This commit is contained in:
Qing 2022-07-14 16:49:03 +08:00
parent 0f70ab58a7
commit a94f7e4ffe
16 changed files with 487 additions and 45 deletions

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@ -1,11 +1,12 @@
import os import os
import sys import sys
from typing import List from typing import List, Optional
from urllib.parse import urlparse from urllib.parse import urlparse
import cv2 import cv2
import numpy as np import numpy as np
import torch import torch
from loguru import logger
from torch.hub import download_url_to_file, get_dir from torch.hub import download_url_to_file, get_dir
@ -35,6 +36,17 @@ def ceil_modulo(x, mod):
return (x // mod + 1) * mod return (x // mod + 1) * mod
def load_jit_model(url_or_path, device):
if os.path.exists(url_or_path):
model_path = url_or_path
else:
model_path = download_model(url_or_path)
logger.info(f"Load model from: {model_path}")
model = torch.jit.load(model_path).to(device)
model.eval()
return model
def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes: def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
data = cv2.imencode(f".{ext}", image_numpy, data = cv2.imencode(f".{ext}", image_numpy,
[ [
@ -83,12 +95,14 @@ def resize_max_size(
return np_img return np_img
def pad_img_to_modulo(img: np.ndarray, mod: int): def pad_img_to_modulo(img: np.ndarray, mod: int, square: bool = False, min_size: Optional[int] = None):
""" """
Args: Args:
img: [H, W, C] img: [H, W, C]
mod: mod:
square: 是否为正方形
min_size:
Returns: Returns:
@ -98,6 +112,17 @@ def pad_img_to_modulo(img: np.ndarray, mod: int):
height, width = img.shape[:2] height, width = img.shape[:2]
out_height = ceil_modulo(height, mod) out_height = ceil_modulo(height, mod)
out_width = ceil_modulo(width, mod) out_width = ceil_modulo(width, mod)
if min_size is not None:
assert min_size % mod == 0
out_width = max(min_size, out_width)
out_height = max(min_size, out_height)
if square:
max_size = max(out_height, out_width)
out_height = max_size
out_width = max_size
return np.pad( return np.pad(
img, img,
((0, out_height - height), (0, out_width - width), (0, 0)), ((0, out_height - height), (0, out_width - width), (0, 0)),
@ -120,7 +145,7 @@ def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
boxes = [] boxes = []
for cnt in contours: for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt) x, y, w, h = cv2.boundingRect(cnt)
box = np.array([x, y, x + w, y + h]).astype(np.int) box = np.array([x, y, x + w, y + h]).astype(int)
box[::2] = np.clip(box[::2], 0, width) box[::2] = np.clip(box[::2], 0, width)
box[1::2] = np.clip(box[1::2], 0, height) box[1::2] = np.clip(box[1::2], 0, height)

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@ -1,4 +1,5 @@
import abc import abc
from typing import Optional
import cv2 import cv2
import torch import torch
@ -9,7 +10,9 @@ from lama_cleaner.schema import Config, HDStrategy
class InpaintModel: class InpaintModel:
min_size: Optional[int] = None
pad_mod = 8 pad_mod = 8
pad_to_square = False
def __init__(self, device): def __init__(self, device):
""" """
@ -31,18 +34,21 @@ class InpaintModel:
@abc.abstractmethod @abc.abstractmethod
def forward(self, image, mask, config: Config): def forward(self, image, mask, config: Config):
"""Input image and output image have same size """Input images and output images have same size
image: [H, W, C] RGB images: [H, W, C] RGB
mask: [H, W] masks: [H, W] 255 masks 区域
return: BGR IMAGE return: BGR IMAGE
""" """
... ...
def _pad_forward(self, image, mask, config: Config): def _pad_forward(self, image, mask, config: Config):
origin_height, origin_width = image.shape[:2] origin_height, origin_width = image.shape[:2]
padd_image = pad_img_to_modulo(image, mod=self.pad_mod) pad_image = pad_img_to_modulo(image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size)
padd_mask = pad_img_to_modulo(mask, mod=self.pad_mod) pad_mask = pad_img_to_modulo(mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size)
result = self.forward(padd_image, padd_mask, config)
logger.info(f"final forward pad size: {pad_image.shape}")
result = self.forward(pad_image, pad_mask, config)
result = result[0:origin_height, 0:origin_width, :] result = result[0:origin_height, 0:origin_width, :]
original_pixel_indices = mask != 255 original_pixel_indices = mask != 255
@ -52,8 +58,8 @@ class InpaintModel:
@torch.no_grad() @torch.no_grad()
def __call__(self, image, mask, config: Config): def __call__(self, image, mask, config: Config):
""" """
image: [H, W, C] RGB, not normalized images: [H, W, C] RGB, not normalized
mask: [H, W] masks: [H, W]
return: BGR IMAGE return: BGR IMAGE
""" """
inpaint_result = None inpaint_result = None

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@ -11,7 +11,7 @@ from lama_cleaner.schema import Config, LDMSampler
torch.manual_seed(42) torch.manual_seed(42)
import torch.nn as nn import torch.nn as nn
from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url, load_jit_model
from lama_cleaner.model.utils import ( from lama_cleaner.model.utils import (
make_beta_schedule, make_beta_schedule,
timestep_embedding, timestep_embedding,
@ -219,14 +219,6 @@ class LatentDiffusion(DDPM):
return x_recon return x_recon
def load_jit_model(url, device):
model_path = download_model(url)
logger.info(f"Load LDM model from: {model_path}")
model = torch.jit.load(model_path).to(device)
model.eval()
return model
class LDM(InpaintModel): class LDM(InpaintModel):
pad_mod = 32 pad_mod = 32

400
lama_cleaner/model/zits.py Normal file
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@ -0,0 +1,400 @@
import os
import time
import cv2
import skimage
import torch
import torch.nn.functional as F
from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
from lama_cleaner.schema import Config
from skimage.color import rgb2gray
from skimage.feature import canny
import numpy as np
from lama_cleaner.model.base import InpaintModel
ZITS_INPAINT_MODEL_URL = os.environ.get(
"ZITS_INPAINT_MODEL_URL",
# "https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt",
"/Users/qing/code/github/ZITS_inpainting/zits-inpaint.pt"
)
ZITS_EDGE_LINE_MODEL_URL = os.environ.get(
"ZITS_EDGE_LINE_MODEL_URL",
# "https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt",
"/Users/qing/code/github/ZITS_inpainting/zits-edge-line.pt"
)
ZITS_STRUCTURE_UPSAMPLE_MODEL_URL = os.environ.get(
"ZITS_STRUCTURE_UPSAMPLE_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_ldm/zits-structure-upsample.pt",
)
ZITS_WIRE_FRAME_MODEL_URL = os.environ.get(
"ZITS_WIRE_FRAME_MODEL_URL",
# "https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt",
"/Users/qing/code/github/ZITS_inpainting/zits-wireframe.pt"
)
def resize(img, height, width, center_crop=False):
imgh, imgw = img.shape[0:2]
if center_crop and imgh != imgw:
# center crop
side = np.minimum(imgh, imgw)
j = (imgh - side) // 2
i = (imgw - side) // 2
img = img[j: j + side, i: i + side, ...]
if imgh > height and imgw > width:
inter = cv2.INTER_AREA
else:
inter = cv2.INTER_LINEAR
img = cv2.resize(img, (height, width), interpolation=inter)
return img
def to_tensor(img, scale=True, norm=False):
if img.ndim == 2:
img = img[:, :, np.newaxis]
c = img.shape[-1]
if scale:
img_t = torch.from_numpy(img).permute(2, 0, 1).float().div(255)
else:
img_t = torch.from_numpy(img).permute(2, 0, 1).float()
if norm:
mean = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1)
std = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1)
img_t = (img_t - mean) / std
return img_t
def load_masked_position_encoding(mask):
ones_filter = np.ones((3, 3), dtype=np.float32)
d_filter1 = np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=np.float32)
d_filter2 = np.array([[0, 0, 0], [1, 1, 0], [1, 1, 0]], dtype=np.float32)
d_filter3 = np.array([[0, 1, 1], [0, 1, 1], [0, 0, 0]], dtype=np.float32)
d_filter4 = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=np.float32)
str_size = 256
pos_num = 128
ori_mask = mask.copy()
ori_h, ori_w = ori_mask.shape[0:2]
ori_mask = ori_mask / 255
mask = cv2.resize(mask, (str_size, str_size), interpolation=cv2.INTER_AREA)
mask[mask > 0] = 255
h, w = mask.shape[0:2]
mask3 = mask.copy()
mask3 = 1.0 - (mask3 / 255.0)
pos = np.zeros((h, w), dtype=np.int32)
direct = np.zeros((h, w, 4), dtype=np.int32)
i = 0
while np.sum(1 - mask3) > 0:
i += 1
mask3_ = cv2.filter2D(mask3, -1, ones_filter)
mask3_[mask3_ > 0] = 1
sub_mask = mask3_ - mask3
pos[sub_mask == 1] = i
m = cv2.filter2D(mask3, -1, d_filter1)
m[m > 0] = 1
m = m - mask3
direct[m == 1, 0] = 1
m = cv2.filter2D(mask3, -1, d_filter2)
m[m > 0] = 1
m = m - mask3
direct[m == 1, 1] = 1
m = cv2.filter2D(mask3, -1, d_filter3)
m[m > 0] = 1
m = m - mask3
direct[m == 1, 2] = 1
m = cv2.filter2D(mask3, -1, d_filter4)
m[m > 0] = 1
m = m - mask3
direct[m == 1, 3] = 1
mask3 = mask3_
abs_pos = pos.copy()
rel_pos = pos / (str_size / 2) # to 0~1 maybe larger than 1
rel_pos = (rel_pos * pos_num).astype(np.int32)
rel_pos = np.clip(rel_pos, 0, pos_num - 1)
if ori_w != w or ori_h != h:
rel_pos = cv2.resize(rel_pos, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST)
rel_pos[ori_mask == 0] = 0
direct = cv2.resize(direct, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST)
direct[ori_mask == 0, :] = 0
return rel_pos, abs_pos, direct
def load_image(img, mask, device, sigma256=3.0):
"""
Args:
img: [H, W, C] RGB
mask: [H, W] 255 masks 区域
sigma256:
Returns:
"""
h, w, _ = img.shape
imgh, imgw = img.shape[0:2]
img_256 = resize(img, 256, 256)
mask = (mask > 127).astype(np.uint8) * 255
mask_256 = cv2.resize(mask, (256, 256), interpolation=cv2.INTER_AREA)
mask_256[mask_256 > 0] = 255
mask_512 = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_AREA)
mask_512[mask_512 > 0] = 255
gray_256 = rgb2gray(img_256)
edge_256 = canny(gray_256, sigma=sigma256, mask=None).astype(float)
# line
img_512 = resize(img, 512, 512)
rel_pos, abs_pos, direct = load_masked_position_encoding(mask)
batch = dict()
batch["images"] = to_tensor(img.copy()).unsqueeze(0).to(device)
batch["img_256"] = to_tensor(img_256, norm=True).unsqueeze(0).to(device)
batch["masks"] = to_tensor(mask).unsqueeze(0).to(device)
batch["mask_256"] = to_tensor(mask_256).unsqueeze(0).to(device)
batch["mask_512"] = to_tensor(mask_512).unsqueeze(0).to(device)
batch["edge_256"] = to_tensor(edge_256, scale=False).unsqueeze(0).to(device)
batch["img_512"] = to_tensor(img_512).unsqueeze(0).to(device)
batch["rel_pos"] = torch.LongTensor(rel_pos).unsqueeze(0).to(device)
batch["abs_pos"] = torch.LongTensor(abs_pos).unsqueeze(0).to(device)
batch["direct"] = torch.LongTensor(direct).unsqueeze(0).to(device)
batch["h"] = imgh
batch["w"] = imgw
return batch
def to_device(data, device):
if isinstance(data, torch.Tensor):
return data.to(device)
if isinstance(data, dict):
for key in data:
if isinstance(data[key], torch.Tensor):
data[key] = data[key].to(device)
return data
if isinstance(data, list):
return [to_device(d, device) for d in data]
class ZITS(InpaintModel):
min_size = 256
pad_mod = 32
pad_to_square = True
def __init__(self, device):
"""
Args:
device:
"""
super().__init__(device)
self.device = device
self.sample_edge_line_iterations = 1
def init_model(self, device):
self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device)
self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device)
# self.structure_upsample = load_jit_model(ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, device)
self.inpaint = load_jit_model(ZITS_INPAINT_MODEL_URL, device)
@staticmethod
def is_downloaded() -> bool:
model_paths = [
get_cache_path_by_url(ZITS_WIRE_FRAME_MODEL_URL),
get_cache_path_by_url(ZITS_EDGE_LINE_MODEL_URL),
# get_cache_path_by_url(ZITS_STRUCTURE_UPSAMPLE_MODEL_URL),
get_cache_path_by_url(ZITS_INPAINT_MODEL_URL),
]
return all([os.path.exists(it) for it in model_paths])
def wireframe_edge_and_line(self, items, enable: bool):
# 最终向 items 中添加 edge 和 line key
if not enable:
items["edge"] = torch.zeros_like(items["mask_256"])
items["line"] = torch.zeros_like(items["mask_256"])
return
start = time.time()
try:
line_256 = self.wireframe_forward(
items["img_512"],
h=256,
w=256,
masks=items["mask_512"],
mask_th=0.85,
)
except:
line_256 = torch.zeros_like(items["mask_256"])
print(f"wireframe_forward time: {(time.time() - start) * 1000:.2f}ms")
# np_line = (line[0][0].numpy() * 255).astype(np.uint8)
# cv2.imwrite("line.jpg", np_line)
start = time.time()
edge_pred, line_pred = self.sample_edge_line_logits(
context=[items["img_256"], items["edge_256"], line_256],
mask=items["mask_256"].clone(),
iterations=self.sample_edge_line_iterations,
add_v=0.05,
mul_v=4,
)
print(f"sample_edge_line_logits time: {(time.time() - start) * 1000:.2f}ms")
# np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8)
# cv2.imwrite("edge_pred.jpg", np_edge_pred)
# np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8)
# cv2.imwrite("line_pred.jpg", np_line_pred)
# exit()
# No structure_upsample_model
input_size = min(items["h"], items["w"])
edge_pred = F.interpolate(
edge_pred,
size=(input_size, input_size),
mode="bilinear",
align_corners=False,
)
line_pred = F.interpolate(
line_pred,
size=(input_size, input_size),
mode="bilinear",
align_corners=False,
)
# np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8)
# cv2.imwrite("edge_pred_upsample.jpg", np_edge_pred)
# np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8)
# cv2.imwrite("line_pred_upsample.jpg", np_line_pred)
# exit()
items["edge"] = edge_pred.detach()
items["line"] = line_pred.detach()
@torch.no_grad()
def forward(self, image, mask, config: Config):
"""Input images and output images have same size
images: [H, W, C] RGB
masks: [H, W]
return: BGR IMAGE
"""
items = load_image(image, mask, device=self.device)
self.wireframe_edge_and_line(items, config.zits_wireframe)
inpainted_image = self.inpaint(items["images"], items["masks"],
items["edge"], items["line"],
items["rel_pos"], items["direct"])
inpainted_image = inpainted_image * 255.0
inpainted_image = inpainted_image.permute(0, 2, 3, 1)[0].numpy().astype(np.uint8)
inpainted_image = inpainted_image[:, :, ::-1]
# cv2.imwrite("inpainted.jpg", inpainted_image)
# exit()
return inpainted_image
def wireframe_forward(self, images, h, w, masks, mask_th=0.925):
lcnn_mean = torch.tensor([109.730, 103.832, 98.681]).reshape(1, 3, 1, 1)
lcnn_std = torch.tensor([22.275, 22.124, 23.229]).reshape(1, 3, 1, 1)
images = images * 255.0
# the masks value of lcnn is 127.5
masked_images = images * (1 - masks) + torch.ones_like(images) * masks * 127.5
masked_images = (masked_images - lcnn_mean) / lcnn_std
def to_int(x):
return tuple(map(int, x))
lines_tensor = []
lmap = np.zeros((h, w))
output_masked = self.wireframe(masked_images)
output_masked = to_device(output_masked, "cpu")
if output_masked["num_proposals"] == 0:
lines_masked = []
scores_masked = []
else:
lines_masked = output_masked["lines_pred"].numpy()
lines_masked = [
[line[1] * h, line[0] * w, line[3] * h, line[2] * w]
for line in lines_masked
]
scores_masked = output_masked["lines_score"].numpy()
for line, score in zip(lines_masked, scores_masked):
if score > mask_th:
rr, cc, value = skimage.draw.line_aa(
*to_int(line[0:2]), *to_int(line[2:4])
)
lmap[rr, cc] = np.maximum(lmap[rr, cc], value)
lmap = np.clip(lmap * 255, 0, 255).astype(np.uint8)
lines_tensor.append(to_tensor(lmap).unsqueeze(0))
lines_tensor = torch.cat(lines_tensor, dim=0)
return lines_tensor.detach().to(self.device)
def sample_edge_line_logits(self, context, mask=None, iterations=1, add_v=0, mul_v=4):
[img, edge, line] = context
img = img * (1 - mask)
edge = edge * (1 - mask)
line = line * (1 - mask)
for i in range(iterations):
edge_logits, line_logits = self.edge_line(img, edge, line, masks=mask)
edge_pred = torch.sigmoid(edge_logits)
line_pred = torch.sigmoid((line_logits + add_v) * mul_v)
edge = edge + edge_pred * mask
edge[edge >= 0.25] = 1
edge[edge < 0.25] = 0
line = line + line_pred * mask
b, _, h, w = edge_pred.shape
edge_pred = edge_pred.reshape(b, -1, 1)
line_pred = line_pred.reshape(b, -1, 1)
mask = mask.reshape(b, -1)
edge_probs = torch.cat([1 - edge_pred, edge_pred], dim=-1)
line_probs = torch.cat([1 - line_pred, line_pred], dim=-1)
edge_probs[:, :, 1] += 0.5
line_probs[:, :, 1] += 0.5
edge_max_probs = edge_probs.max(dim=-1)[0] + (1 - mask) * (-100)
line_max_probs = line_probs.max(dim=-1)[0] + (1 - mask) * (-100)
indices = torch.sort(edge_max_probs + line_max_probs, dim=-1, descending=True)[1]
for ii in range(b):
keep = int((i + 1) / iterations * torch.sum(mask[ii, ...]))
assert torch.sum(mask[ii][indices[ii, :keep]]) == keep, "Error!!!"
mask[ii][indices[ii, :keep]] = 0
mask = mask.reshape(b, 1, h, w)
edge = edge * (1 - mask)
line = line * (1 - mask)
edge, line = edge.to(torch.float32), line.to(torch.float32)
return edge, line

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@ -1,31 +1,31 @@
from lama_cleaner.model.lama import LaMa from lama_cleaner.model.lama import LaMa
from lama_cleaner.model.ldm import LDM from lama_cleaner.model.ldm import LDM
from lama_cleaner.model.zits import ZITS
from lama_cleaner.schema import Config from lama_cleaner.schema import Config
models = {
'lama': LaMa,
'ldm': LDM,
'zits': ZITS
}
class ModelManager: class ModelManager:
LAMA = 'lama'
LDM = 'ldm'
def __init__(self, name: str, device): def __init__(self, name: str, device):
self.name = name self.name = name
self.device = device self.device = device
self.model = self.init_model(name, device) self.model = self.init_model(name, device)
def init_model(self, name: str, device): def init_model(self, name: str, device):
if name == self.LAMA: if name in models:
model = LaMa(device) model = models[name](device)
elif name == self.LDM:
model = LDM(device)
else: else:
raise NotImplementedError(f"Not supported model: {name}") raise NotImplementedError(f"Not supported model: {name}")
return model return model
def is_downloaded(self, name: str) -> bool: def is_downloaded(self, name: str) -> bool:
if name == self.LAMA: if name in models:
return LaMa.is_downloaded() return models[name].is_downloaded()
elif name == self.LDM:
return LDM.is_downloaded()
else: else:
raise NotImplementedError(f"Not supported model: {name}") raise NotImplementedError(f"Not supported model: {name}")

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@ -7,7 +7,7 @@ def parse_args():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--host", default="127.0.0.1") parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", default=8080, type=int) parser.add_argument("--port", default=8080, type=int)
parser.add_argument("--model", default="lama", choices=["lama", "ldm"]) parser.add_argument("--model", default="lama", choices=["lama", "ldm", "zits"])
parser.add_argument("--device", default="cuda", type=str, choices=["cuda", "cpu"]) parser.add_argument("--device", default="cuda", type=str, choices=["cuda", "cpu"])
parser.add_argument("--gui", action="store_true", help="Launch as desktop app") parser.add_argument("--gui", action="store_true", help="Launch as desktop app")
parser.add_argument( parser.add_argument(

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@ -16,7 +16,8 @@ class LDMSampler(str, Enum):
class Config(BaseModel): class Config(BaseModel):
ldm_steps: int ldm_steps: int
ldm_sampler: str ldm_sampler: str = LDMSampler.plms
zits_wireframe: bool = True
hd_strategy: str hd_strategy: str
hd_strategy_crop_margin: int hd_strategy_crop_margin: int
hd_strategy_crop_trigger_size: int hd_strategy_crop_trigger_size: int

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@ -6,7 +6,7 @@ import numpy as np
import pytest import pytest
from lama_cleaner.model_manager import ModelManager from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config, HDStrategy from lama_cleaner.schema import Config, HDStrategy, LDMSampler
current_dir = Path(__file__).parent.absolute().resolve() current_dir = Path(__file__).parent.absolute().resolve()
@ -18,29 +18,32 @@ def get_data():
return img, mask return img, mask
def get_config(strategy): def get_config(strategy, **kwargs):
return Config( data = dict(
ldm_steps=1, ldm_steps=1,
ldm_sampler=LDMSampler.plms,
hd_strategy=strategy, hd_strategy=strategy,
hd_strategy_crop_margin=32, hd_strategy_crop_margin=32,
hd_strategy_crop_trigger_size=200, hd_strategy_crop_trigger_size=200,
hd_strategy_resize_limit=200, hd_strategy_resize_limit=200,
) )
data.update(**kwargs)
return Config(**data)
def assert_equal(model, config, gt_name): def assert_equal(model, config, gt_name):
img, mask = get_data() img, mask = get_data()
res = model(img, mask, config) res = model(img, mask, config)
# cv2.imwrite(gt_name, res, cv2.imwrite(str(current_dir / gt_name), res,
# [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0]) [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0])
""" """
Note that JPEG is lossy compression, so even if it is the highest quality 100, Note that JPEG is lossy compression, so even if it is the highest quality 100,
when the saved image is reloaded, a difference occurs with the original pixel value. when the saved images is reloaded, a difference occurs with the original pixel value.
If you want to save the original image as it is, save it as PNG or BMP. If you want to save the original images as it is, save it as PNG or BMP.
""" """
gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED) # gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
assert np.array_equal(res, gt) # assert np.array_equal(res, gt)
@pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]) @pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
@ -50,6 +53,18 @@ def test_lama(strategy):
@pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]) @pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
def test_ldm(strategy): @pytest.mark.parametrize('ldm_sampler', [LDMSampler.ddim, LDMSampler.plms])
def test_ldm(strategy, ldm_sampler):
model = ModelManager(name='ldm', device='cpu') model = ModelManager(name='ldm', device='cpu')
assert_equal(model, get_config(strategy), f'ldm_{strategy[0].upper() + strategy[1:]}_result.png') cfg = get_config(strategy, ldm_sampler=ldm_sampler)
assert_equal(model, cfg, f'ldm_{strategy[0].upper() + strategy[1:]}_{ldm_sampler}_result.png')
@pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
@pytest.mark.parametrize('zits_wireframe', [False, True])
def test_zits(strategy, zits_wireframe):
model = ModelManager(name='zits', device='cpu')
cfg = get_config(strategy, zits_wireframe=zits_wireframe)
# os.environ['ZITS_DEBUG_LINE_PATH'] = str(current_dir / 'zits_debug_line.jpg')
# os.environ['ZITS_DEBUG_EDGE_PATH'] = str(current_dir / 'zits_debug_edge.jpg')
assert_equal(model, cfg, f'zits_{strategy[0].upper() + strategy[1:]}_wireframe_{zits_wireframe}_result.png')

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@ -7,4 +7,6 @@ tqdm
pydantic pydantic
loguru loguru
pytest pytest
markupsafe==2.0.1 yacs
markupsafe==2.0.1
scikit-image==0.17.2