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face_utils.py
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face_utils.py
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import numpy as np
import cv2
class face_utils():
def __init__(self):
# self.shape = shape
# self.rect = rect
pass
def shape_to_np( shape , dtype="int"):
"""
take the shape landmark predicted object and converts it to numpy
"""
# initialize the list of (x, y)-coordinates
coords = np.zeros((68, 2), dtype=dtype)
# loop over the 68 facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
def rect_to_bb(rect ):
"""
take a bounding predicted by dlib and convert it
to the format (x, y, w, h) as we would normally do
with OpenCV
"""
# rect = self.rect
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
# return a tuple of (x, y, w, h)
return (x, y, w, h)
def draw_bbox(image, dets):
"""
draws faces detected
params:
image
dets: dlib face detection object
returns:
image with bbox drawn on them
"""
for bbox in dets:
(x1,y1) = (bbox.left(),bbox.top())
(x2,y2) = (bbox.right(),bbox.bottom())
image = cv2.rectangle(image, (x1,y1), (x2,y2), (0,255,0), 2 )
return image
def largest_bbox(dets):
"""
returns largest detected face
"""
largest = -999
for bbox in dets:
x1 = min(bbox.left(),bbox.right())
x2 = max(bbox.left(),bbox.right())
y1 = min(bbox.top(),bbox.bottom())
y2 = max(bbox.top(),bbox.bottom())
area = (x2-x1) * ( y2 - y1)
if area > largest:
dets = bbox
largest = area
return dets