/
hsvthreshold.py
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hsvthreshold.py
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import sys
sys.path.append('/users/sujit/libs/libfreenect/wrappers/python/')
import freenect
import cv2
import numpy as np
import math
use_webcam = False
if use_webcam:
c = cv2.VideoCapture(0)
c.open(0)
params = {
"huel": 0,
"hueh": 255,
"satl": 0,
"sath": 255,
"luml": 40,
"lumh": 255
}
"""params = {
"huel": 0,
"hueh": 255,
"satl": 0,
"sath": 255,
"luml": 0,
"lumh": 255
}"""
"""params = {
"hueh": 19,
"huel": 7,
"luml": 103,
"lumh": 255,
"sath": 206,
"satl": 71
}"""
"""params = {
"hueh": 74,
"huel": 52,
"luml": 29,
"lumh": 83,
"sath": 255,
"satl": 119
}"""
width = 640
height = 480
tolerance = 0.1
#imgpath = "wpitest.jpg"
imgpath = ""
comps = None
cam = np.array([
[594.21480358642339, 0.0, 339.30546187516956],
[0.0, 591.04092248505947, 242.73843891390746],
[0.0, 0.0, 1.0]
])
dis = np.array([-0.26389095690190378, 0.99983033880181316, -0.00076323952014484080, 0.0050337278410637169, -1.3056496956879815])
target = np.array([
[-15.5, -7.25, 0],
[-15.5, 7.25, 0],
[15.5, 7.25, 0],
[15.5, -7.25, 0]
])
def getimg_webcam():
_, img = c.read()
return img
def getimg_irkinect():
raw_data, _ = freenect.sync_get_video(0, freenect.VIDEO_IR_8BIT)
#depth, _ = freenect.sync_get_depth()
#print depth.scn
return cv2.cvtColor(np.array(raw_data), cv2.COLOR_GRAY2BGR)
def getimg():
if use_webcam:
return getimg_webcam()
else:
return getimg_irkinect()
def donothing(img):
return img
def hsvthreshold(img):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lowerb = np.array([params['huel'], params['satl'], params['luml']],np.uint8)
upperb = np.array([params['hueh'], params['sath'], params['lumh']],np.uint8)
threshed = cv2.inRange(hsv, lowerb, upperb)
return threshed
def brightnessthreshold(img):
_, ret = cv2.threshold(img, 200, 255, cv2.THRESH_TOZERO)
return ret
def onchange(prop):
def ret(x):
params[prop] = x
cv2.setTrackbarPos(prop, 'processing', x)
return ret
def blurred(img):
return hsvthreshold(cv2.GaussianBlur(img, (11, 11), 5))
def contours(img):
#img = blurred(img)
#img = cv2.erode(hsvthreshold(img), cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
img = hsvthreshold(img)
ret = cv2.merge([img, img, img])
cons, hier = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
targets = []
#cv2.drawContours(ret, np.array(cons), -1, np.array([255.0, 255.0, 0.0]), -1)
for i in range(len(cons)):
if cv2.contourArea(cons[i]) >= 1000:
#print len(cons[i])
#print len(cv2.convexHull(cons[i]))
#cons[i] = cv2.convexHull(cons[i])
cv2.drawContours(ret, np.array(cons), i, np.array([255.0, 0.0, 0.0]), 1)
pts = []
for pt in cons[i]:
pts.append(pt[0])
#ret[pt[0][1]][pt[0][0]] = np.array([255.0, 0.0, 0.0])
corn = corners(pts, ret)
if len(corn) == 4:
rvec, tvec = cv2.solvePnP(target,np.array(corn[:]),cam,dis)
print np.linalg.norm(tvec)
#target = pts[:,1].astype(np.double).sum() / len(pts)
for x, y in corn:
cv2.circle(ret, (int(x), int(y)), 5, np.array([0.0, 0.0, 255.0]), -1)
return ret
def corners(pts, img):
pts = np.array(pts)
cx = pts[:,0].astype(np.double).sum() / len(pts)
cy = pts[:,1].astype(np.double).sum() / len(pts)
deltas = pts.astype(np.double) - [[cx, cy]] * len(pts)
dists = np.sqrt(deltas[:,0] ** 2 + deltas[:,1] ** 2)
angles = np.arctan2(deltas[:,0], deltas[:,1])
stacked = np.column_stack([deltas, dists, angles])
sorted = stacked[np.lexsort(stacked.transpose())]
ret = []
l = len(sorted)
sides = []
side = []
for i in range(l):
perfect = True
#print "%f\t%f" % (sorted[i][3], sorted[i][2])
side.append(sorted[i][:2] + [cx, cy])
for k in range(1, 15):
next = (i + k) % l
prev = (i - k) % l
if not (sorted[i][2] > sorted[next][2] and sorted[i][2] > sorted[prev][2]):
perfect = False
#print "%f, %f, %f" % (sorted[prev][2], sorted[i][2], sorted[next][2])
if perfect:
ret.append(sorted[i][:2] + [cx, cy])
sides.append(side[:])
side = []
if len(ret) > 4:
ret = []
break
if len(sides) > 0:
sides[0].extend(side)
if len(ret) == 4:
for i in range(len(sides)):
x1 = sides[i][0][0]
y1 = sides[i][0][1]
x2 = sides[i][len(sides[i]) - 1][0]
y2 = sides[i][len(sides[i]) - 1][1]
for n in range(len(sides[i])):
x = sides[i][n][0]
y = sides[i][n][1]
a = (y2 - y1) / (x2 - x1)
# Slope of the xy sides
b = -1
# Downward Direction
c = -a * x1 + y1
# Equation of distance between x and y
dist = math.fabs(a * x + b * y + c)/math.sqrt(a**2 + b**2)
# maximum distance of a rectangle
if dist >= 100:
ret = []
img[sides[i][n][1]][sides[i][n][0]] = np.array([(i) * 100 % 255, (i + 1) * 100 % 255, (i+2) * 100 % 255])
"""for quad in range(4):
best = quad * l / 4
for i in range(quad * l / 4 + 1, (quad + 1) * l / 4):
if sorted[i][2] > sorted[best][2]:
best = i
ret.append(sorted[best][:2] + [cx, cy])"""
return ret
def loop(processimg):
if not use_webcam:
ctx = freenect.init()
dev = freenect.open_device(ctx, 0)
freenect.set_tilt_degs(dev, 10)
freenect.close_device(dev)
cv2.namedWindow('processing')
for k, v in params.iteritems():
cv2.createTrackbar(k, 'processing', v, 255, onchange(k))
runonce = True
while runonce:
#runonce = False
if imgpath != "":
img = cv2.imread(imgpath)
else:
img = getimg()
cv2.imshow('processing', cv2.resize(processimg(img), (width, height)))
char = cv2.waitKey(10)
if char == 27:
break
elif char == ord('p'):
for k, v in params.iteritems():
print "%s: %d" % (k, v)
#freenect.set_tilt_degs(dev, pid)
cv2.destroyAllWindows()
loop(contours)