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@jrosebr1 @abhiTronix
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# import the necessary packages
from collections import OrderedDict
import numpy as np
import cv2
# define a dictionary that maps the indexes of the facial
# landmarks to specific face regions
#For dlib’s 68-point facial landmark detector:
FACIAL_LANDMARKS_68_IDXS = OrderedDict([
("mouth", (48, 68)),
("inner_mouth", (60, 68)),
("right_eyebrow", (17, 22)),
("left_eyebrow", (22, 27)),
("right_eye", (36, 42)),
("left_eye", (42, 48)),
("nose", (27, 36)),
("jaw", (0, 17))
])
#For dlib’s 5-point facial landmark detector:
FACIAL_LANDMARKS_5_IDXS = OrderedDict([
("right_eye", (2, 3)),
("left_eye", (0, 1)),
("nose", (4))
])
# in order to support legacy code, we'll default the indexes to the
# 68-point model
FACIAL_LANDMARKS_IDXS = FACIAL_LANDMARKS_68_IDXS
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
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 shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# loop over all facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
# create two copies of the input image -- one for the
# overlay and one for the final output image
overlay = image.copy()
output = image.copy()
# if the colors list is None, initialize it with a unique
# color for each facial landmark region
if colors is None:
colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
(168, 100, 168), (158, 163, 32),
(163, 38, 32), (180, 42, 220)]
# loop over the facial landmark regions individually
for (i, name) in enumerate(FACIAL_LANDMARKS_IDXS.keys()):
# grab the (x, y)-coordinates associated with the
# face landmark
(j, k) = FACIAL_LANDMARKS_IDXS[name]
pts = shape[j:k]
# check if are supposed to draw the jawline
if name == "jaw":
# since the jawline is a non-enclosed facial region,
# just draw lines between the (x, y)-coordinates
for l in range(1, len(pts)):
ptA = tuple(pts[l - 1])
ptB = tuple(pts[l])
cv2.line(overlay, ptA, ptB, colors[i], 2)
# otherwise, compute the convex hull of the facial
# landmark coordinates points and display it
else:
hull = cv2.convexHull(pts)
cv2.drawContours(overlay, [hull], -1, colors[i], -1)
# apply the transparent overlay
cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
# return the output image
return output
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