|
|
import cv2 |
|
|
import numpy as np |
|
|
import csv |
|
|
|
|
|
|
|
|
|
|
|
def normalize8(I): |
|
|
mn = I.min() |
|
|
mx = I.max() |
|
|
mx -= mn |
|
|
I = ((I - mn) / mx) * 255 |
|
|
return I.astype(np.uint8) |
|
|
|
|
|
|
|
|
def overlay_transparent(background, overlay, x, y): |
|
|
|
|
|
background_width = background.shape[1] |
|
|
background_height = background.shape[0] |
|
|
|
|
|
if x >= background_width or y >= background_height: |
|
|
return background |
|
|
|
|
|
h, w = overlay.shape[0], overlay.shape[1] |
|
|
|
|
|
if x + w > background_width: |
|
|
w = background_width - x |
|
|
overlay = overlay[:, :w] |
|
|
|
|
|
if y + h > background_height: |
|
|
h = background_height - y |
|
|
overlay = overlay[:h] |
|
|
|
|
|
if overlay.shape[2] < 4: |
|
|
overlay = np.concatenate( |
|
|
[ |
|
|
overlay, |
|
|
np.ones((overlay.shape[0], overlay.shape[1], 1), dtype=overlay.dtype) |
|
|
* 255, |
|
|
], |
|
|
axis=2, |
|
|
) |
|
|
|
|
|
overlay_image = overlay[..., :3] |
|
|
mask = overlay[..., 3:] / 255.0 |
|
|
|
|
|
background[y : y + h, x : x + w] = (1.0 - mask) * background[ |
|
|
y : y + h, x : x + w |
|
|
] + mask * overlay_image |
|
|
|
|
|
return background |
|
|
|
|
|
|
|
|
def get_mask_points(mask_name): |
|
|
if mask_name == "Front Man Mask": |
|
|
mask_path = "./assets/front_man_mask.png" |
|
|
csv_path = "./assets/front_man_mask.csv" |
|
|
elif mask_name == "Guards Mask": |
|
|
mask_path = "./assets/guards_mask.png" |
|
|
csv_path = "./assets/guards_mask.csv" |
|
|
elif mask_name == "Red Mask": |
|
|
mask_path = "./assets/redmask.png" |
|
|
csv_path = "./assets/red_mask.csv" |
|
|
elif mask_name == "Blue Mask": |
|
|
mask_path = "./assets/bluemask.png" |
|
|
csv_path = "./assets/blue_mask.csv" |
|
|
else: |
|
|
raise ValueError(f"❌ Unknown mask name: {mask_name}") |
|
|
|
|
|
mask_img = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED) |
|
|
if mask_img is None: |
|
|
raise FileNotFoundError(f"❌ Could not load mask image: {mask_path}") |
|
|
|
|
|
mask_img = mask_img.astype(np.float32) / 255.0 |
|
|
|
|
|
mask_points = {} |
|
|
with open(csv_path) as csv_file: |
|
|
csv_reader = csv.reader(csv_file, delimiter=",") |
|
|
for i, row in enumerate(csv_reader): |
|
|
mask_points[int(row[0])] = [float(row[1]), float(row[2])] |
|
|
|
|
|
return mask_points, mask_img |
|
|
|
|
|
def mask_overlay(image, faces, mask_up, mask_down, mask_name): |
|
|
mask_points, mask_img = get_mask_points(mask_name) |
|
|
mirror_point = { |
|
|
234: 1, |
|
|
93: 2, |
|
|
132: 3, |
|
|
58: 4, |
|
|
172: 5, |
|
|
136: 6, |
|
|
150: 7, |
|
|
149: 8, |
|
|
176: 9, |
|
|
148: 10, |
|
|
152: 11, |
|
|
377: 12, |
|
|
400: 13, |
|
|
378: 14, |
|
|
379: 15, |
|
|
365: 16, |
|
|
397: 17, |
|
|
288: 18, |
|
|
361: 19, |
|
|
323: 20, |
|
|
454: 21, |
|
|
356: 22, |
|
|
389: 23, |
|
|
251: 24, |
|
|
284: 25, |
|
|
332: 26, |
|
|
297: 27, |
|
|
338: 28, |
|
|
10: 29, |
|
|
109: 30, |
|
|
67: 31, |
|
|
103: 32, |
|
|
54: 33, |
|
|
21: 34, |
|
|
162: 35, |
|
|
127: 36, |
|
|
} |
|
|
|
|
|
mask_points = mask_points |
|
|
src_pts = [] |
|
|
for i in sorted(mask_points.keys()): |
|
|
try: |
|
|
src_pts.append(np.array(mask_points[i])) |
|
|
except ValueError: |
|
|
continue |
|
|
src_pts = np.array(src_pts, dtype="float32") |
|
|
extend_y = [ |
|
|
1, |
|
|
2, |
|
|
3, |
|
|
4, |
|
|
5, |
|
|
6, |
|
|
7, |
|
|
8, |
|
|
9, |
|
|
10, |
|
|
11, |
|
|
12, |
|
|
13, |
|
|
14, |
|
|
15, |
|
|
16, |
|
|
17, |
|
|
18, |
|
|
19, |
|
|
20, |
|
|
21, |
|
|
] |
|
|
minimize_y = [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36] |
|
|
face_points = {} |
|
|
for i in faces[0]: |
|
|
for j in mirror_point.keys(): |
|
|
if i[0] == j: |
|
|
if mirror_point[i[0]] in minimize_y: |
|
|
face_points[mirror_point[j]] = [ |
|
|
float(i[1]), |
|
|
float(i[2] - int(mask_up)), |
|
|
] |
|
|
else: |
|
|
if mirror_point[i[0]] in extend_y: |
|
|
face_points[mirror_point[j]] = [ |
|
|
float(i[1]), |
|
|
float(i[2] + int(mask_down)), |
|
|
] |
|
|
else: |
|
|
face_points[mirror_point[j]] = [float(i[1]), float(i[2])] |
|
|
dst_pts = [] |
|
|
for i in sorted(face_points.keys()): |
|
|
try: |
|
|
dst_pts.append(np.array(face_points[i])) |
|
|
except ValueError: |
|
|
continue |
|
|
dst_pts = np.array(dst_pts, dtype="float32") |
|
|
M, _ = cv2.findHomography(src_pts, dst_pts) |
|
|
transformed_mask = cv2.warpPerspective( |
|
|
mask_img, |
|
|
M, |
|
|
(image.shape[1], image.shape[0]), |
|
|
None, |
|
|
cv2.INTER_LINEAR, |
|
|
cv2.BORDER_CONSTANT, |
|
|
borderValue=[0, 0, 0, 0], |
|
|
) |
|
|
png_image = normalize8(transformed_mask) |
|
|
new_image = overlay_transparent(image, png_image, 0, 0) |
|
|
|
|
|
return new_image |
