def main(input_img):
pil_image = stringToImage(input_img[input_img.find(",")+1:])
bgr_image = np.array(pil_image)
img, faces, face_features = features.extract_image_features(bgr_image)
estimated_gazes = gaze.test_faces(img, faces, face_features)
return lib.to_output_json(faces, face_features, estimated_gazes)
def main(input_img):
pil_image = stringToImage(input_img[input_img.find(",") + 1:])
bgr_image = np.array(pil_image)
img, faces, face_features = features.extract_image_features(bgr_image)
estimated_gazes = gaze.test_faces(img, faces, face_features)
results = []
for gaze_detected in estimated_gazes:
results.append(gaze_detected.tolist())
parsed_results = {"estimated_gazes": results}
return parsed_results
def __init__(self, src=0):
# initialize the video camera stream and read the first frame
# from the stream
self.webcam_stream = WebcamVideoStream(src).start()
frame, frame_time = self.webcam_stream.read()
self.frame_time = frame_time
if frame is not None:
(self.img, self.faces,
self.face_features) = extract_image_features(frame)
# initialize the variable used to indicate if the thread should
# be stopped
self.stopped = False
def update(self):
# keep looping infinitely until the thread is stopped
while True:
# if the thread indicator variable is set, stop the thread
# print('updating')
if self.stopped:
# print('returning')
return
# otherwise, read the next frame from the stream
frame, frame_time = self.webcam_stream.read()
self.frame_time = frame_time
if frame is not None:
(self.img, self.faces,
self.face_features) = extract_image_features(frame)
def __next__(self):
self.frameID += 1
#print(self.frameID)
outputs = []
ret, frame = self.cap.read()
img, dfaces, dface_features = extract_image_features(frame)
if self.frameID % self.multiplier == 0:
outputs = show_gaze(frame)
print(self.frameID)
gaze = detect_gaze(frame, outputs, 1280, 720, 12, 720)
if self.g != gaze:
self.timer = 0
print('last for: %i seconds' % self.timer)
else:
self.timer += 1
print('last for: %i seconds' % self.timer)
self.g = gaze
return frame, self.g, dfaces, dface_features
def show_gaze(img):
img, faces, face_features = extract_image_features(img)
return test_imgs(img, faces, face_features)
def extract_features_and_detect_gazes(img):
img, faces, face_features = extract_image_features(img)
return test_faces(img, faces, face_features)
from WebcamVideoStream import WebcamVideoStream
from features import extract_image_features, draw_detected_features
from lib import current_time
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
try:
for i in range(1, 100):
_, img = cap.read()
# print(img.shape, faces, face_features)
if img is not None:
print('writing frame', i)
cv2.imwrite('output/frame-' + str(i) + '.png', img)
img, faces, face_features = extract_image_features(img)
images_with_features = np.copy(img)
draw_detected_features(images_with_features, faces, face_features)
cv2.imwrite('output/frame-' + str(i) + '-features.png',
images_with_features)
# do whatever you need to do with the data
finally:
cap.release()
def ex(img):
img, faces, face_features = extract_image_features(img)
return test_imgs(img, faces, face_features)