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preprocess_image.py
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preprocess_image.py
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import os
import sys
import cv2
import argparse
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
class BackgroundRemoval():
def __init__(self, device='cuda'):
from carvekit.api.high import HiInterface
self.interface = HiInterface(
object_type="object", # Can be "object" or "hairs-like".
batch_size_seg=5,
batch_size_matting=1,
device=device,
seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net
matting_mask_size=2048,
trimap_prob_threshold=231,
trimap_dilation=30,
trimap_erosion_iters=5,
fp16=True,
)
@torch.no_grad()
def __call__(self, image):
# image: [H, W, 3] array in [0, 255].
image = Image.fromarray(image)
image = self.interface([image])[0]
image = np.array(image)
return image
class BLIP2():
def __init__(self, device='cuda'):
self.device = device
from transformers import AutoProcessor, Blip2ForConditionalGeneration
self.processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16).to(device)
@torch.no_grad()
def __call__(self, image):
image = Image.fromarray(image)
inputs = self.processor(image, return_tensors="pt").to(self.device, torch.float16)
generated_ids = self.model.generate(**inputs, max_new_tokens=20)
generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return generated_text
class DPT():
def __init__(self, task='depth', device='cuda'):
self.task = task
self.device = device
from dpt import DPTDepthModel
if task == 'depth':
path = 'pretrained/omnidata/omnidata_dpt_depth_v2.ckpt'
self.model = DPTDepthModel(backbone='vitb_rn50_384')
self.aug = transforms.Compose([
transforms.Resize((384, 384)),
transforms.ToTensor(),
transforms.Normalize(mean=0.5, std=0.5)
])
else: # normal
path = 'pretrained/omnidata/omnidata_dpt_normal_v2.ckpt'
self.model = DPTDepthModel(backbone='vitb_rn50_384', num_channels=3)
self.aug = transforms.Compose([
transforms.Resize((384, 384)),
transforms.ToTensor()
])
# load model
checkpoint = torch.load(path, map_location='cpu')
if 'state_dict' in checkpoint:
state_dict = {}
for k, v in checkpoint['state_dict'].items():
state_dict[k[6:]] = v
else:
state_dict = checkpoint
self.model.load_state_dict(state_dict)
self.model.eval().to(device)
@torch.no_grad()
def __call__(self, image):
# image: np.ndarray, uint8, [H, W, 3]
H, W = image.shape[:2]
image = Image.fromarray(image)
image = self.aug(image).unsqueeze(0).to(self.device)
if self.task == 'depth':
depth = self.model(image).clamp(0, 1)
depth = F.interpolate(depth.unsqueeze(1), size=(H, W), mode='bicubic', align_corners=False)
depth = depth.squeeze(1).cpu().numpy()
return depth
else:
normal = self.model(image).clamp(0, 1)
normal = F.interpolate(normal, size=(H, W), mode='bicubic', align_corners=False)
normal = normal.cpu().numpy()
return normal
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str, help="path to image (png, jpeg, etc.)")
parser.add_argument('--size', default=256, type=int, help="output resolution")
parser.add_argument('--border_ratio', default=0.2, type=float, help="output border ratio")
parser.add_argument('--recenter', type=bool, default=True, help="recenter, potentially not helpful for multiview zero123")
parser.add_argument('--dont_recenter', dest='recenter', action='store_false')
opt = parser.parse_args()
out_dir = os.path.dirname(opt.path)
out_rgba = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_rgba.png')
out_depth = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_depth.png')
out_normal = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_normal.png')
out_caption = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_caption.txt')
# load image
print(f'[INFO] loading image...')
image = cv2.imread(opt.path, cv2.IMREAD_UNCHANGED)
if image.shape[-1] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# carve background
print(f'[INFO] background removal...')
carved_image = BackgroundRemoval()(image) # [H, W, 4]
mask = carved_image[..., -1] > 0
# predict depth
print(f'[INFO] depth estimation...')
dpt_depth_model = DPT(task='depth')
depth = dpt_depth_model(image)[0]
depth[mask] = (depth[mask] - depth[mask].min()) / (depth[mask].max() - depth[mask].min() + 1e-9)
depth[~mask] = 0
depth = (depth * 255).astype(np.uint8)
del dpt_depth_model
# predict normal
print(f'[INFO] normal estimation...')
dpt_normal_model = DPT(task='normal')
normal = dpt_normal_model(image)[0]
normal = (normal * 255).astype(np.uint8).transpose(1, 2, 0)
normal[~mask] = 0
del dpt_normal_model
# recenter
if opt.recenter:
print(f'[INFO] recenter...')
final_rgba = np.zeros((opt.size, opt.size, 4), dtype=np.uint8)
final_depth = np.zeros((opt.size, opt.size), dtype=np.uint8)
final_normal = np.zeros((opt.size, opt.size, 3), dtype=np.uint8)
coords = np.nonzero(mask)
x_min, x_max = coords[0].min(), coords[0].max()
y_min, y_max = coords[1].min(), coords[1].max()
h = x_max - x_min
w = y_max - y_min
desired_size = int(opt.size * (1 - opt.border_ratio))
scale = desired_size / max(h, w)
h2 = int(h * scale)
w2 = int(w * scale)
x2_min = (opt.size - h2) // 2
x2_max = x2_min + h2
y2_min = (opt.size - w2) // 2
y2_max = y2_min + w2
final_rgba[x2_min:x2_max, y2_min:y2_max] = cv2.resize(carved_image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
final_depth[x2_min:x2_max, y2_min:y2_max] = cv2.resize(depth[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
final_normal[x2_min:x2_max, y2_min:y2_max] = cv2.resize(normal[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
else:
final_rgba = carved_image
final_depth = depth
final_normal = normal
# write output
cv2.imwrite(out_rgba, cv2.cvtColor(final_rgba, cv2.COLOR_RGBA2BGRA))
cv2.imwrite(out_depth, final_depth)
cv2.imwrite(out_normal, final_normal)
# predict caption (it's too slow... use your brain instead)
# print(f'[INFO] captioning...')
# blip2 = BLIP2()
# caption = blip2(image)
# with open(out_caption, 'w') as f:
# f.write(caption)