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faceswap.py
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faceswap.py
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import os
import cv2
import matplotlib.pyplot as plt
from face_enhancer import load_face_enhancer_model
def validate_image(img):
if not os.path.exists(img):
raise ValueError(f'Image {img} does not exist')
# check if img is a valid image file
if not os.path.isfile(img):
raise ValueError(f'Image {img} is not a valid image file')
# validate it to be jpg jpeg, png formats
if not img.lower().endswith(('.jpg', '.jpeg', '.png')):
raise ValueError(f'Image {img} is not a valid image file')
def cpu_warning(device):
if device == "cpu":
print("Using CPU for face enhancer. If you have a GPU, you can set device='cuda' to speed up the process. You can also set enhance=False to skip the enhancement.")
def swap_n_show(img1_fn, img2_fn, app, swapper,
plot_before=False, plot_after=True, enhance=False, enhancer='REAL-ESRGAN 2x',device="cpu"):
validate_image(img1_fn)
validate_image(img2_fn)
img1 = cv2.imread(img1_fn)
img2 = cv2.imread(img2_fn)
if plot_before:
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].imshow(img1[:,:,::-1])
axs[0].axis('off')
axs[1].imshow(img2[:,:,::-1])
axs[1].axis('off')
plt.show()
# Do the swap
face1 = app.get(img1)[0]
face2 = app.get(img2)[0]
img1_ = img1.copy()
img2_ = img2.copy()
if plot_after:
img1_ = swapper.get(img1_, face1, face2, paste_back=True)
img2_ = swapper.get(img2_, face2, face1, paste_back=True)
if enhance:
cpu_warning(device)
model, model_runner = load_face_enhancer_model(enhancer,device)
img1_ = model_runner(img1_, model)
img2_ = model_runner(img2_, model)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].imshow(img1_[:,:,::-1])
axs[0].axis('off')
axs[1].imshow(img2_[:,:,::-1])
axs[1].axis('off')
plt.show()
return img1_, img2_
def swap_n_show_same_img(img1_fn,
app, swapper,
plot_before=False,
plot_after=True, enhance=False, enhancer='REAL-ESRGAN 2x',device="cpu"):
validate_image(img1_fn)
img1 = cv2.imread(img1_fn)
if plot_before:
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.imshow(img1[:,:,::-1])
ax.axis('off')
plt.show()
# Do the swap
faces = app.get(img1)
face1, face2 = faces[0], faces[1]
img1_ = img1.copy()
if plot_after:
img1_ = swapper.get(img1_, face1, face2, paste_back=True)
img1_ = swapper.get(img1_, face2, face1, paste_back=True)
if enhance:
cpu_warning(device)
model, model_runner = load_face_enhancer_model(enhancer,device)
img1_ = model_runner(img1_, model)
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.imshow(img1_[:,:,::-1])
ax.axis('off')
plt.show()
return img1_
def swap_face_single(img1_fn, img2_fn, app, swapper,
plot_before=False, plot_after=True, enhance=False, enhancer='REAL-ESRGAN 2x',device="cpu"):
validate_image(img1_fn)
validate_image(img2_fn)
img1 = cv2.imread(img1_fn)
img2 = cv2.imread(img2_fn)
if plot_before:
axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].imshow(img1[:,:,::-1])
axs[0].axis('off')
axs[1].imshow(img2[:,:,::-1])
axs[1].axis('off')
plt.show()
# Do the swap
face1 = app.get(img1)[0]
face2 = app.get(img2)[0]
img1_ = img1.copy()
if plot_after:
img1_ = swapper.get(img1_, face1, face2, paste_back=True)
if enhance:
cpu_warning(device)
model, model_runner = load_face_enhancer_model(enhancer,device)
img1_ = model_runner(img1_, model)
# Save the image
output_fn = os.path.join('outputs', os.path.basename(img1_fn))
cv2.imwrite(output_fn, img1_)
print(f'Image saved to {output_fn}')
return img1_
def fine_face_swap(img1_fn, img2_fn, app, swapper,enhance=False, enhancer='REAL-ESRGAN 2x',device="cpu"):
img1 = cv2.imread(img1_fn)
facesimg1 = app.get(img1)
total_faces_img1 = len(facesimg1)
if total_faces_img1 > 1:
print(f'{total_faces_img1} faces detected')
fig, axs = plt.subplots(1, total_faces_img1, figsize=(12, 5))
for i, face in enumerate(facesimg1):
bbox = face['bbox']
bbox = [int(b) for b in bbox]
axs[i].imshow(img1[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1])
axs[i].axis('off')
axs[i].set_title(f'Face {i+1}')
plt.suptitle('Select a face to swap')
plt.show()
else:
print(f'{total_faces_img1} face detected')
bbox = facesimg1[0]['bbox']
bbox = [int(b) for b in bbox]
plt.imshow(img1[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1])
plt.axis('off')
plt.title('Face 1')
plt.show()
# Select a face from img1
face_idximg1 = int(input(f'Enter face number (1-{total_faces_img1}): '))
if face_idximg1 < 1 or face_idximg1 > total_faces_img1:
raise ValueError(f'Invalid face number {face_idximg1}')
face = facesimg1[face_idximg1-1]
bbox = face['bbox']
bbox = [int(b) for b in bbox]
face_img = img1[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1]
plt.imshow(face_img)
plt.axis('off')
plt.title(f'Face {face_idximg1}')
plt.suptitle('Selected face')
plt.show()
img2 = cv2.imread(img2_fn)
facesimg2 = app.get(img2)
total_faces_img2 = len(facesimg2)
if total_faces_img2 > 1:
print(f'{total_faces_img2} faces detected')
fig, axs = plt.subplots(1, total_faces_img2, figsize=(12, 5))
for i, face in enumerate(facesimg2):
bbox = face['bbox']
bbox = [int(b) for b in bbox]
axs[i].imshow(img2[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1])
axs[i].axis('off')
axs[i].set_title(f'Face {i+1}')
plt.suptitle('Select a face to swap')
plt.show()
else:
print(f'{total_faces_img2} face detected')
bbox = facesimg2[0]['bbox']
bbox = [int(b) for b in bbox]
plt.imshow(img2[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1])
plt.axis('off')
plt.title('Face 1')
plt.show()
# Select a face from img2
face_idximg2 = int(input(f'Enter face number (1-{total_faces_img2}): '))
if face_idximg2 < 1 or face_idximg2 > total_faces_img2:
raise ValueError(f'Invalid face number {face_idximg2}')
face = facesimg2[face_idximg2-1]
bbox = face['bbox']
bbox = [int(b) for b in bbox]
face_img = img2[bbox[1]:bbox[3],bbox[0]:bbox[2],::-1]
plt.imshow(face_img)
plt.axis('off')
plt.title(f'Face {face_idximg2}')
plt.suptitle('Selected face')
plt.show()
# source face
face1 = app.get(img1)[face_idximg1-1]
face2 = app.get(img2)[face_idximg2-1]
img1_ = img1.copy()
img1_ = swapper.get(img1_, face1, face2, paste_back=True)
if enhance:
cpu_warning(device)
model, model_runner = load_face_enhancer_model(enhancer,device)
img1_ = model_runner(img1_, model)
# Save the image
output_fn = os.path.join('outputs', os.path.basename(img1_fn))
cv2.imwrite(output_fn, img1_)
print(f'Image saved to {output_fn}')
return img1_