def mask_face(image, face_location, six_points, angle, args, type="surgical"):
debug = False
# Find the face angle
threshold = 13
if angle < -threshold:
type += "_right"
elif angle > threshold:
type += "_left"
face_height = face_location[2] - face_location[0]
face_width = face_location[1] - face_location[3]
# image = image_raw[
# face_location[0]-int(face_width/2): face_location[2]+int(face_width/2),
# face_location[3]-int(face_height/2): face_location[1]+int(face_height/2),
# :,
# ]
# cv2.imshow('win', image)
# cv2.waitKey(0)
# Read appropriate mask image
w = image.shape[0]
h = image.shape[1]
if not "empty" in type and not "inpaint" in type:
cfg = read_cfg(config_filename="masks/masks.cfg",
mask_type=type,
verbose=False)
else:
if "left" in type:
str = "surgical_blue_left"
elif "right" in type:
str = "surgical_blue_right"
else:
str = "surgical_blue"
cfg = read_cfg(config_filename="masks/masks.cfg",
mask_type=str,
verbose=False)
img = cv2.imread(cfg.template, cv2.IMREAD_UNCHANGED)
# Process the mask if necessary
if args.pattern:
# Apply pattern to mask
img = texture_the_mask(img, args.pattern, args.pattern_weight)
if args.color:
# Apply color to mask
img = color_the_mask(img, args.color, args.color_weight)
mask_line = np.float32([
cfg.mask_a, cfg.mask_b, cfg.mask_c, cfg.mask_f, cfg.mask_e, cfg.mask_d
])
# Warp the mask
M, mask = cv2.findHomography(mask_line, six_points)
dst_mask = cv2.warpPerspective(img, M, (h, w))
dst_mask_points = cv2.perspectiveTransform(mask_line.reshape(-1, 1, 2), M)
mask = dst_mask[:, :, 3]
face_height = face_location[2] - face_location[0]
face_width = face_location[1] - face_location[3]
image_face = image[face_location[0] +
int(face_height / 2):face_location[2],
face_location[3]:face_location[1], :, ]
image_face = image
# Adjust Brightness
mask_brightness = get_avg_brightness(img)
img_brightness = get_avg_brightness(image_face)
delta_b = 1 + (img_brightness - mask_brightness) / 255
dst_mask = change_brightness(dst_mask, delta_b)
# Adjust Saturation
mask_saturation = get_avg_saturation(img)
img_saturation = get_avg_saturation(image_face)
delta_s = 1 - (img_saturation - mask_saturation) / 255
dst_mask = change_saturation(dst_mask, delta_s)
# Apply mask
mask_inv = cv2.bitwise_not(mask)
img_bg = cv2.bitwise_and(image, image, mask=mask_inv)
img_fg = cv2.bitwise_and(dst_mask, dst_mask, mask=mask)
out_img = cv2.add(img_bg, img_fg[:, :, 0:3])
if "empty" in type or "inpaint" in type:
out_img = img_bg
# Plot key points
if "inpaint" in type:
out_img = cv2.inpaint(out_img, mask, 3, cv2.INPAINT_TELEA)
# dst_NS = cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)
if debug:
for i in six_points:
cv2.circle(out_img, (i[0], i[1]),
radius=4,
color=(0, 0, 255),
thickness=-1)
for i in dst_mask_points:
cv2.circle(out_img, (i[0][0], i[0][1]),
radius=4,
color=(0, 255, 0),
thickness=-1)
return out_img, mask
def mask_face(image, face_location, six_points, angle, args, type="surgical"):
debug = False
# Find the face angle
threshold = 13
if angle < -threshold:
type += "_right"
elif angle > threshold:
type += "_left"
face_height = face_location[2] - face_location[0]
face_width = face_location[1] - face_location[3]
if face_height > face_width:
w = image.shape[0]
h = image.shape[1]
if not "empty" in type and not "inpaint" in type:
cfg = read_cfg(config_filename="masks/masks.cfg", mask_type=type, verbose=False)
else:
if "left" in type:
str = "surgical_blue_left"
elif "right" in type:
str = "surgical_blue_right"
else:
str = "surgical_blue"
cfg = read_cfg(config_filename="masks/masks.cfg", mask_type=str, verbose=False)
img = cv2.imread(cfg.template, cv2.IMREAD_UNCHANGED)
# Process the mask if necessary
if args.pattern:
# Apply pattern to mask
img = texture_the_mask(img, args.pattern, random.uniform(0.0, 0.7))
if args.color:
img = color_the_mask(img, args.color, random.uniform(0.0, 0.7))
mask_line = np.float32(
[cfg.mask_a, cfg.mask_b, cfg.mask_c, cfg.mask_f, cfg.mask_e, cfg.mask_d]
)
# Warp the mask
M, mask = cv2.findHomography(mask_line, six_points)
dst_mask = cv2.warpPerspective(img, M, (h, w))
dst_mask_points = cv2.perspectiveTransform(mask_line.reshape(-1, 1, 2), M)
mask = dst_mask[:, :, 3]
face_height = face_location[2] - face_location[0]
face_width = face_location[1] - face_location[3]
image_face = image
# Adjust Brightness
mask_brightness = get_avg_brightness(img)
img_brightness = get_avg_brightness(image_face)
delta_b = 1 + (img_brightness - mask_brightness) / 255
# print('delta_b:', delta_b)
dst_mask = change_brightness(dst_mask, delta_b)
# Adjust Saturation
mask_saturation = get_avg_saturation(img)
img_saturation = get_avg_saturation(image_face)
delta_s = 1 - (img_saturation - mask_saturation) / 255
# print('delta_s:', delta_s)
dst_mask = change_saturation(dst_mask, delta_s)
# Apply mask
mask_inv = cv2.bitwise_not(mask)
inv_mask_inv = cv2.bitwise_not(mask_inv)
img_bg = image
# img_bg_hsv = cv2.cvtColor(img_bg, cv2.COLOR_RGB2HSV)
# h_raw, s_raw, v_raw = cv2.split(img_bg_hsv)
img_fg = cv2.bitwise_and(dst_mask,dst_mask, mask=mask)
# img_fg_blur = cv2.GaussianBlur(img_fg, (3,3), 5)
# cv2.namedWindow("img_fg_blur", cv2.IMREAD_UNCHANGED)
# cv2.imshow("img_fg_blur", img_fg_blur)
img_fg_rgba = cv2.cvtColor(img_fg, cv2.COLOR_RGB2RGBA)
img_fg_rgba[:,:,3] = inv_mask_inv
# cv2.namedWindow("img_fg_rgba", cv2.IMREAD_UNCHANGED)
# cv2.imshow("img_fg_rgba", img_fg_rgba)
ret, thresh = cv2.threshold(img_fg_rgba,0,255,cv2.THRESH_BINARY)
# print('ret, thresh:', ret, thresh)
ave = cv2.mean(thresh)[0]/255
# print("ave:", ave)
if ave > 0.1:
blur_score = variance_of_laplacian(img_bg)
img_fg_rgba_pil = Image.fromarray(img_fg_rgba)
img_bg_pil = Image.fromarray(img_bg)
img_bg_pil.paste(img_fg_rgba_pil,(0,0),img_fg_rgba_pil)
img_bg_np = np.asarray(img_bg_pil)
if blur_score <= 100:
gaussian_blur = (7,7)
weight_blend = 0.45
elif blur_score > 100 and blur_score <= 300:
gaussian_blur = (5,5)
weight_blend = 0.5
else:
gaussian_blur = (3,3)
weight_blend = 0.65
blur_output = cv2.GaussianBlur(img_bg_np, gaussian_blur , 3)
histogram_blend = hist_norm(blur_output,img_bg_np)
blend_img = cv2.addWeighted(img_bg_np, (1 - weight_blend), blur_output, weight_blend, 3)
blend_img = np.asarray(blend_img)
# print(histogram_blend)
# cv2.namedWindow("blend_img", cv2.WINDOW_NORMAL)
# cv2.imshow("blend_img", blend_img)
# cv2.waitKey(0)
return blend_img
else:
return None
def mask_face(image, face_location, six_points, angle, args, type="surgical"):
cvimage = image
for i in six_points:
cv2.circle(cvimage, (i[0], i[1]), radius=4, color=(0, 0, 255), thickness=-1)
cv2.imwrite('E:\\2020FA\\EECS504\project\\report\\scale2.png', cvimage)
# cv2.imshow("image", cvimage)
# Find the face angle
threshold = 13
if angle < -threshold:
type += "_right"
elif angle > threshold:
type += "_left"
w = image.shape[0]
h = image.shape[1]
if not "empty" in type and not "inpaint" in type:
cfg = read_cfg(config_filename="masks/masks.cfg", mask_type=type, verbose=False)
else:
if "left" in type:
str = "surgical_blue_left"
elif "right" in type:
str = "surgical_blue_right"
else:
str = "surgical_blue"
cfg = read_cfg(config_filename="masks/masks.cfg", mask_type=str, verbose=False)
img = cv2.imread(cfg.template, cv2.IMREAD_UNCHANGED)
mask_line = np.float32(
[cfg.mask_a, cfg.mask_b, cfg.mask_c, cfg.mask_f, cfg.mask_e, cfg.mask_d]
)
# Warp the mask
M, mask = cv2.findHomography(mask_line, six_points)
dst_mask = cv2.warpPerspective(img, M, (h, w))
mask = dst_mask[:, :, 3]
face_height = face_location[3] - face_location[1]
print("face_location[3] - face_location[1]: {} - {} = {}.".format(face_location[3], face_location[1], face_height))
image_face = image
# Adjust Brightness
mask_brightness = get_avg_brightness(img)
img_brightness = get_avg_brightness(image_face)
delta_b = 1 + (img_brightness - mask_brightness) / 255
dst_mask = change_brightness(dst_mask, delta_b)
# Adjust Saturation
mask_saturation = get_avg_saturation(img)
img_saturation = get_avg_saturation(image_face)
delta_s = 1 - (img_saturation - mask_saturation) / 255
dst_mask = change_saturation(dst_mask, delta_s)
# Apply mask
mask_inv = cv2.bitwise_not(mask)
img_bg = cv2.bitwise_and(image, image, mask=mask_inv)
img_fg = cv2.bitwise_and(dst_mask, dst_mask, mask=mask)
out_img = cv2.add(img_bg, img_fg[:, :, 0:3])
if "empty" in type or "inpaint" in type:
out_img = img_bg
# Plot key points
if "inpaint" in type:
out_img = cv2.inpaint(out_img, mask, 3, cv2.INPAINT_TELEA)
return out_img, mask
