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import os | |
import khandy | |
import numpy as np | |
from .base import OnnxModel | |
from .base import check_image_dtype_and_shape | |
class InsectDetector(OnnxModel): | |
def __init__(self): | |
current_dir = os.path.dirname(os.path.abspath(__file__)) | |
model_path = os.path.join( | |
current_dir, | |
"__pycache__/MuGemSt/insecta/quarrying_insect_detector.onnx", | |
) | |
self.input_width = 640 | |
self.input_height = 640 | |
super(InsectDetector, self).__init__(model_path) | |
def _preprocess(self, image): | |
check_image_dtype_and_shape(image) | |
# image size normalization | |
image, scale, pad_left, pad_top = khandy.letterbox_image( | |
image, self.input_width, self.input_height, 0, return_scale=True | |
) | |
# image channel normalization | |
image = khandy.normalize_image_channel(image, swap_rb=True) | |
# image dtype normalization | |
image = khandy.rescale_image(image, "auto", np.float32) | |
# to tensor | |
image = np.transpose(image, (2, 0, 1)) | |
image = np.expand_dims(image, axis=0) | |
return image, scale, pad_left, pad_top | |
def _post_process( | |
self, outputs_list, scale, pad_left, pad_top, conf_thresh, iou_thresh | |
): | |
pred = outputs_list[0][0] | |
pass_t = pred[:, 4] > conf_thresh | |
pred = pred[pass_t] | |
boxes = khandy.convert_boxes_format(pred[:, :4], "cxcywh", "xyxy") | |
boxes = khandy.unletterbox_2d_points(boxes, scale, pad_left, pad_top, False) | |
confs = np.max(pred[:, 5:] * pred[:, 4:5], axis=-1) | |
classes = np.argmax(pred[:, 5:] * pred[:, 4:5], axis=-1) | |
keep = khandy.non_max_suppression(boxes, confs, iou_thresh) | |
return boxes[keep], confs[keep], classes[keep] | |
def detect(self, image, conf_thresh=0.5, iou_thresh=0.5): | |
image, scale, pad_left, pad_top = self._preprocess(image) | |
outputs_list = self.forward(image) | |
boxes, confs, classes = self._post_process( | |
outputs_list, | |
scale=scale, | |
pad_left=pad_left, | |
pad_top=pad_top, | |
conf_thresh=conf_thresh, | |
iou_thresh=iou_thresh, | |
) | |
return boxes, confs, classes | |