Spaces:
Runtime error
Runtime error
File size: 3,341 Bytes
64dbbc4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
import os
import cv2
import numpy as np
def resize_size(image, size=720):
h, w, c = np.shape(image)
if min(h, w) > size:
if h > w:
h, w = int(size * h / w), size
else:
h, w = size, int(size * w / h)
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
return image
def padTo16x(image):
h, w, c = np.shape(image)
if h % 16 == 0 and w % 16 == 0:
return image, h, w
nh, nw = (h // 16 + 1) * 16, (w // 16 + 1) * 16
img_new = np.ones((nh, nw, 3), np.uint8) * 255
img_new[:h, :w, :] = image
return img_new, h, w
def get_f5p(landmarks, np_img):
eye_left = find_pupil(landmarks[36:41], np_img)
eye_right = find_pupil(landmarks[42:47], np_img)
if eye_left is None or eye_right is None:
print('cannot find 5 points with find_puil, used mean instead.!')
eye_left = landmarks[36:41].mean(axis=0)
eye_right = landmarks[42:47].mean(axis=0)
nose = landmarks[30]
mouth_left = landmarks[48]
mouth_right = landmarks[54]
f5p = [[eye_left[0], eye_left[1]], [eye_right[0], eye_right[1]],
[nose[0], nose[1]], [mouth_left[0], mouth_left[1]],
[mouth_right[0], mouth_right[1]]]
return f5p
def find_pupil(landmarks, np_img):
h, w, _ = np_img.shape
xmax = int(landmarks[:, 0].max())
xmin = int(landmarks[:, 0].min())
ymax = int(landmarks[:, 1].max())
ymin = int(landmarks[:, 1].min())
if ymin >= ymax or xmin >= xmax or ymin < 0 or xmin < 0 or ymax > h or xmax > w:
return None
eye_img_bgr = np_img[ymin:ymax, xmin:xmax, :]
eye_img = cv2.cvtColor(eye_img_bgr, cv2.COLOR_BGR2GRAY)
eye_img = cv2.equalizeHist(eye_img)
n_marks = landmarks - np.array([xmin, ymin]).reshape([1, 2])
eye_mask = cv2.fillConvexPoly(
np.zeros_like(eye_img), n_marks.astype(np.int32), 1)
ret, thresh = cv2.threshold(eye_img, 100, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
thresh = (1 - thresh / 255.) * eye_mask
cnt = 0
xm = []
ym = []
for i in range(thresh.shape[0]):
for j in range(thresh.shape[1]):
if thresh[i, j] > 0.5:
xm.append(j)
ym.append(i)
cnt += 1
if cnt != 0:
xm.sort()
ym.sort()
xm = xm[cnt // 2]
ym = ym[cnt // 2]
else:
xm = thresh.shape[1] / 2
ym = thresh.shape[0] / 2
return xm + xmin, ym + ymin
def all_file(file_dir):
L = []
for root, dirs, files in os.walk(file_dir):
for file in files:
extend = os.path.splitext(file)[1]
if extend == '.png' or extend == '.jpg' or extend == '.jpeg':
L.append(os.path.join(root, file))
return L
def initialize_mask(box_width):
h, w = [box_width, box_width]
mask = np.zeros((h, w), np.uint8)
center = (int(w / 2), int(h / 2))
axes = (int(w * 0.4), int(h * 0.49))
mask = cv2.ellipse(img=mask, center=center, axes=axes, angle=0, startAngle=0, endAngle=360, color=(1),
thickness=-1)
mask = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
maxn = max(w, h) * 0.15
mask[(mask < 255) & (mask > 0)] = mask[(mask < 255) & (mask > 0)] / maxn
mask = np.clip(mask, 0, 1)
return mask.astype(float)
|