import sys
from scipy import ndimage
import cv2
import utils
from svm import fetch_data
if len(sys.argv) == 1:
fetch_data()
exit()
elif len(sys.argv) > 2:
print("\nError: More Than One Saving Directory Specified\n")
exit()
else:
PROFILE_FOLDER_PATH = utils.create_profile_in_database(sys.argv[1])
DISPLAY_FACE_DIM = (128, 128)
SKIP_FRAME = 2 # the fixed skip frame
FRAME_SKIP_RATE = 0 # skip SKIP_FRAME frames every other frame
SCALE_FACTOR = 2 # used to resize the captured frame for face detection for faster processing speed
CURRENT_ROTATION_MAP = utils.get_rotation_map(0)
WEBCAM = cv2.VideoCapture(0)
RET, FRAME = WEBCAM.read() # get first frame
FRAME_SCALE = (int(FRAME.shape[1] / SCALE_FACTOR),
int(FRAME.shape[0] / SCALE_FACTOR)) # (y, x)
CROPPED_FACE = []
NUM_OF_FACE_TO_COLLECT = 128
NUM_OF_FACE_SAVED = 0
UNSAVED = True
cropped_face = []
num_of_face_to_collect = 150
num_of_face_saved = 0
# For saving face data to directory
profile_folder_path = None
if len(sys.argv) == 1:
print "\nError: No Saving Diectory Specified\n"
exit()
elif len(sys.argv) > 2:
print "\nError: More Than One Saving Directory Specified\n"
exit()
else:
profile_folder_path = ut.create_profile_in_database(sys.argv[1])
while ret:
key = cv2.waitKey(1)
# exit on 'q' 'esc' 'Q'
if key in [27, ord('Q'), ord('q')]:
break
# resize the captured frame for face detection to increase processing speed
resized_frame = cv2.resize(frame, frame_scale)
processed_frame = resized_frame
# Skip a frame if the no face was found last frame
if frame_skip_rate == 0:
faceFound = False
for rotation in current_rotation_map:
cropped_face = []
num_of_face_to_collect = 150
num_of_face_saved = 0
# For saving face data to directory
profile_folder_path = None
if len(sys.argv) == 1:
print "\nError: No Saving Diectory Specified\n"
exit()
elif len(sys.argv) > 2:
print "\nError: More Than One Saving Directory Specified\n"
exit()
else:
profile_folder_path = ut.create_profile_in_database(sys.argv[1])
while ret:
key = cv2.waitKey(1)
# exit on 'q' 'esc' 'Q'
if key in [27, ord("Q"), ord("q")]:
break
# resize the captured frame for face detection to increase processing speed
resized_frame = cv2.resize(frame, frame_scale)
processed_frame = resized_frame
# Skip a frame if the no face was found last frame
if frame_skip_rate == 0:
faceFound = False
for rotation in current_rotation_map: