def betaface_recognition(image_dict):
""" Given a message contained a cropped head, send it to BetaFace
API for face recognition. This can take anywhere from 1 to 10 seconds.
"""
try:
api = BetaFaceAPI()
# Get image from message, save it to disk and
# pass it on to BetaFace API
image = base64_to_image(image_dict['head_image']['image'],
int(image_dict['head_image']['width']),
int(image_dict['head_image']['height']))
temp_path = random_file_name('jpg', "FR_")
logger.info("Generated random path to save image: %s" % temp_path)
image = image.rotate(180)
image.save(temp_path)
logger.info("Saved image to disk at path %s" % temp_path)
matches = api.recognize_faces(temp_path, 'amilab.ro')
logger.info("Received recognized faces from BetaFace API %r" % matches)
#os.remove(str(temp_path))
#logger.info("Removed image from disk (%s)" % str(temp_path))
return matches
except:
logger.exception("Failed to recognize faces via BetaFace API")
return {}
def crop_face_from_image(image):
""" Return a cropped face from a given image. """
# Try to detect faces in the image - if there is None,
# there is nothing to do. Otherwise, keep only the first
# face regardless of how many were detected.
faces = voting_face_detect(image)
if len(faces) == 0:
return None
(x, y, w, h) = faces[0][0]
logger.info("Found face with size %d x %d" % (w, h))
# Take the first detected face and scale the rectangle
# 2 times around its center.
face_rect = (x, y, x + w, y + h)
face_rect = rectangle_scale(*face_rect, factor = 2)
logger.info("Doubling both dimensions to %d x %d" % (face_rect[2] - face_rect[0],
face_rect[3] - face_rect[1]))
# Intersect this with the image rectangle
image_rect = (0, 0, image.size[0], image.size[1])
cropping_rect = rectangle_intersection(face_rect, image_rect)
logger.info("Final cropping rect has dimensions %d x %d" % (cropping_rect[2] - cropping_rect[0],
cropping_rect[3] - cropping_rect[1]))
logger.info("Cropping rect: %.2f %.2f %.2f %.2f" % (cropping_rect[0],
cropping_rect[1],
cropping_rect[2],
cropping_rect[3]))
cropping_rect = (int(cropping_rect[0]), int(cropping_rect[1]),
int(cropping_rect[2]), int(cropping_rect[3]))
cropped_image = image.crop(cropping_rect)
# Save both images and log this so we can debug more easily
temp_path_full_image = random_file_name('jpg')
temp_path_face_image = random_file_name('jpg')
image.save(temp_path_full_image)
cropped_image.save(temp_path_face_image)
logger.info("Saved full image at %s" % temp_path_full_image)
logger.info("Saved full image at %s" % temp_path_face_image)
return cropped_image