def measure(im, debug=False):
im2 = image.max_size(im, (800, 600))
b,g,r = image.split(im2)
#cv.EqualizeHist(r,r)
##cv.EqualizeHist(g,g)
##cv.EqualizeHist(b,b)
im2 = image.merge(b,g,r)
eyes = 0
#objs = []
#for cascade in eye_cascades:
# print cascade
# cascade = cv.Load(cascade)
# objs = filter_overlap(detect(im, cascade))
# draw_objects(im, objs, color=cv.RGB(0,255,0))
# eyes += len(objs)
#faces = 0
if debug:
im3 = cv.CloneImage(im2)
faces = []
for cascade in face_cascades:#(face_cascades[0],face_cascades[-1]):
cascade = cv.Load(cascade)
detected_faces = detect(im2, cascade)
faces += detected_faces
if debug:
for i,rect in enumerate(faces):
rect.draw(im3, color=cv.RGB(255,16*i,16*i))
if debug:
image.show(im3, "Faces + Repeats")
faces = filter_overlap(faces)
#print (objs[1], objs[6])
#draw_objects(im2, map(tuple, faces.keys()))
for rect, count in faces.iteritems():
rect.draw(im2, color=cv.RGB(255,0,0))
#print (objs[3],objs[13])
#draw_objects(im, filter_overlap((objs[3],objs[13])))
#objs = []
#for cascade in body_cascades:
# print cascade
# cascade = cv.Load(cascade)
# objs += detect(im, cascade)
#draw_objects(im, filter_overlap(objs), color=cv.RGB(0,0,255))
#objs = []
#for cascade in mouth_cascades:
# print cascade
# cascade = cv.Load(cascade)
# objs += detect(im, cascade)
#draw_objects(im, filter_overlap(objs), color=cv.RGB(255,0,255))
score = 0
for face_rect, count in faces.iteritems():
score += count * 0.25 + 0.15
print faces
if debug:
image.show(im2, "Faces")
return (im2, faces), score
v = image.new_from(l)
cv.Set(u, 0)
cv.Set(v, 0)
size = cv.GetSize(l)
print size
for x in range(256):
for y in range(size[1]):
cv.Set2D(u, y, x, x)
cv.Set2D(v, 255 - x, min(y, 255), x)
image.show(u, "U")
image.show(v, "V")
rgb = image.luv2rgb(image.merge(l, u, v))
r, g, b = image.split(rgb)
#xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY)
xor = image.Xor(u, v)
cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO)
image.show(rgb, "RGB")
image.show(xor, "Xor")
#cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb)
_, sat, _ = image.split(image.rgb2hsv(rgb))
image.show(sat, 'Saturation')
#cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4)))
cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb)
image.show(rgb, "Rgb - Xor")
v = image.new_from(l)
cv.Set(u, 0)
cv.Set(v, 0)
size = cv.GetSize(l)
print size
for x in range(256):
for y in range(size[1]):
cv.Set2D(u, y, x, x)
cv.Set2D(v, 255-x, min(y, 255), x)
image.show(u, "U")
image.show(v, "V")
rgb = image.luv2rgb(image.merge(l,u,v))
r,g,b = image.split(rgb)
#xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY)
xor = image.Xor(u,v)
cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO)
image.show(rgb, "RGB")
image.show(xor, "Xor")
#cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb)
_, sat, _ = image.split(image.rgb2hsv(rgb))
image.show(sat, 'Saturation')
#cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4)))
cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb)
image.show(rgb, "Rgb - Xor")
