Create helpers.py

helpers.py

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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), (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