist = ColorUtils.normColorDist(
self._clusterer.cluster_centers_[0],
self._clusterer.cluster_centers_[1])
self._squareFreqs[j, i] = freq
self._squareDists[j, i] = dist
def _updateScene(self):
success, self._scene = self._capture.read(self._scene)
if not success:
return False # Failure
# Use the red channel as grayscale.
self._sceneGray = ColorUtils.extractChannel(self._scene, 2,
self._sceneGray)
return True # Success
def _updateScene(self):
success, self._scene = self._capture.read(self._scene)
if not success:
return False # Failure
# Use the red channel as grayscale.
self._sceneGray = ColorUtils.extractChannel(
self._scene, 2, self._sceneGray)
return True # Success
def _updateSquareData(self, i, j):
x0 = i * self._squareWidth
x1 = x0 + self._squareWidth
y0 = j * self._squareWidth
y1 = y0 + self._squareWidth
# Find the two dominant colors in the square.
self._clusterer.fit(self._board[y0:y1,
x0:x1].reshape(self._squareArea, 3))
# Find the proportion of the square's area that is
# occupied by the less dominant color.
freq = numpy.mean(self._clusterer.labels_)
if freq > 0.5:
freq = 1.0 - freq
# Find the distance between the dominant colors.
dist = ColorUtils.normColorDist(self._clusterer.cluster_centers_[0],
self._clusterer.cluster_centers_[1])
self._squareFreqs[j, i] = freq
self._squareDists[j, i] = dist
