forked from 170010011/fr
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
# 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), (0, 0, 255)]
|
||
|
||
# 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
|