def start(self):
cv2.namedWindow("opening...")
vc = cv2.VideoCapture(0)
detect_flag = False
videoflag = 0
rval, frame = vc.read()
s = time.time()
a = time.time()
tuples = [] # list of (image_ID, face, tag)
self.faces = []
while True:
if frame is not None:
pframe = cv2.flip(cv2.resize(frame, (960, 720)), 1)
dframe = cv2.resize(pframe, (480, 360)) # size ratio = 4
ratio = 2
p = Photo()
faces = detect_faces(self.detector, dframe)
faces = sorted(faces, key=lambda tup: tup[0])
for (r_x, r_y, r_w, r_h) in faces:
x, y, w, h = ratio*r_x, ratio*r_y, ratio*r_w, ratio*r_h
p.add_face(x, y, w, h)
# Draw a rectangle around the faces
cv2.rectangle(pframe, (x, y), (x+w, y+h), (255, 0, 0), 2)
p.image = pframe
p.extract_features()
self.faces = []
if len(p.faces) != 0:
X = np.array([f.feature for f in p.faces])
predY = self.model.predict(X)
for (i, f) in enumerate(p.faces):
if inv_label_maps[predY[i]] == 'T':
cv2.rectangle(pframe, (f.x, f.y), (f.x+f.w, f.y+f.h), (0, 255, 0), 2)
self.faces += [(f.x, f.y, f.w, f.h, inv_label_maps[predY[i]], None)]
cv2.imshow("camera", pframe)
rval, frame = vc.read()
oper = cv2.waitKey(1) & 0xFF
if oper == ord('p'):
t = time.time()
iname = datetime.datetime.fromtimestamp(t).strftime('%Y-%m-%d %H.%M.%S')
print iname
#iname = "/Users/GDog/Desktop/" + iname + ".png"
self.images = pframe
vc.release()
cv2.destroyAllWindows()
break
def start():
cv2.namedWindow("preview")
vc = cv2.VideoCapture(0)
videoflag = 0
rval, frame = vc.read()
s = time.time()
a = time.time()
while True:
if frame is not None:
pframe = cv2.flip(cv2.resize(frame, (960, 720)), 1)
p = Photo()
faces = detect_faces(detector, pframe)
faces = sorted(faces, key=lambda tup: tup[0])
for (x, y, w, h) in faces:
p.add_face(x, y, w, h)
# Draw a rectangle around the faces
cv2.rectangle(pframe, (x, y), (x+w, y+h), (255, 0, 0), 2)
p.image = pframe
p.extract_features()
if len(p.faces) != 0:
X = np.array([f.feature for f in p.faces])
predY = model.predict(X)
for (i, f) in enumerate(p.faces):
if inv_label_maps[predY[i]] == 'T':
cv2.rectangle(pframe, (f.x, f.y), (f.x+f.w, f.y+f.h), (0, 255, 0), 2)
cv2.imshow("image", pframe)
rval, frame = vc.read()
oper = cv2.waitKey(1) & 0xFF
if oper == ord('q'):
break
if oper == ord('p'):
t = time.time()
iname = datetime.datetime.fromtimestamp(t).strftime('%Y-%m-%d %H.%M.%S')
print iname
#iname = "/Users/GDog/Desktop/" + iname + ".png"
self.images += [pframe]
vc.release()
break
videoflag = 0 rval, frame = vc.read() s = time.time() a = time.time() while True: if frame is not None: #print frame.shape print "image extract:", time.time()-a a = time.time() pframe = cv2.flip(cv2.resize(frame, (960, 720)), 1) print "flip+resize: ", time.time()-a a = time.time() #detector part p = Photo() faces = detect_faces(detector, pframe) faces = sorted(faces, key=lambda tup: tup[0]) for (x, y, w, h) in faces: p.add_face(x, y, w, h) # Draw a rectangle around the faces cv2.rectangle(pframe, (x, y), (x+w, y+h), (255, 0, 0), 2) print "detect: ", time.time()-a a = time.time() #filter part p.image = pframe p.extract_features() if len(p.faces) != 0: X = np.array([f.feature for f in p.faces]) predY = model.predict(X)
from utils import detect_faces
from datasets import Photo, inv_label_maps
if __name__ == "__main__":
print "USAGE : python predict.py model_path (-d image_predfix)/(image_path) "
model_path = sys.argv[1]
if sys.argv[2] == "-d":
image_prefix = sys.argv[3]
image_list = [image_path for image_path in sorted(glob.glob(image_prefix + "/*.[Jj][Pp][Gg]"))]
else:
image_list = [sys.argv[2]]
with open(model_path, "rb") as f:
model = cPickle.load(f)
for image_path in image_list:
p = Photo()
# before applying the filter
image1 = cv2.imread(image_path)
image1 = cv2.resize(image1, (960, 720))
if image1 is None:
print "no image", image_path
continue
detector = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
faces = detect_faces(detector, image1)
print "find %d faces in image %s " % (len(faces), image_path)
faces = sorted(faces, key=lambda tup: tup[0])
for (x, y, w, h) in faces:
p.add_face(x, y, w, h)
# Draw a rectangle around the faces
cv2.rectangle(image1, (x, y), (x + w, y + h), (0, 255, 0), 2)
