inference.py

import time
import cv2
import numpy as np
import onnxruntime

try:
    from demo.object_detection.utils import draw_detections
except (ImportError, ModuleNotFoundError):
    from utils import draw_detections

class YOLOv10:
    def __init__(self, path):
        self.initialize_model(path)

    def __call__(self, image):
        return self.detect_objects(image)

    def initialize_model(self, path):
        self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'])
        self.get_input_details()
        self.get_output_details()

    def detect_objects(self, image, conf_threshold=0.3):
        input_tensor = self.prepare_input(image)
        return self.inference(image, input_tensor, conf_threshold)

    def prepare_input(self, image):
        self.img_height, self.img_width = image.shape[:2]
        input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        input_img = cv2.resize(input_img, (self.input_width, self.input_height))
        input_img = input_img / 255.0
        input_img = input_img.transpose(2, 0, 1)
        input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
        return input_tensor

    def inference(self, image, input_tensor, conf_threshold=0.3):
        start = time.perf_counter()
        outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor})
        print(f"Inference time: {(time.perf_counter() - start) * 1000:.2f} ms")
        boxes, scores, class_ids = self.process_output(outputs, conf_threshold)
        return self.draw_detections(image, boxes, scores, class_ids)

    def process_output(self, output, conf_threshold=0.3):
        predictions = np.squeeze(output[0])
        scores = predictions[:, 4]
        predictions = predictions[scores > conf_threshold, :]
        scores = scores[scores > conf_threshold]
        if len(scores) == 0:
            return [], [], []
        class_ids = predictions[:, 5].astype(int)
        boxes = self.extract_boxes(predictions)
        return boxes, scores, class_ids

    def extract_boxes(self, predictions):
        boxes = predictions[:, :4]
        boxes = self.rescale_boxes(boxes)
        return boxes

    def rescale_boxes(self, boxes):
        input_shape = np.array([self.input_width, self.input_height, self.input_width, self.input_height])
        boxes = np.divide(boxes, input_shape, dtype=np.float32)
        boxes *= np.array([self.img_width, self.img_height, self.img_width, self.img_height])
        return boxes

    def draw_detections(self, image, boxes, scores, class_ids, draw_scores=True, mask_alpha=0.4):
        return draw_detections(image, boxes, scores, class_ids, mask_alpha)

    def get_input_details(self):
        model_inputs = self.session.get_inputs()
        self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
        self.input_shape = model_inputs[0].shape
        self.input_height = self.input_shape[2]
        self.input_width = self.input_shape[3]

    def get_output_details(self):
        model_outputs = self.session.get_outputs()
        self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]