如何使用OpenCV和Mediapipe实现逼真的唇色变化？

yduiuuwa  于 2023-03-23  发布在  其他
关注(0)|答案(3)|浏览(260)

我需要帮助使用Mediapipe更改视频中人物的唇色。我使用Mediapipe进行面部标志点检测和跟踪，但我不确定如何继续更改唇色。我在Mediapipe文档中找不到任何有关如何实现此操作的资源。
这需要OpenCV比Mediapipe做得更多。你可能想搜索如何使用cv2.fillPoly填充多边形。你需要地标来定义轮廓，你可以在这里参考这张图片来找到哪些地标。
我使用Python和OpenCV。在Google Colab上运行代码。我确实尝试了@fadiaburaid建议的方法，但结果并不符合标准。当Mediapipe检测到坐标不断变化时，多边形似乎在跳舞，并且在图像上绘制的多边形似乎明显不均匀。我尝试了羽化，但它没有将结果质量带到可接受的水平。
任何改善和稳定多边形混合的建议都欢迎！！

人脸裁剪

from google.colab import output
from google.colab.patches import cv2_imshow

import cv2
import mediapipe as mp

# Load the MediaPipe Face Detection model
mp_face_detection = mp.solutions.face_detection

# Initialize the Face Detection model
face_detection = mp_face_detection.FaceDetection()

# Load the image
image = cv2.imread('/content/wallpaper.png')

# Convert the image to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Detect faces in the image
results = face_detection.process(image)

# Get the first detected face
face = results.detections[0]

# Get the bounding box of the face
x1 = int(face.location_data.relative_bounding_box.xmin * image.shape[1])
y1 = int(face.location_data.relative_bounding_box.ymin * image.shape[0])
x2 = int(x1 + face.location_data.relative_bounding_box.width * image.shape[1])
y2 = int(y1 + face.location_data.relative_bounding_box.height * image.shape[0])

# Calculate the size of the square bounding box
size = max(x2 - x1, y2 - y1)

# Calculate the center of the bounding box
center_x = (x1 + x2) // 2
center_y = (y1 + y2) // 2

# Calculate the coordinates of the square bounding box
x1_square = center_x - size // 2
y1_square = center_y - size // 2

x2_square = x1_square + size 
y2_square= y1_square + size 

# Crop and show square face region from original image 
square_face_region=image[y1_square:y2_square,x1_square:x2_square]

resized_image=cv2.resize(square_face_region,(480,480))
resized_image_bgr = cv2.cvtColor(resized_image, cv2.COLOR_RGB2BGR)

# Save the image
cv2.imwrite('resized_image.jpg', resized_image_bgr)

掩码生成

import itertools
import numpy as np

# Load the MediaPipe Face Mesh model
mp_face_mesh = mp.solutions.face_mesh

# Initialize the Face Mesh model
face_mesh = mp_face_mesh.FaceMesh( static_image_mode=True,refine_landmarks=True,min_detection_confidence=0.5)

image = resized_image_bgr

# Define the left eye landmark indices
# LIPS = list(set(itertools.chain(*mp_face_mesh.FACEMESH_LIPS)))

# upper = [409,405,375,321,314,267,269,270,291,146,181,185,91,84,61,37, 39, 40,0,17]
# lower = [402,415,312,311,310,308,324,318,317,178,191,80, 81, 82,87, 88,95,78,13, 14]

upper_new = [0,267,269,270,409,291,375,321,405,314,17,84,181,91,146,61,185,40,39,37]
lower_new = [13,312,311,310,415,308,324,318,402,317,14,87,178,88,95,78,191,80,81,82]

# Detect the face landmarks
results = face_mesh.process(image)

# Create an empty mask with the same shape as the image
mask_upper = np.zeros(image.shape[:2], dtype=np.uint8)

# Draw white polygons on the mask using the upper landmarks
for face_landmarks in results.multi_face_landmarks:
    points_upper = []
    for i in upper_new:
        landmark = face_landmarks.landmark[i]
        x = int(landmark.x * image.shape[1])
        y = int(landmark.y * image.shape[0])
        points_upper.append((x, y))
    cv2.fillConvexPoly(mask_upper, np.int32(points_upper), 255)

# Create an empty mask with the same shape as the image
mask_lower = np.zeros(image.shape[:2], dtype=np.uint8)

# Draw white polygons on the mask using the lower landmarks
for face_landmarks in results.multi_face_landmarks:
    points_lower = []
    for i in lower_new:
        landmark = face_landmarks.landmark[i]
        x = int(landmark.x * image.shape[1])
        y = int(landmark.y * image.shape[0])
        points_lower.append((x, y))
    cv2.fillPoly(mask_lower, np.int32([points_lower]), 255)

# Subtract the lower mask from the upper mask
mask_diff = cv2.subtract(mask_upper, mask_lower)

# Apply morphology operations to smooth mask 
kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5 ,5))
mask_diff=cv2.morphologyEx(mask_diff,cv2.MORPH_OPEN,kernel)
mask_diff=cv2.morphologyEx(mask_diff,cv2.MORPH_CLOSE,kernel)

cv2_imshow(mask_diff)

掩码混合

# Convert the mask to 3 channels
mask_diff_3ch = cv2.cvtColor(mask_diff, cv2.COLOR_GRAY2BGR)

image = cv2.imread('/content/resized_image.jpg')

# Apply the mask to the original image
masked_image = cv2.bitwise_and(image, mask_diff_3ch)

cv2_imshow(masked_image)

def create_colored_mask(hex_color, shape):
    # Convert the hex color code to an RGB tuple
    rgb_color = tuple(int(hex_color[i:i+2], 16) for i in (0, 2 ,4))
    
    # Create a blank mask with the given shape
    colored_mask = np.zeros(shape, dtype=np.uint8)
    
    # Set the color channels according to the chosen RGB color
    colored_mask[:,:,0] = rgb_color[2]
    colored_mask[:,:,1] = rgb_color[1]
    colored_mask[:,:,2] = rgb_color[0]
    
    return colored_mask

# Create a 3-channel version of your mask_diff array
mask_diff_3ch = cv2.cvtColor(mask_diff,cv2.COLOR_GRAY2BGR)

# Ask the user to enter a hex color code for their mask
hex_color = input('Enter a hex color code for your mask (e.g. FF0000 for red): ')

# Create a colored mask with the chosen hex color and same shape as your original mask
colored_mask = create_colored_mask(hex_color, mask_diff_3ch.shape)

# Apply the colored mask where your original mask is True
masked_image = cv2.bitwise_and(colored_mask,colored_mask ,mask=mask_diff)

# Superimpose the colored mask on your original image
final_image = cv2.addWeighted(image, 1 , masked_image ,1 ,0)

cv2_imshow(final_image)

我从上面的代码中得到了以下结果。但我希望从视频或照片输入中得到更高质量的结果。

enter image description here
输入图像：Input Image
裁剪的输入图像：Cropped Input Image
遮罩图像：Mask Image
最终图像：Final Image with Masking

opencv

3条答案

按热度按时间
qvtsj1bj

qvtsj1bj1#

这里有一个轻微的变化，我希望改进我的方法张贴在如何改变嘴唇的颜色，得到了它的地标，而不干扰它的纹理？在opencv python。
主要区别是：1）提供掩模，但是与嘴唇不匹配。所以我稍微放大了一点。2）我将cv2.add更改为cv2.addWeighted以将新颜色与嘴唇混合。新颜色的权重决定了应用的唇色量。我混合了图像（权重1）与新的颜色（权重0.75）。改变权重0.75的愿望。3）我增加了反锯齿的距离上的面具做一个软混合在边缘的嘴唇。
输入：

面罩：

import cv2
import numpy as np
import skimage.exposure

# specify desired bgr color for lips and make into array
desired_color = (170, 130, 255)
desired_color = np.asarray(desired_color, dtype=np.float64)

# create swatch
swatch = np.full((200,200,3), desired_color, dtype=np.uint8)

# read image
img = cv2.imread("lady2.jpg")

# read mask
mask = cv2.imread("lady2_mask.png", cv2.IMREAD_GRAYSCALE)

# dilate mask to make it better fit the lips
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)

# get average bgr color of lips
ave_color = cv2.mean(img, mask=mask)[:3]
print(ave_color)

# compute difference colors and make into an image the same size as input
diff_color = desired_color - ave_color
diff_color = np.full_like(img, diff_color, dtype=np.uint8)

# shift input image color
new_img = cv2.addWeighted(img, 1.0, diff_color, 0.75, 0)

# antialias mask, convert to float in range 0 to 1 and make 3-channels
mask = cv2.GaussianBlur(mask, (0,0), sigmaX=15, sigmaY=15, borderType = cv2.BORDER_DEFAULT)
mask = skimage.exposure.rescale_intensity(mask, in_range=(128,255), out_range=(0,1)).astype(np.float32)
mask = cv2.merge([mask,mask,mask])

# combine img and new_img using mask
result = (img * (1 - mask) + new_img * mask)
result = result.clip(0,255).astype(np.uint8)

# save result
cv2.imwrite('lady2_swatch.png', swatch)
cv2.imwrite('lady2_mask.png', (255*mask).clip(0,255).astype(np.uint8))
cv2.imwrite('lady2_recolor.jpg', result)

cv2.imshow('swatch', swatch)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

新唇色样本：

结果：

赞(0）分享  回复(0）举报  2023-03-23
zrfyljdw

zrfyljdw2#

这是一个修改后的脚本，它可以更好地处理大多数颜色。它可以在HSV颜色空间中处理颜色差异。
输入：

面罩：

import cv2
import numpy as np
import skimage.exposure

# specify desired bgr color for lips and make into array
#desired_color = (170,130,255)    # pink
#desired_color = (255,0,0)        # blue
desired_color = (0,255,0)         # green

print(desired_color)

# create swatch
swatch = np.full((200,200,3), desired_color, dtype=np.uint8)

# read image
img = cv2.imread("lady2.jpg")

# read mask
mask = cv2.imread("lady2_mask.png", cv2.IMREAD_GRAYSCALE)

# convert input to HSV and separate channels
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_img)

# dilate mask to make it better fit the lips
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)

# get average bgr color of lips as array
ave_color = cv2.mean(img, mask=mask)[:3]
print(ave_color)

# create 1 pixel image of average color
ave_color_img = np.full((1,1,3), ave_color, dtype=np.float32)
print(ave_color_img)

# create 1 pixel image of desired color
desired_color_img = np.full((1,1,3), desired_color, dtype=np.float32)
print(desired_color_img)

# convert desired color image to HSV
desired_hsv = cv2.cvtColor(desired_color_img, cv2.COLOR_BGR2HSV)

# convert average color image to HSV
ave_hsv = cv2.cvtColor(ave_color_img, cv2.COLOR_BGR2HSV)

# compute difference in HSV color arrays and separate channel values
diff_hsv = desired_hsv - ave_hsv
diff_h, diff_s, diff_v = cv2.split(diff_hsv)
print(diff_hsv)

# shift input image color
hnew = np.mod(h + diff_h/2, 180).astype(np.uint8)
snew = (s + diff_s).clip(0,255).astype(np.uint8)
vnew = (v + diff_v).clip(0,255).astype(np.uint8)

# merge channels back to HSV image
hsv_new = cv2.merge([hnew,snew,vnew])

# convert new HSV image to BGR
new_img = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# antialias mask, convert to float in range 0 to 1 and make 3-channels
mask = cv2.GaussianBlur(mask, (0,0), sigmaX=5, sigmaY=5, borderType = cv2.BORDER_DEFAULT)
mask = skimage.exposure.rescale_intensity(mask, in_range=(128,255), out_range=(0,1)).astype(np.float32)
mask = cv2.merge([mask,mask,mask])

# combine img and new_img using mask 
result = (img * (1 - mask) + new_img * mask)
result = result.clip(0,255).astype(np.uint8)

# save result
cv2.imwrite('lady2_swatch.png', swatch)
cv2.imwrite('lady2_recolor.jpg', result)

cv2.imshow('swatch', swatch)
cv2.imshow('mask', mask)
cv2.imshow('new_img', new_img)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

粉色结果：

蓝色结果：

绿色结果：

赞(0）分享  回复(0）举报  2023-03-23
p1tboqfb

p1tboqfb3#

这里是一个进一步的改进，增加了饱和度和亮度的增益，以便可以加深或减轻颜色。在下面我使用sfact=3和vfact=1.5来制作更深的绿色。

import cv2
import numpy as np
import skimage.exposure

# specify desired bgr color for lips and make into array
#desired_color = (170,130,255)    # pink
#desired_color = (255,0,0)        # blue
desired_color = (0,255,0)         # green

print(desired_color)

# create swatch
swatch = np.full((200,200,3), desired_color, dtype=np.uint8)

# read image
img = cv2.imread("lady2.jpg")

# read mask
mask = cv2.imread("lady2_mask.png", cv2.IMREAD_GRAYSCALE)

# convert input to HSV and separate channels
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_img)

# dilate mask to make it better fit the lips
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)

# get average bgr color of lips as array
ave_color = cv2.mean(img, mask=mask)[:3]
print(ave_color)

# create 1 pixel image of average color
ave_color_img = np.full((1,1,3), ave_color, dtype=np.float32)
print(ave_color_img)

# create 1 pixel image of desired color
desired_color_img = np.full((1,1,3), desired_color, dtype=np.float32)
print(desired_color_img)

# convert desired color image to HSV
desired_hsv = cv2.cvtColor(desired_color_img, cv2.COLOR_BGR2HSV)

# convert average color image to HSV
ave_hsv = cv2.cvtColor(ave_color_img, cv2.COLOR_BGR2HSV)

# compute difference in HSV color arrays and separate channel values
diff_hsv = desired_hsv - ave_hsv
diff_h, diff_s, diff_v = cv2.split(diff_hsv)
print(diff_hsv)

# shift input image color
sfact=3
vfact=1.5
hnew = np.mod(h + diff_h/2, 180).astype(np.uint8)
snew = (sfact*(s + diff_s)).clip(0,255).astype(np.uint8)
vnew = (vfact*(v + diff_v)).clip(0,255).astype(np.uint8)

# merge channels back to HSV image
hsv_new = cv2.merge([hnew,snew,vnew])

# convert new HSV image to BGR
new_img = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# antialias mask, convert to float in range 0 to 1 and make 3-channels
mask = cv2.GaussianBlur(mask, (0,0), sigmaX=5, sigmaY=5, borderType = cv2.BORDER_DEFAULT)
mask = skimage.exposure.rescale_intensity(mask, in_range=(128,255), out_range=(0,1)).astype(np.float32)
mask = cv2.merge([mask,mask,mask])

# combine img and new_img using mask 
result = (img * (1 - mask) + new_img * mask)
result = result.clip(0,255).astype(np.uint8)

# save result
cv2.imwrite('lady2_swatch.png', swatch)
cv2.imwrite('lady2_recolor.jpg', result)

cv2.imshow('swatch', swatch)
cv2.imshow('mask', mask)
cv2.imshow('new_img', new_img)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

绿色结果：

赞(0）分享  回复(0）举报  2023-03-23
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