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import os
from functools import partial
from typing import List, Union
import numpy as np
import numpy.typing as npt
import torch
import plasma.huggingface as hf
from .module.yolov5.yolo_utils.datasets import letterbox
from .module.yolov5.yolo_utils.general import non_max_suppression, scale_coords
from .preprocess import create_batch, process_image
from .utils import (
DETECTOR_WEIGHT_ID,
check_image_shape,
load_yolo_model,
)
class StampDetector:
def __init__(
self, model_path: Union[str, None] = None, device: str = "cpu", conf_thres: float = 0.5, iou_thres: float = 0.3
) -> None:
"""Create an object for stamp detection"""
# assert device == "cpu", "Currently only support cpu inference"
checkpoint = hf.download_file(model_path)
print(model_path)
print(checkpoint)
self.device = device
self.model, self.stride = load_yolo_model(checkpoint, device="cpu")
self.img_size = 640
self.conf_thres = conf_thres
self.iou_thres = iou_thres
self.process_func_ = partial(process_image, device="cpu")
def __call__(self, image_list: Union[List[npt.NDArray], npt.NDArray]) -> List[npt.NDArray]:
"""Returns a list of bounding boxes [xmin, ymin, xmax, ymax] for each image in image_list
Each element in the list is a numpy array of shape N x 4
Args:
image_list (Union[List[npt.NDArray], npt.NDArray]): input images
Returns:
[List[np.ndarray]]: output bounding boxes
"""
if not isinstance(image_list, (np.ndarray, list)):
raise TypeError("Invalid Type: Input must be of type list or np.ndarray")
if len(image_list) > 0:
check_image_shape(image_list[0])
else:
return []
return self.__detect(image_list) # type: ignore
def __detect(self, image_list): # type: ignore
"""
Use __call__ method
"""
batches, indices = create_batch(image_list, set(list(x.shape for x in image_list)))
predictions = []
for origin_images in batches:
images = [letterbox(x, 640, stride=32)[0] for x in origin_images] # type: ignore
images = list(map(self.process_func_, images))
tensor = torch.stack(images).half()
with torch.no_grad():
pred = self.model(tensor)[0]
all_boxes = []
pred = non_max_suppression(pred, 0.3, 0.30, classes=0, agnostic=1) # type: ignore
for idx, det in enumerate(pred):
if len(det):
det[:, :4] = scale_coords(images[idx].shape[1:], det[:, :4], origin_images[0].shape) # type: ignore
det = det[:, :4].round()
all_boxes.append(det.cpu().numpy().astype("int").tolist())
else:
all_boxes.append([])
predictions.extend(all_boxes)
z = zip(predictions, indices)
sorted_result = sorted(z, key=lambda x: x[1])
predictions, _ = zip(*sorted_result)
return list(predictions)
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