helpers.py

# 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
    print('naman')
	# 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