anydoor/inference_trained_select.py

import cv2
import einops
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from cldm.hack import disable_verbosity, enable_sliced_attention
from datasets.data_utils import * 
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
from omegaconf import OmegaConf
from PIL import Image


save_memory = False
disable_verbosity()
if save_memory:
    enable_sliced_attention()


config = OmegaConf.load('./configs/inference.yaml')
model_ckpt =  config.pretrained_model
model_config = config.config_file

model = create_model(model_config ).cpu()
model.load_state_dict(load_state_dict(model_ckpt, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)



def aug_data_mask(image, mask):
    transform = A.Compose([
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.5),
        ])
    transformed = transform(image=image.astype(np.uint8), mask = mask)
    transformed_image = transformed["image"]
    transformed_mask = transformed["mask"]
    return transformed_image, transformed_mask


def process_pairs(ref_image, ref_mask, tar_image, tar_mask):
    # ========= Reference ===========
    # ref expand 
    ref_box_yyxx = get_bbox_from_mask(ref_mask)

    # ref filter mask 
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
    masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3)

    y1,y2,x1,x2 = ref_box_yyxx
    masked_ref_image = masked_ref_image[y1:y2,x1:x2,:]
    ref_mask = ref_mask[y1:y2,x1:x2]


    ratio = np.random.randint(12, 13) / 10
    masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio)
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)

    # to square and resize
    masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False)
    masked_ref_image = cv2.resize(masked_ref_image, (224,224) ).astype(np.uint8)

    ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False)
    ref_mask_3 = cv2.resize(ref_mask_3, (224,224) ).astype(np.uint8)
    ref_mask = ref_mask_3[:,:,0]

    # ref aug 
    masked_ref_image_aug = masked_ref_image #aug_data(masked_ref_image) 

    # collage aug 
    masked_ref_image_compose, ref_mask_compose = masked_ref_image, ref_mask #aug_data_mask(masked_ref_image, ref_mask) 
    masked_ref_image_aug = masked_ref_image_compose.copy()
    ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1)
    ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255)

    # ========= Target ===========
    tar_box_yyxx = get_bbox_from_mask(tar_mask)
    tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2])

    # crop
    tar_box_yyxx_crop =  expand_bbox(tar_image, tar_box_yyxx, ratio=[1.5, 3])    #1.2 1.6
    tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
    y1,y2,x1,x2 = tar_box_yyxx_crop

    cropped_target_image = tar_image[y1:y2,x1:x2,:]
    tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop)
    y1,y2,x1,x2 = tar_box_yyxx

    # collage
    ref_image_collage = cv2.resize(ref_image_collage, (x2-x1, y2-y1))
    ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8)

    collage = cropped_target_image.copy() 
    collage[y1:y2,x1:x2,:] = ref_image_collage

    collage_mask = cropped_target_image.copy() * 0.0
    collage_mask[y1:y2,x1:x2,:] = 1.0

    # the size before pad
    H1, W1 = collage.shape[0], collage.shape[1]
    cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8)
    collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8)
    collage_mask = pad_to_square(collage_mask, pad_value = -1, random = False).astype(np.uint8)

    # the size after pad
    H2, W2 = collage.shape[0], collage.shape[1]
    cropped_target_image = cv2.resize(cropped_target_image, (512,512)).astype(np.float32)
    collage = cv2.resize(collage, (512,512)).astype(np.float32)
    collage_mask  = (cv2.resize(collage_mask, (512,512)).astype(np.float32) > 0.5).astype(np.float32)

    masked_ref_image_aug = masked_ref_image_aug  / 255 
    cropped_target_image = cropped_target_image / 127.5 - 1.0
    collage = collage / 127.5 - 1.0 
    collage = np.concatenate([collage, collage_mask[:,:,:1]  ] , -1)

    item = dict(ref=masked_ref_image_aug.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), extra_sizes=np.array([H1, W1, H2, W2]), tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ) ) 
    return item


def crop_back( pred, tar_image,  extra_sizes, tar_box_yyxx_crop):
    H1, W1, H2, W2 = extra_sizes
    y1,y2,x1,x2 = tar_box_yyxx_crop    
    pred = cv2.resize(pred, (W2, H2))
    m = 5 # maigin_pixel

    if W1 == H1:
        tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
        return tar_image

    if W1 < W2:
        pad1 = int((W2 - W1) / 2)
        pad2 = W2 - W1 - pad1
        pred = pred[:,pad1: -pad2, :]
    else:
        pad1 = int((H2 - H1) / 2)
        pad2 = H2 - H1 - pad1
        pred = pred[pad1: -pad2, :, :]

    gen_image = tar_image.copy()
    gen_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
    return gen_image


def inference_single_image(ref_image, ref_mask, tar_image, tar_mask, guidance_scale = 5.0):
    item = process_pairs(ref_image, ref_mask, tar_image, tar_mask)
    ref = item['ref'] * 255
    tar = item['jpg'] * 127.5 + 127.5
    hint = item['hint'] * 127.5 + 127.5

    hint_image = hint[:,:,:-1]
    hint_mask = item['hint'][:,:,-1] * 255
    hint_mask = np.stack([hint_mask,hint_mask,hint_mask],-1)
    ref = cv2.resize(ref.astype(np.uint8), (512,512))

    seed = random.randint(0, 65535)
    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    ref = item['ref']
    tar = item['jpg'] 
    hint = item['hint']
    num_samples = 1

    control = torch.from_numpy(hint.copy()).float().cuda() 
    control = torch.stack([control for _ in range(num_samples)], dim=0)
    control = einops.rearrange(control, 'b h w c -> b c h w').clone()


    clip_input = torch.from_numpy(ref.copy()).float().cuda() 
    clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0)
    clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone()

    guess_mode = False
    H,W = 512,512

    cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]}
    un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
    shape = (4, H // 8, W // 8)

    if save_memory:
        model.low_vram_shift(is_diffusing=True)

    # ====
    num_samples = 1 #gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
    image_resolution = 512  #gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
    strength = 1  #gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
    guess_mode = False #gr.Checkbox(label='Guess Mode', value=False)
    #detect_resolution = 512  #gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1)
    ddim_steps = 50 #gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
    scale = guidance_scale  #gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
    seed = -1  #gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
    eta = 0.0 #gr.Number(label="eta (DDIM)", value=0.0)

    model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
    samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                    shape, cond, verbose=False, eta=eta,
                                                    unconditional_guidance_scale=scale,
                                                    unconditional_conditioning=un_cond)
    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    x_samples = model.decode_first_stage(samples)
    x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy()#.clip(0, 255).astype(np.uint8)

    result = x_samples[0][:,:,::-1]
    result = np.clip(result,0,255)

    pred = x_samples[0]
    pred = np.clip(pred,0,255)[1:,:,:]
    sizes = item['extra_sizes']
    tar_box_yyxx_crop = item['tar_box_yyxx_crop'] 
    gen_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop) 
    return gen_image


if __name__ == '__main__': 
   
    import os 
    import itertools

    save_dir = '/work/we_select/out'
    cloth_dir = '/work/we_select/cloth'
    cloth_mask_dir = '/work/we_select/cloth-mask'
    image_dir = '/work/we_select/image'
    image_parse_v3_dir = '/work/we_select/image-parse-v3'

    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    cloth_image_names = os.listdir(cloth_dir)
    ref_image_names = os.listdir(image_dir)

    assert len(ref_image_names) > 0, "No reference images found"

    ref_images_cycle = itertools.cycle(ref_image_names)

    for cloth_image_name in cloth_image_names:
        ref_image_name = next(ref_images_cycle)  
        
        # Construct paths for cloth and its mask
        cloth_image_path = os.path.join(cloth_dir, cloth_image_name)
        cloth_mask_path = os.path.join(cloth_mask_dir, cloth_image_name)
        
        # Construct paths for reference image and its mask
        ref_image_path = os.path.join(image_dir, ref_image_name)
        ref_mask_path = os.path.join(image_parse_v3_dir, ref_image_name.replace('.jpg', '.png'))

        # Load and process the cloth image and mask
        cloth_image = cv2.imread(cloth_image_path)
        cloth_image = cv2.cvtColor(cloth_image, cv2.COLOR_BGR2RGB)
        cloth_mask = (cv2.imread(cloth_mask_path) > 128).astype(np.uint8)[:, :, 0]

        # Load and process the reference image and mask
        ref_image = cv2.imread(ref_image_path)
        ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
        ref_mask = Image.open(ref_mask_path).convert('P')
        ref_mask = np.array(ref_mask) == 5  # Assuming '5' denotes the relevant class for the mask

        # Generate the image using the provided function
        gen_image = inference_single_image(cloth_image, cloth_mask, ref_image, ref_mask)
        gen_path = os.path.join(save_dir, str('5_' + cloth_image_name))

        # Concatenate and save the visualization
        vis_image = cv2.hconcat([cloth_image, ref_image, gen_image])
        cv2.imwrite(gen_path, vis_image[:, :, ::-1])

        #--
        
        # Load and process the cloth image and mask
        cloth_image = cv2.imread(cloth_image_path)
        cloth_image = cv2.cvtColor(cloth_image, cv2.COLOR_BGR2RGB)
        cloth_mask = (cv2.imread(cloth_mask_path) > 128).astype(np.uint8)[:, :, 0]

        # Load and process the reference image and mask
        ref_image = cv2.imread(ref_image_path)
        ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
        ref_mask = Image.open(ref_mask_path).convert('P')
        ref_mask = np.array(ref_mask) == 9  # Assuming '5' denotes the relevant class for the mask

        # Generate the image using the provided function
        gen_image = inference_single_image(cloth_image, cloth_mask, ref_image, ref_mask)
        gen_path = os.path.join(save_dir, str('9_' + cloth_image_name))

        # Concatenate and save the visualization
        vis_image = cv2.hconcat([cloth_image, ref_image, gen_image])
        cv2.imwrite(gen_path, vis_image[:, :, ::-1])