def detect1(img):
"""
:param img: Detection based on green filter + center of mass
:return:
"""
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
mask = cv2.inRange(hsv, HSV_G_low, HSV_G_upp)
imshow("d1:mask", mask.astype(np.uint8))
contours, hierarchy = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
""" get every contour point (=APPROX_NONE), so we can use perimeter to
approximate object center of mass"""
img_poly = img.copy()
for contour in contours:
for point in contour:
x,y = point[0] # each point is a [[x,y]] element. Just remove wrapping array
cv2.circle(img_poly, (x,y), 1, (255,0,255))
''' center from perimeter '''
m = cv2.moments(contour)
m00 = m['m00']
if m00 > 0:
cx = int(m['m10']/m00)
cy = int(m['m01']/m00)
cv2.circle(img_poly, (cx,cy), 2, (255,0,0), -1)
''' center from area '''
(x,y,w,h) = cv2.boundingRect(contour)
roi = mask[y:y+h,x:x+w]
m = cv2.moments(roi)
cx = int(m['m10']/m['m00'])
cy = int(m['m01']/m['m00'])
cv2.circle(img_poly, (x+cx,y+cy), 2, (0,0,255), -1)
imshow("d1:arrow vertex", img_poly)
def detect3(img, debug=True):
mark_list = detectGreenArrows(img)
if len(mark_list) == 0: return
if debug:
draw = img.copy()
for mark in mark_list:
cv2.drawContours(draw, [mark[2]], -1, (255,0,255))
cv2.circle(draw, mark[0], 2, (255,255,255), -1)
(x,y,w,h) = mark[1]
cv2.rectangle(draw, (x,y), (x+w,y+h), (100,100,100))
r = int(max(w,h)*YELLOW_SEARCH_AREA_RATIO)
cv2.circle(draw, mark[0], r, (255,255,255), 1)
imshow('d3: detectGreenArrows', draw)
corners_list = np.asarray(detectYellowCorners(img))
if len(corners_list) == 0: return
''' marriage problem'''
count=0
for mark in mark_list:
count+=1; print 'mark[%d]'%(count)
center = np.asarray(mark[0])
radius = max(mark[1][2], mark[1][3])
radius = np.ceil(radius*YELLOW_SEARCH_AREA_RATIO)
diff = corners_list - center
dist = np.linalg.norm(diff, axis=1)
condition = dist < radius
passing = corners_list[condition] # or condition.nonzero()
print 'passing corners:',len(passing)
if debug:
for point in passing:
cv2.circle(draw, tuple(point), 2, (255,255,255), -1)
imshow('d3: detectCorners', draw)
if len(passing) >= 4:
sorted = adrian_rosebrock.order_points(passing[0:4])
scale=200
ref = np.array([ (0,0), (1,0), (1,1), (0,1) ])*scale
tf = rectification.calculePerspectiveTransform(sorted, ref)
img_rect = cv2.warpPerspective(img, tf, (scale,scale))
if debug:
for point in passing:
cv2.circle(draw, tuple(point), 2, (255,255,255), -1)
imshow('d3: mark[%d]'%(count), img_rect)
def detect2(img):
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
mask = cv2.inRange(hsv, HSV_Y_low, HSV_Y_upp)
imshow("d2:mask", mask.astype(np.uint8))
''' median blur to drop salt noise
dilate op. to avoidmedian cutoff '''
k_cross = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
mask = cv2.dilate(mask, k_cross)
imshow("d2:dilate", mask)
mask = cv2.medianBlur(mask, 3)
imshow("d2:median", mask)
img_poly = img.copy()
detected_corners = []
contours, hierarchy = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
''' center from perimeter '''
m = cv2.moments(contour)
m00 = m['m00']
if m00 > 0:
cx = int(m['m10']/m00)
cy = int(m['m01']/m00)
cv2.circle(img_poly, (cx,cy), 3, (0,0,255), -1)
detected_corners.append( (cx,cy) )
imshow("d2:yellow corners", img_poly)
''' group marks '''
print 'yellow corners detected:', len(detected_corners)
if len(detected_corners) >= 4:
take4 = detected_corners[0:4]
''' sort it
use (0,0) distance to known tl and br points'''
tld = np.Inf
brd = 0
img_poly = img_poly.copy()
for point in take4:
cv2.circle(img_poly, point, 3, (255,0,0), -1)
d = np.linalg.norm(point)
if d<tld:
tld=d,
tl=point
if d>brd:
brd=d
br=point
take4.remove(tl)
take4.remove(br)
cv2.line(img_poly, tl, br, (0,255,255))
imshow("d2:points", img_poly)
''' use cross-product to detect side position '''
for pt in take4:
v1 = (br[0]-tl[0], br[1]-tl[1], 0)
v2 = (pt[0]-tl[0], pt[1]-tl[1], 0)
sign = np.cross(v1,v2)[2]
if sign < 0:
ne=pt
else:
po=pt
return
""" ne or po can be undefined. This is due:
1) 4 selected points could be bad (impossible configuration)
2) distance to (0,0) is not 100% stable to perspective nor rotations (like roll)
"""
sorted = np.array([ tl, po, br, ne ])
ref = np.array([ (0,0), (0,100), (100,100), (100,0) ])
tf = rectification.calculePerspectiveTransform(sorted, ref)
img_rect = cv2.warpPerspective(img, tf, (100,100))
imshow("d2:rectified", img_rect)
