def ProcessFrame(frame):
out = obj([False, 0, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], 255])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = (row * col) / 3
minArea = 200
red = cv2.medianBlur(red, ksize=3)
ret, thresh = cv2.threshold(red,
np.mean(red) - max(np.std(red), 10) * 2.65,
255, cv2.THRESH_BINARY_INV)
debugFrame("threshOg", thresh)
kernel = np.ones((7, 7), np.uint8)
thresh = cv2.erode(thresh, kernel, iterations=2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for cont in contours:
if len(cont) < 3:
continue
(center, (w, h), ang) = cv2.minAreaRect(cont)
area = w * h
aspectRatio = 0
if w != 0:
aspectRatio = h / w
if aspectRatio < 1 and aspectRatio != 0:
aspectRatio = 1 / aspectRatio
ang += 90
else:
aspectRatio = 1
#caluclating solidity
contArea = cv2.contourArea(cont)
if contArea < .1:
conArea = 1.0
if area == 0:
area = 1
solidity = contArea / (1.0 * area)
loc = (int(center[0]), int(center[1]))
stringOut = "%f" % (solidity)
#cv2.putText(frameOut, stringOut, loc, cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
if area > 1500 and aspectRatio > 3 and aspectRatio < 13 and solidity > .8:
angp = -1 * ang
#angp = angp - round((angp/180.0)) * 180
stringOut += ", ang: %d" % (angp)
cv2.drawContours(frameOut, [cont], -1, (0, 0, 0), 3)
out = obj([True, angp, center[0], center[1]])
frameOut = out.draw(frameOut)
cv2.putText(frameOut, stringOut, loc, cv2.FONT_HERSHEY_SIMPLEX, .5,
255)
print stringOut
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
out = obj()
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("ChannelOfInterest", r)
edges = cv2.Canny(r, std * 3.5, 1.2* std)
debugFrame("edges", edges)
lines = cv2.HoughLinesP(edges, 4, math.pi/180, 200, minLineLength = 100, maxLineGap = 50)
if isinstance(lines, list):
print "numLines: %d" % len(lines[0])
for line in lines[0]:
p1 = (line[0], line[1])
p2 = (line[2], line[3])
dx = p1[0] - p2[0]
dy = abs(p1[1] - p2[1])
theta = math.atan2(dy, dx)
if abs(theta - math.pi/2) < 10 *math.pi/180:
cv2.line(frameOut, p1, p2, (255, 0, 255), 5)
"""
Below is the param info for HoughCircles
image is the imaged being looked at by the function
method cv2.CV_HOUGH_GRADIENT is the only method available
dp the size of accumulator inverse relative to input image, dp = 2 means accumulator is half dim of input image
minDist minimum distance between detected circles, too small and mult neighbor circ founds, big and neighbor circ counted as same
param1 bigger value input into canny, unsure why it is useful therefore not using
param2 vote threshold
minRadius min Radius of circle to look for
maxRadius max Radius of circle to look for
"""
maxrad = 300
minrad = 10
step = 50
for radius in range(minrad + step, maxrad + 1, step):
circles = cv2.HoughCircles(image = r, method = cv2.cv.CV_HOUGH_GRADIENT, dp = .5, minDist = radius * 2, param2 = int((2 * radius * math.pi)/15), minRadius = radius - step, maxRadius = radius)
msg = "minRadius: %d, maxRadius %d" % (radius - step, radius)
if type(circles) != type(None):
print msg + " found: %d" % (len(circles))
for circ in circles[0,:]:
out.append(circ)
out.draw(frameOut)
else:
print msg + " no circ found"
frameOut = out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out
def ProcessFrame(frame):
print("called")
out = obj()
frameOut = frame.copy()
HEIGHT, WIDTH, _ = frame.shape
contImg = np.zeros((HEIGHT, WIDTH, 3), np.uint8)
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("red", r)
if std > 6:
edges = cv2.Canny(r, std * 1.8, std * 1.2)
else:
edges = cv2.Canny(r, 30, 20)
debugFrame("edges", edges)
lines = cv2.HoughLinesP(edges,
4,
math.pi / 180,
200,
minLineLength=100,
maxLineGap=50)
if isinstance(lines, list):
print "numLines: %d" % len(lines[0])
for line in lines[0]:
p1 = (line[0], line[1])
p2 = (line[2], line[3])
dx = p1[0] - p2[0]
dy = abs(p1[1] - p2[1])
theta = math.atan2(dy, dx)
if abs(theta - math.pi / 2) < 10 * math.pi / 180:
cv2.line(frameOut, p1, p2, (255, 0, 255), 5)
contours, _ = cv2.findContours(edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
dice = []
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
if area < WIDTH * HEIGHT / 2:
cv2.drawContours(contImg, contours, i, (255, 255, 255), 5)
x, y, w, h = cv2.boundingRect(contours[i])
if w * h > 700 and h != 0 and w / h < 2 and w / h > 1 / 2:
cv2.rectangle(frameOut, (x, y), (x + w, y + h), (0, 0, 255), 2)
dice.append([x + w / 2, y + h / 2, (w + h) / 2])
debugFrame("contours", contImg)
print "Contours: ", len(contours)
if (len(dice) >= 2):
for die in dice:
out.append(die)
frameOut = out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("ChannelOfInterest", r)
edges = cv2.Canny(r, std * 3.5, 1.2* std)
debugFrame("edges", edges)
lines = cv2.HoughLinesP(edges, 4, math.pi/180, 200, minLineLength = 100, maxLineGap = 50)
if lines != None:
print "numLines: %d" % len(lines[0])
for line in lines[0]:
p1 = (line[0], line[1])
p2 = (line[2], line[3])
dx = p1[0] - p2[0]
dy = abs(p1[1] - p2[1])
theta = math.atan2(dy, dx)
if abs(theta - math.pi/2) < 10 *math.pi/180:
cv2.line(frameOut, p1, p2, (255, 0, 255), 5)
"""
Below is the param info for HoughCircles
image is the imaged being looked at by the function
method cv2.CV_HOUGH_GRADIENT is the only method available
dp the size of accumulator inverse relative to input image, dp = 2 means accumulator is half dim of input image
minDist minimum distance between detected circles, too small and mult neighbor circ founds, big and neighbor circ counted as same
param1 bigger value input into canny, unsure why it is useful therefore not using
param2 vote threshold
minRadius min Radius of circle to look for
maxRadius max Radius of circle to look for
"""
maxrad = 300
minrad = 10
step = 50
for radius in range(minrad + step, maxrad + 1, step):
circles = cv2.HoughCircles(image = r, method = cv2.cv.CV_HOUGH_GRADIENT, dp = .5, minDist = radius * 2, param2 = int((2 * radius * math.pi)/20), minRadius = radius - step, maxRadius = radius)
msg = "minRadius: %d, maxRadius %d" % (radius - step, radius)
if circles != None:
print msg + " found: %d" % (len(circles))
for circ in circles[0,:]:
cv2.circle(frameOut, (int(circ[0]), int(circ[1])), circ[2], (0, 255, 255), 2)
cv2.circle(frameOut, (int(circ[0]), int(circ[1])), 2, (0, 0, 255), 2)
else:
print msg + " no circ found"
frameOut = out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out.dict()
def ProcessFrame(frame):
out = obj([False, 0, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], 255])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = (row * col)/3
minArea = 200
red = cv2.medianBlur(red, ksize = 3)
ret, thresh = cv2.threshold(red, np.mean(red) - max(np.std(red), 10) * 2.65, 255, cv2.THRESH_BINARY_INV)
debugFrame("threshOg", thresh)
kernel = np.ones((7,7),np.uint8)
thresh = cv2.erode(thresh,kernel, iterations = 2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cont in contours:
if len(cont) < 3:
continue
(center, (w, h), ang) = cv2.minAreaRect(cont)
area = w * h
aspectRatio = 0
if w != 0:
aspectRatio = h / w
if aspectRatio < 1 and aspectRatio != 0:
aspectRatio = 1/aspectRatio
ang+=90
else:
aspectRatio = 1
#caluclating solidity
contArea = cv2.contourArea(cont)
if contArea < .1:
conArea = 1.0
if area == 0:
area = 1
solidity = contArea / (1.0 * area)
loc = (int(center[0]), int(center[1]))
stringOut = "%f" % (solidity)
#cv2.putText(frameOut, stringOut, loc, cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
if area > 1500 and aspectRatio > 3 and aspectRatio < 13 and solidity > .8:
angp = -1 * ang
#angp = angp - round((angp/180.0)) * 180
stringOut += ", ang: %d" % (angp)
cv2.drawContours(frameOut, [cont], -1, (0,0, 0), 3)
out = obj([True, angp, center[0], center[1]])
frameOut = out.draw(frameOut)
cv2.putText(frameOut, stringOut, loc, cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
print stringOut
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
print("called")
out = obj()
frameOut = frame.copy()
HEIGHT,WIDTH,_ = frame.shape
contImg = np.zeros((HEIGHT,WIDTH,3), np.uint8)
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("red", r)
if std > 6:
edges = cv2.Canny(r, std * 1.8 , std * 1.2)
else:
edges = cv2.Canny(r, 30 , 20)
debugFrame("edges", edges)
lines = cv2.HoughLinesP(edges, 4, math.pi/180, 200, minLineLength = 100, maxLineGap = 50)
if isinstance(lines, list):
print "numLines: %d" % len(lines[0])
for line in lines[0]:
p1 = (line[0], line[1])
p2 = (line[2], line[3])
dx = p1[0] - p2[0]
dy = abs(p1[1] - p2[1])
theta = math.atan2(dy, dx)
if abs(theta - math.pi/2) < 10 *math.pi/180:
cv2.line(frameOut, p1, p2, (255, 0, 255), 5)
contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
dice = []
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
if area < WIDTH * HEIGHT / 2:
cv2.drawContours(contImg, contours, i, (255, 255, 255), 5)
x,y,w,h = cv2.boundingRect(contours[i])
if w * h > 700 and h != 0 and w/h < 2 and w/h > 1/2:
cv2.rectangle(frameOut,(x,y),(x+w,y+h),(0,0,255),2)
dice.append([x + w/2, y + h/2, (w+h) / 2])
debugFrame("contours", contImg)
print "Contours: ", len(contours)
if(len(dice) >= 2):
for die in dice:
out.append(die)
frameOut = out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], 255))
r = cv2.cvtColor(frameOut, cv2.COLOR_BGR2GRAY)
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
r = frame[:, :, 2]
debugFrame("red", r)
r = cv2.GaussianBlur(r, (7, 7), 0)
edges = cv2.Canny(r, std * 2.0, 1.3 * std)
debugFrame("edges", edges)
lines = cv2.HoughLines(edges, 3, math.pi / 180, 110)
poles = []
if lines != None:
print "numLines: %d" % len(lines[0])
for line in lines[0]:
r = line[0]
theta = line[1]
if (abs(theta - round((theta / math.pi)) * math.pi) <
3.0 * math.pi / 180.0):
a = math.cos(theta)
b = math.sin(theta)
x0 = a * r
y0 = b * r
pt1 = (int(x0 - 100 * b), int(y0 + 100 * a))
pt2 = (int(x0 + 100 * b), int(y0 - 100 * a))
poles.append(((x0, y0), pt1, pt2))
cv2.line(frameOut, pt1, pt2, (255, 0, 255), 5)
#filtering out the matching poles
poles = sorted(poles, key=sortPoles)
if len(poles) > 0:
gatePoles = [poles[0]]
for pole in poles:
if abs(pole[0][0] - gatePoles[-1][0][0]) > 80:
gatePoles.append(pole)
for pole in gatePoles:
cv2.line(frameOut, pole[1], pole[2], (0, 0, 255), 10)
if len(gatePoles) == 2:
out = obj([True, gatePoles[0][0][0], gatePoles[1][0][0]])
print "len Poles: %d, len gatePoles: %d" % (len(poles), len(gatePoles))
frameOut = out.draw(frameOut)
debugFrame("houghReg", frameOut)
print "-------------------------------"
return out.dict()
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], 255))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("red", r)
edges = cv2.Canny(r, std * 1.8, 1.2 * std)
debugFrame("edges", edges)
lines = cv2.HoughLinesP(edges,
4,
math.pi / 180,
145,
minLineLength=200,
maxLineGap=80)
poles = []
if lines != None:
print "numLines: %d" % len(lines[0])
for line in lines[0]:
p1 = (line[0], line[1])
p2 = (line[2], line[3])
dx = p1[0] - p2[0]
dy = abs(p1[1] - p2[1])
theta = math.atan2(dy, dx)
if abs(theta - math.pi / 2) < 10 * math.pi / 180:
cv2.line(frameOut, p1, p2, (255, 0, 255), 5)
poles.append((p1, p2))
#filtering out the matching poles
poles = sorted(poles, key=sortPoles)
if len(poles) > 0:
gatePoles = [poles[0]]
for pole in poles:
if abs(pole[0][0] - gatePoles[-1][0][0]) > 80:
gatePoles.append(pole)
for pole in gatePoles:
cv2.line(frameOut, pole[0], pole[1], (0, 0, 255), 10)
if len(gatePoles) == 2:
out = obj([True, gatePoles[0][0][0], gatePoles[1][0][0]])
print "len Poles: %d, len gatePoles: %d" % (len(poles), len(gatePoles))
frameOut = out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out.dict()
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
print "r mean: %d" % (mean[2])
r = dist(frame, (mean[0], mean[1], max(mean[2], 80)))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("red", r)
if std > 6:
edges = cv2.Canny(r, std * 2.0, std * 1.1)
else:
edges = cv2.Canny(r, 30 , 20)
debugFrame("edges", edges)
contours, _ = cv2.findContours(r, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
possiblePoles = []
for cont in contours:
cv2.drawContours(frame, [cont], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(cont)
area = w * h
if w > 0:
aspectRatio = h / w
if aspectRatio < 1 and aspectRatio != 0:
aspectRatio = 1/aspectRatio
ang+=90
else:
aspectRation = 20
angp = abs(ang)
angp = abs(angp - round((angp/180.0)) * 180)
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
if (angp < 10 or angp > 170) and area > minArea and area < maxArea and abs(aspectRatio) <= 2:
possiblePoles.append((cont, center))
possiblePoles = sorted(possiblePoles, key = sortPoles)
for pole in possiblePoles:
cv2.drawContours(frameOut, [pole[0]], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(pole[0])
aspectRatio = h / w
if aspectRatio < 1:
ang+=90
angp = abs(ang)
angp = abs(angp - round((angp/180)) * 180)
area = h * w
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
if len(possiblePoles) > 1:
prevPole = possiblePoles[0]
poles = [prevPole]
for pole in possiblePoles:
cv2.drawContours(frame, [pole[0]], -1, (0, 0, 255), 3)
diff = abs( pole[1][0] - prevPole[1][0])
if (diff > 20):
poles.append(pole)
prevPole = pole
if (len(poles) == 2):
left = poles[0]
right = poles[1]
out = obj([True, int(right[1][0]), int(left[1][0])])
frameOut = out.draw(frameOut)
cv2.drawContours(frameOut, [left[0], right[0]], -1, (0,0, 0), 3)
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
import time
#time.sleep(.1)
out = obj([False, 0, 0, 0, 0, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], 255])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = (row * col) / 3
minArea = 200
red = cv2.medianBlur(red, ksize=3)
ret, thresh = cv2.threshold(red,
np.mean(red) - max(np.std(red), 10) * 2.65,
255, cv2.THRESH_BINARY_INV)
debugFrame("threshOg", thresh)
kernel = np.ones((7, 7), np.uint8)
thresh = cv2.erode(thresh, kernel, iterations=2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
r = cv2.GaussianBlur(r, (9, 9), 0)
for i in range(len(contours)):
epsilon = 0.045 * cv2.arcLength(contours[i], True)
approx = cv2.approxPolyDP(contours[i], epsilon, True)
#check if shape triangle
cv2.fillConvexPoly(frameOut, approx, (255, 0, 0))
foundThreeOrFour = False
""""if the approximated contour is a triangle"""
if len(approx) == 3:
#foundThreeOrFour = True
"""Finding the point opposite of the hypotenuse"""
pts = []
for ptWrapper in approx:
pts.append(ptWrapper[0])
#find hypotenuse
maxLen = 0
maxIdx = 0
for j in range(3):
#print j
p1 = pts[j]
#print (j+1) %3
p2 = pts[(j + 1) % 3]
sideLen = math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
if sideLen > maxLen:
maxLen = sideLen
maxIdx = j
p1 = pts[maxIdx]
p2 = pts[(maxIdx + 1) % 3]
center = pts[(maxIdx + 2) % 3]
#cv2.circle(frameOut,(center[0], center[1]) , 5, color = (0,0,0), thickness = -1)
elif len(approx) == 4:
foundThreeOrFour = True
"""Figure out closest and furthest points"""
pts = []
for ptWrapper in approx:
pts.append(ptWrapper[0])
maxDist = 0
end1 = 0
end2 = 0
for i in range(0, len(pts)):
for j in range(i, len(pts)):
ptdist = math.sqrt((pts[i][0] - pts[j][0])**2 +
(pts[i][1] - pts[j][1])**2)
if (ptdist >= maxDist):
maxDist = ptdist
end1 = i
end2 = j
p1, p2 = pts[end1], pts[end2]
centerPts = []
for i in range(len(pts)):
if i != end1 and i != end2:
centerPts.append(pts[i])
center = ((centerPts[0][0] + centerPts[1][0]) / 2,
(centerPts[0][1] + centerPts[1][1]) / 2)
#out = obj([True, int(center[0]), int(center[1]), int(endPts[0][0]), int(endPts[0][1]), int(endPts[1][0]), int(endPts[1][1])])
#frameOut = out.draw(frameOut)
if foundThreeOrFour:
v1 = [p1[0] - center[0], p1[1] - center[1]]
v2 = [p2[0] - center[0], p2[1] - center[1]]
dot = v1[0] * v2[0] + v1[1] * v2[1]
"""Finding magnitude of 2 shorter sides and the angle opposite of the hypotenuse"""
mag1 = math.sqrt((v1[0])**2 + (v1[1])**2)
mag2 = math.sqrt((v2[0])**2 + (v2[1])**2)
#may increment mags and arg inside acos to be not 0 or in acos domain
if mag1 == 0:
mag1 += .001
if mag2 == 0:
mag2 += .001
angle = math.acos(.999 * dot / (mag1 * mag2)) * 180.0 / math.pi
"""
checks that the biggest angle of the triangle is roughly 130 degrees and that the sides are similar sizes
130 degrees corresponds to the 45 degree bend
"""
MAX_ANG_DIF = 12
MAX_LEN_DIF = 60
MIN_LEN = 60
TARGET_ANGLE = 130
if mag1 > 100 or mag2 > 100:
print "Angle, Mag1, Mag2: ", int(angle), int(mag1), int(mag2)
#print angle
if (abs(TARGET_ANGLE - abs(angle)) < MAX_ANG_DIF
) and abs(mag1 - mag2) < MAX_LEN_DIF and mag1 > MIN_LEN:
out = obj([
True,
int(center[0]),
int(center[1]),
int(p1[0]),
int(p1[1]),
int(p2[0]),
int(p2[1])
])
frameOut = out.draw(frameOut)
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
out = obj()
frameOut = frame.copy()
frame = norm(frame)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
print "m: %d, std %d" % (mean, std)
#r = frame[:, :, 2]
r = cv2.GaussianBlur(r, (9, 9), 0)
debugFrame("ChannelOfInterest", r)
edges = cv2.Canny(r, std * 2.5, 1.2* std)
debugFrame("edges", edges)
height, width = frame.shape[:2]
outImg = np.zeros((height,width,3), np.uint8)
#frameOut = outImg
"""
Below is the param info for HoughCircles
image is the imaged being looked at by the function
method cv2.CV_HOUGH_GRADIENT is the only method available
dp the size of accumulator inverse relative to input image, dp = 2 means accumulator is half dim of input image
minDist minimum distance between detected circles, too small and mult neighbor circ founds, big and neighbor circ counted as same
param1 bigger value input into canny, unsure why it is useful therefore not using
param2 vote threshold
minRadius min Radius of circle to look for
maxRadius max Radius of circle to look for
"""
""" Find and make list of circles in the image """
drawAvg = False
circleList = []
minDeg = 80.0
s = .8
d = 3.7
maxrad = 150
minrad = 30
step = 50
for radius in range(minrad + step, maxrad + 1, step):
circles = cv2.HoughCircles(image = r, method = cv2.cv.CV_HOUGH_GRADIENT, dp = 4.2, minDist = 2 * radius, param2 = int((2 * radius * math.pi)/8.94), minRadius = radius - step, maxRadius = radius)
msg = "minRadius: %d, maxRadius %d" % (radius - step, radius)
if type(circles) != type(None):
print msg + " found: %d" % (len(circles))
for circ in circles[0,:]:
circleList.append(circ)
if seeCircles:
cv2.circle(frameOut, (circ[0], circ[1]), circ[2], (0,0,0), 2, 8, 0 )
else:
print msg + " no circ found"
if len(circleList) > 2:
pts = []
for circle in circleList:
#out.append(circle)
if seeCircles:
cv2.circle(frameOut, (int(circle[0]), int(circle[1])), int(circle[2]), (255,0,0), 2, 8, 0 )
cv2.circle(frameOut, (int(circle[0]), int(circle[1])), 7, (0,0,0), 2, 8, 0 )
pts.append([circle[0], circle[1]])
contour = np.array(pts, dtype=np.int32)
(x,y), r = cv2.minEnclosingCircle(contour)
out.append([int(x), int(y), int(r)])
#cv2.circle(frameOut, (int(x), int(y)), int(r), (0,0,0), 2, 8, 0 )
frameOut = out.draw(frameOut)
out.draw(frameOut)
debugFrame("houghProb", frameOut)
print "-------------------------------"
return out
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], max(m[2] + 50, 255)])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = 30000
minArea = 1000
ret, thresh = cv2.threshold(red,
np.mean(red) - np.std(red) * 1.5, 255,
cv2.THRESH_BINARY_INV)
kernel = np.ones((5, 5), np.uint8)
kernel[:, 0:1] = 0
kernel[:, 3:5] = 0
debugFrame("threshOg", thresh)
thresh = cv2.erode(thresh, kernel, iterations=1)
thresh = cv2.dilate(thresh, kernel, iterations=2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
possiblePoles = []
for cont in contours:
#cv2.drawContours(frame, [cont], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(cont)
area = w * h
if w > 0:
aspectRatio = h / w
if aspectRatio < 1 and aspectRatio != 0:
aspectRatio = 1 / aspectRatio
ang += 90
else:
aspectRation = 20
angp = abs(ang)
angp = abs(angp - round((angp / 180.0)) * 180)
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])),
cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
if (
angp < 10 or angp > 170
) and area > minArea and area < maxArea and abs(aspectRatio - 9) <= 4:
possiblePoles.append((cont, center))
possiblePoles = sorted(possiblePoles, key=sortPoles)
for pole in possiblePoles:
cv2.drawContours(frameOut, [pole[0]], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(pole[0])
aspectRatio = h / w
if aspectRatio < 1:
ang += 90
angp = abs(ang)
angp = abs(angp - round((angp / 180)) * 180)
area = h * w
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
if len(possiblePoles) > 1:
prevPole = possiblePoles[0]
poles = [prevPole]
for pole in possiblePoles:
cv2.drawContours(frame, [pole[0]], -1, (0, 0, 255), 3)
diff = abs(pole[1][0] - prevPole[1][0])
if (diff > 20):
poles.append(pole)
prevPole = pole
if (len(poles) == 2):
left = poles[0]
right = poles[1]
out = obj([True, int(right[1][0]), int(left[1][0])])
frameOut = out.draw(frameOut)
cv2.drawContours(frameOut, [left[0], right[0]], -1, (0, 0, 0), 3)
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
out = obj([False, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], max(m[2] + 50, 255)])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = 30000
minArea = 1000
ret, thresh = cv2.threshold(red, np.mean(red) - np.std(red) * 1.5, 255, cv2.THRESH_BINARY_INV)
kernel = np.ones((5,5),np.uint8)
kernel[:, 0:1] = 0
kernel[:, 3:5] = 0
debugFrame("threshOg", thresh)
thresh = cv2.erode(thresh,kernel, iterations = 1)
thresh = cv2.dilate(thresh, kernel, iterations = 2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
possiblePoles = []
for cont in contours:
#cv2.drawContours(frame, [cont], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(cont)
area = w * h
if w > 0:
aspectRatio = h / w
if aspectRatio < 1 and aspectRatio != 0:
aspectRatio = 1/aspectRatio
ang+=90
else:
aspectRation = 20
angp = abs(ang)
angp = abs(angp - round((angp/180.0)) * 180)
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, 255)
if (angp < 10 or angp > 170) and area > minArea and area < maxArea and abs(aspectRatio - 9) <= 4:
possiblePoles.append((cont, center))
possiblePoles = sorted(possiblePoles, key = sortPoles)
for pole in possiblePoles:
cv2.drawContours(frameOut, [pole[0]], -1, (0, 0, 255), 3)
(center, (w, h), ang) = cv2.minAreaRect(pole[0])
aspectRatio = h / w
if aspectRatio < 1:
ang+=90
angp = abs(ang)
angp = abs(angp - round((angp/180)) * 180)
area = h * w
stringOut = "%d %d %d" % (angp, area, aspectRatio)
cv2.putText(frameOut, stringOut, (int(center[0]), int(center[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
if len(possiblePoles) > 1:
prevPole = possiblePoles[0]
poles = [prevPole]
for pole in possiblePoles:
cv2.drawContours(frame, [pole[0]], -1, (0, 0, 255), 3)
diff = abs( pole[1][0] - prevPole[1][0])
if (diff > 20):
poles.append(pole)
prevPole = pole
if (len(poles) == 2):
left = poles[0]
right = poles[1]
out = obj([True, int(right[1][0]), int(left[1][0])])
frameOut = out.draw(frameOut)
cv2.drawContours(frameOut, [left[0], right[0]], -1, (0,0, 0), 3)
debugFrame("out", frameOut)
return out
def ProcessFrame(frame):
import time
#time.sleep(.1)
out = obj([False, 0, 0, 0, 0, 0, 0])
frameOut = np.copy(frame)
frame = norm(frame)
m = cv2.mean(frame)
red = dist(frame, [m[0], m[1], 255])
debugFrame("red", red)
(row, col, pages) = frame.shape
maxArea = (row * col)/3
minArea = 200
red = cv2.medianBlur(red, ksize = 3)
ret, thresh = cv2.threshold(red, np.mean(red) - max(np.std(red), 10) * 2.65, 255, cv2.THRESH_BINARY_INV)
debugFrame("threshOg", thresh)
kernel = np.ones((7,7),np.uint8)
thresh = cv2.erode(thresh,kernel, iterations = 2)
debugFrame("thresh", thresh)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
mean, std = cv2.meanStdDev(frame)
r = dist(frame, (mean[0], mean[1], mean[2]))
mean, std = cv2.meanStdDev(r)
r = cv2.GaussianBlur(r, (9, 9), 0)
for i in range(len(contours)):
epsilon = 0.045*cv2.arcLength(contours[i],True)
approx = cv2.approxPolyDP(contours[i],epsilon,True)
#check if shape triangle
cv2.fillConvexPoly(frameOut, approx, (255,0,0))
foundThreeOrFour = False
""""if the approximated contour is a triangle"""
if len(approx) == 3:
#foundThreeOrFour = True
"""Finding the point opposite of the hypotenuse"""
pts = []
for ptWrapper in approx:
pts.append(ptWrapper[0])
#find hypotenuse
maxLen = 0
maxIdx = 0
for j in range(3):
#print j
p1 = pts[j]
#print (j+1) %3
p2 = pts[(j + 1)%3]
sideLen = math.sqrt( (p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2 )
if sideLen > maxLen:
maxLen = sideLen
maxIdx = j
p1 = pts[maxIdx]
p2 = pts[(maxIdx + 1) % 3]
center = pts[(maxIdx + 2) % 3]
#cv2.circle(frameOut,(center[0], center[1]) , 5, color = (0,0,0), thickness = -1)
elif len(approx) == 4:
foundThreeOrFour = True
"""Figure out closest and furthest points"""
pts = []
for ptWrapper in approx:
pts.append(ptWrapper[0])
maxDist = 0
end1 = 0
end2 = 0
for i in range(0,len(pts)):
for j in range(i,len(pts)):
ptdist = math.sqrt( (pts[i][0] - pts[j][0]) ** 2 + (pts[i][1] - pts[j][1]) ** 2 )
if(ptdist >= maxDist):
maxDist = ptdist
end1 = i
end2 = j
p1, p2 = pts[end1], pts[end2]
centerPts = []
for i in range(len(pts)):
if i != end1 and i != end2:
centerPts.append(pts[i])
center = ( (centerPts[0][0] + centerPts[1][0]) / 2, (centerPts[0][1] + centerPts[1][1]) / 2 )
#out = obj([True, int(center[0]), int(center[1]), int(endPts[0][0]), int(endPts[0][1]), int(endPts[1][0]), int(endPts[1][1])])
#frameOut = out.draw(frameOut)
if foundThreeOrFour:
v1 = [p1[0] - center[0], p1[1] - center[1]]
v2 = [p2[0] - center[0], p2[1] - center[1]]
dot = v1[0] * v2[0] + v1[1] * v2[1]
"""Finding magnitude of 2 shorter sides and the angle opposite of the hypotenuse"""
mag1 = math.sqrt( (v1[0]) ** 2 + (v1[1]) ** 2 )
mag2 = math.sqrt( (v2[0]) ** 2 + (v2[1]) ** 2 )
#may increment mags and arg inside acos to be not 0 or in acos domain
if mag1 == 0:
mag1 += .001
if mag2 == 0:
mag2 += .001
angle = math.acos(.999 * dot/(mag1 * mag2)) * 180.0 / math.pi
"""
checks that the biggest angle of the triangle is roughly 130 degrees and that the sides are similar sizes
130 degrees corresponds to the 45 degree bend
"""
MAX_ANG_DIF = 12
MAX_LEN_DIF = 60
MIN_LEN = 60
TARGET_ANGLE = 130
if mag1 > 100 or mag2 > 100:
print "Angle, Mag1, Mag2: ", int(angle), int(mag1), int(mag2)
#print angle
if( abs(TARGET_ANGLE - abs(angle)) < MAX_ANG_DIF ) and abs(mag1 - mag2) < MAX_LEN_DIF and mag1 > MIN_LEN:
out = obj([True, int(center[0]), int(center[1]), int(p1[0]), int(p1[1]), int(p2[0]), int(p2[1])])
frameOut = out.draw(frameOut)
debugFrame("out", frameOut)
return out
