56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
import numpy as np
|
|
from typing import List
|
|
|
|
from .base_model import BaseModel
|
|
from .utils import letterbox, get_heatmap_points, \
|
|
get_real_keypoints, refine_keypoints_dark, refine_keypoints, simcc_decoder
|
|
|
|
|
|
class Heatmap(BaseModel):
|
|
def __init__(self,
|
|
model_path: str,
|
|
dark: bool = False,
|
|
device: str = 'CUDA',
|
|
warmup: int = 30):
|
|
super(Heatmap, self).__init__(model_path, device, warmup)
|
|
self.use_dark = dark
|
|
self.img_size = ()
|
|
|
|
def preprocess(self, image: np.ndarray):
|
|
th, tw = self.input_shape[2:]
|
|
self.img_size = image.shape[:2]
|
|
image, _, _, _ = letterbox(image, (tw, th))
|
|
tensor = (image - np.array((103.53, 116.28, 123.675))) / np.array((57.375, 57.12, 58.395))
|
|
tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2)).astype(np.float32)
|
|
return tensor
|
|
|
|
def postprocess(self, tensor: List[np.ndarray]):
|
|
heatmaps = tensor[0]
|
|
heatmaps = np.squeeze(heatmaps, axis=0)
|
|
keypoints = get_heatmap_points(heatmaps)
|
|
if self.use_dark:
|
|
keypoints = refine_keypoints_dark(keypoints, heatmaps, 11)
|
|
else:
|
|
keypoints = refine_keypoints(keypoints, heatmaps)
|
|
keypoints = get_real_keypoints(keypoints, heatmaps, self.img_size)
|
|
return keypoints
|
|
|
|
|
|
class SimCC(BaseModel):
|
|
def __init__(self, model_path: str, device: str = 'CUDA', warmup: int = 30):
|
|
super(SimCC, self).__init__(model_path, device, warmup)
|
|
self.dx = 0
|
|
self.dy = 0
|
|
self.scale = 0
|
|
|
|
def preprocess(self, image: np.ndarray):
|
|
tensor = np.asarray(image).astype(self.input_type, copy=False)
|
|
tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2))
|
|
return tensor
|
|
|
|
def postprocess(self, tensor: List[np.ndarray]):
|
|
keypoints = np.concatenate(
|
|
[tensor[0][0], np.expand_dims(tensor[1][0], axis=-1)], axis=-1
|
|
)
|
|
return keypoints
|