How to use insightface library directly [HELP] #427

New Issue

2023-06-28T12:45:39+02:00

harisreedhar commented

2023-06-28 12:45:39 +02:00

(Migrated from github.com)

Hi, I'm trying to use simswap with insightface library directly installed through pip. But result look totally weird. How to use model correctly? Is latent_id and embedding different?
What am i missing🤔?

Here is the entire code:

# running from simswap directory

import cv2
import PIL
import torch
import torch.nn as nn
import numpy as np
import insightface
from torchvision import transforms
from models.fs_networks import Generator_Adain_Upsample

def resize_with_padding(image, target_size):
    original_height, original_width = image.shape[:2]
    target_width, target_height = target_size
    aspect_ratio = original_width / original_height
    target_aspect_ratio = target_width / target_height
    if target_aspect_ratio > aspect_ratio:
        new_width = target_height * aspect_ratio
        new_height = target_height
    else:
        new_width = target_width
        new_height = target_width / aspect_ratio
    resized_image = cv2.resize(image, (int(new_width), int(new_height)))
    top_padding = (target_height - resized_image.shape[0]) // 2
    bottom_padding = target_height - resized_image.shape[0] - top_padding
    left_padding = (target_width - resized_image.shape[1]) // 2
    right_padding = target_width - resized_image.shape[1] - left_padding
    padded_image = cv2.copyMakeBorder(
        resized_image,
        top_padding,
        bottom_padding,
        left_padding,
        right_padding,
        cv2.BORDER_CONSTANT,
        value=(0, 0, 0)
    )
    return padded_image

## insightface analyser
face_analyser = insightface.app.FaceAnalysis(name='buffalo_l')
face_analyser.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.5)

def get_face_info(img):
    crop = lambda img,bb:img[int(bb[1]):int(bb[3]), int(bb[0]):int(bb[2])]
    face = face_analyser.get(img)[0]
    embedding = np.array([face['embedding']])
    cropped = crop(img, face['bbox'])
    return resize_with_padding(cropped, (224,224)), embedding

## read images
img1 = cv2.imread("./demo_file/Iron_man.jpg")
img2 = cv2.imread("./demo_file/specific3.png")

## cropped face and embedding
face1, embed1 = get_face_info(img1)
face2, embed2 = get_face_info(img2)

## load simswap model
device = torch.device("cuda:0")
net = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=False)
net.to(device)
net.load_state_dict(torch.load("./checkpoints/simswap_224_latest_net_G.pth"))

with torch.no_grad():
    transform = transforms.ToTensor()
    tensor1 = transform(face1[:, :, ::-1].astype('float32').copy())
    tensor1 = tensor1.unsqueeze(0)

    embed2 = torch.from_numpy(embed2)
    embed2 /= torch.linalg.norm(embed2, dim=1, keepdim=True)

    tensor1 = tensor1.to('cuda')
    embed2 = embed2.to('cuda')

    img_fake = net.forward(tensor1, embed2)

cv2_img = img_fake.squeeze(0).permute(1, 2, 0).cpu().numpy()[..., ::-1]
cv2.imwrite("test_swap.jpg", (cv2_img*255).astype('uint8'))

img1:

img2

result

Hi, I'm trying to use simswap with insightface library directly installed through pip. But result look totally weird. How to use model correctly? Is latent_id and embedding different? What am i missing🤔? Here is the entire code: ````python # running from simswap directory import cv2 import PIL import torch import torch.nn as nn import numpy as np import insightface from torchvision import transforms from models.fs_networks import Generator_Adain_Upsample def resize_with_padding(image, target_size): original_height, original_width = image.shape[:2] target_width, target_height = target_size aspect_ratio = original_width / original_height target_aspect_ratio = target_width / target_height if target_aspect_ratio > aspect_ratio: new_width = target_height * aspect_ratio new_height = target_height else: new_width = target_width new_height = target_width / aspect_ratio resized_image = cv2.resize(image, (int(new_width), int(new_height))) top_padding = (target_height - resized_image.shape[0]) // 2 bottom_padding = target_height - resized_image.shape[0] - top_padding left_padding = (target_width - resized_image.shape[1]) // 2 right_padding = target_width - resized_image.shape[1] - left_padding padded_image = cv2.copyMakeBorder( resized_image, top_padding, bottom_padding, left_padding, right_padding, cv2.BORDER_CONSTANT, value=(0, 0, 0) ) return padded_image ## insightface analyser face_analyser = insightface.app.FaceAnalysis(name='buffalo_l') face_analyser.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.5) def get_face_info(img): crop = lambda img,bb:img[int(bb[1]):int(bb[3]), int(bb[0]):int(bb[2])] face = face_analyser.get(img)[0] embedding = np.array([face['embedding']]) cropped = crop(img, face['bbox']) return resize_with_padding(cropped, (224,224)), embedding ## read images img1 = cv2.imread("./demo_file/Iron_man.jpg") img2 = cv2.imread("./demo_file/specific3.png") ## cropped face and embedding face1, embed1 = get_face_info(img1) face2, embed2 = get_face_info(img2) ## load simswap model device = torch.device("cuda:0") net = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=False) net.to(device) net.load_state_dict(torch.load("./checkpoints/simswap_224_latest_net_G.pth")) with torch.no_grad(): transform = transforms.ToTensor() tensor1 = transform(face1[:, :, ::-1].astype('float32').copy()) tensor1 = tensor1.unsqueeze(0) embed2 = torch.from_numpy(embed2) embed2 /= torch.linalg.norm(embed2, dim=1, keepdim=True) tensor1 = tensor1.to('cuda') embed2 = embed2.to('cuda') img_fake = net.forward(tensor1, embed2) cv2_img = img_fake.squeeze(0).permute(1, 2, 0).cpu().numpy()[..., ::-1] cv2.imwrite("test_swap.jpg", (cv2_img*255).astype('uint8')) ```` img1: ![Iron_man](https://github.com/neuralchen/SimSwap/assets/46858047/f496b4a6-85fd-4bbe-9d8a-5a4bbd3165f7) img2 ![specific3](https://github.com/neuralchen/SimSwap/assets/46858047/e076523f-78d5-41e4-b27d-39ac4acc66ac) result ![test_swap](https://github.com/neuralchen/SimSwap/assets/46858047/d1d76f88-1569-4f50-b6db-fb06060b9729)

jasan-s commented

2023-07-07 17:23:06 +02:00

(Migrated from github.com)

@harisreedhar why close? I'm also looking to get better than 128x128 resolution from insightface

Sign in to join this conversation.

1 Participants

Notifications

Due Date

No due date set.

Dependencies

No dependencies set.

Reference: CalvinBackup/neuralchen-SimSwap#427