forked from 170010011/fr
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
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# import the necessary packages
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from collections import OrderedDict
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import numpy as np
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import cv2
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# define a dictionary that maps the indexes of the facial
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# landmarks to specific face regions
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#For dlib’s 68-point facial landmark detector:
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FACIAL_LANDMARKS_68_IDXS = OrderedDict([
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("mouth", (48, 68)),
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("inner_mouth", (60, 68)),
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("right_eyebrow", (17, 22)),
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("left_eyebrow", (22, 27)),
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("right_eye", (36, 42)),
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("left_eye", (42, 48)),
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("nose", (27, 36)),
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("jaw", (0, 17))
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])
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#For dlib’s 5-point facial landmark detector:
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FACIAL_LANDMARKS_5_IDXS = OrderedDict([
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("right_eye", (2, 3)),
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("left_eye", (0, 1)),
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("nose", (4))
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])
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# in order to support legacy code, we'll default the indexes to the
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# 68-point model
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FACIAL_LANDMARKS_IDXS = FACIAL_LANDMARKS_68_IDXS
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def rect_to_bb(rect):
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# take a bounding predicted by dlib and convert it
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# to the format (x, y, w, h) as we would normally do
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# with OpenCV
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x = rect.left()
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y = rect.top()
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w = rect.right() - x
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h = rect.bottom() - y
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# return a tuple of (x, y, w, h)
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return (x, y, w, h)
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def shape_to_np(shape, dtype="int"):
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# initialize the list of (x, y)-coordinates
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coords = np.zeros((shape.num_parts, 2), dtype=dtype)
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# loop over all facial landmarks and convert them
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# to a 2-tuple of (x, y)-coordinates
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for i in range(0, shape.num_parts):
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coords[i] = (shape.part(i).x, shape.part(i).y)
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# return the list of (x, y)-coordinates
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return coords
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def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
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# create two copies of the input image -- one for the
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# overlay and one for the final output image
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overlay = image.copy()
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output = image.copy()
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# if the colors list is None, initialize it with a unique
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# color for each facial landmark region
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if colors is None:
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colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
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(168, 100, 168), (158, 163, 32),
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(163, 38, 32), (180, 42, 220), (0, 0, 255)]
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# loop over the facial landmark regions individually
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for (i, name) in enumerate(FACIAL_LANDMARKS_IDXS.keys()):
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# grab the (x, y)-coordinates associated with the
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# face landmark
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(j, k) = FACIAL_LANDMARKS_IDXS[name]
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pts = shape[j:k]
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# check if are supposed to draw the jawline
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if name == "jaw":
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# since the jawline is a non-enclosed facial region,
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# just draw lines between the (x, y)-coordinates
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for l in range(1, len(pts)):
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ptA = tuple(pts[l - 1])
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ptB = tuple(pts[l])
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cv2.line(overlay, ptA, ptB, colors[i], 2)
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# otherwise, compute the convex hull of the facial
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# landmark coordinates points and display it
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else:
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hull = cv2.convexHull(pts)
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cv2.drawContours(overlay, [hull], -1, colors[i], -1)
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# apply the transparent overlay
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cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
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# return the output image
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return output
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