def result(self, image_path, results, expected, compare=True):
fullpath = path = os.path.abspath(os.path.join('data', image_path))
if self.resized_dir is not None:
im = image.max_size(image.load(path), (320, 240))
path = os.path.join(self.resized_dir, os.path.basename(image_path))
print os.path.dirname(image_path)
image.save(im, path)
del im
self.write('<tr>')
self.write('<td rowspan="%(len)d"><a href="%(fullpath)s"><img src="%(path)s" width="320" /><br />%(imgpath)s</a></td>',
fullpath=fullpath, path=path, imgpath=image_path, len=len(results))
for i,pair in enumerate(results.iteritems()):
name,result = pair
if i != 0:
self.write("</tr><tr>")
self.write('<td>%(name)s</td><td>%(score)r</td>',
name=name, score=result[1])
self.write('<td class="%(cls)s">%(confirmed)s</td><td>%(expected)s</td>',
cls="bad" if compare and result[2] != (name in expected) else "good",
confirmed="Yes" if result[2] else "",
expected="Yes" if name in expected else "")
self.write('<td class="%(cls)s">%(timing).2f second(s)</td>',
cls="bad" if compare and result[3] > 10 else "",
timing=result[3])
self.write("</tr>")
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
def load(self, max_size=None):
try:
im = image.load(self.imgpath)
except IOError:
return None
if max_size is not None:
im = image.max_size(im, max_size)
return im
def load(self, max_size=None):
try:
im = image.load(self.imgpath)
except IOError:
return None
if max_size is not None:
im = image.max_size(im, max_size)
return im
