Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,632 Bytes
29d49a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import os
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from ..util import HWC3, resize_image, safe_step
from .ted import TED
class TEEDdetector:
def __init__(self, model):
self.model = model
@classmethod
def from_pretrained(cls, pretrained_model_or_path, filename=None, subfolder=None):
if os.path.isdir(pretrained_model_or_path):
model_path = os.path.join(pretrained_model_or_path, filename)
else:
model_path = hf_hub_download(
pretrained_model_or_path, filename, subfolder=subfolder
)
model = TED()
model.load_state_dict(torch.load(model_path, map_location="cpu"))
return cls(model)
def to(self, device):
self.model.to(device)
return self
def __call__(
self,
input_image,
detect_resolution=512,
safe_steps=2,
output_type="pil",
):
device = next(iter(self.model.parameters())).device
if not isinstance(input_image, np.ndarray):
input_image = np.array(input_image, dtype=np.uint8)
output_type = output_type or "pil"
else:
output_type = output_type or "np"
original_height, original_width, _ = input_image.shape
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
assert input_image.ndim == 3
height, width, _ = input_image.shape
with torch.no_grad():
image_teed = torch.from_numpy(input_image.copy()).float().to(device)
image_teed = rearrange(image_teed, "h w c -> 1 c h w")
edges = self.model(image_teed)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [
cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR)
for e in edges
]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
if safe_steps != 0:
edge = safe_step(edge, safe_steps)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
detected_map = edge
detected_map = HWC3(detected_map)
detected_map = cv2.resize(
detected_map,
(original_width, original_height),
interpolation=cv2.INTER_LINEAR,
)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
|