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import time
import cv2
import numpy as np
import onnxruntime
from utils import draw_detections
class YOLOv10:
def __init__(self, path):
# Initialize model
self.initialize_model(path)
def __call__(self, image):
return self.detect_objects(image)
def initialize_model(self, path):
self.session = onnxruntime.InferenceSession(
path, providers=onnxruntime.get_available_providers()
)
# Get model info
self.get_input_details()
self.get_output_details()
def detect_objects(self, image, conf_threshold=0.3):
input_tensor = self.prepare_input(image)
# Perform inference on the image
new_image = self.inference(image, input_tensor, conf_threshold)
return new_image
def prepare_input(self, image):
self.img_height, self.img_width = image.shape[:2]
input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Resize input image
input_img = cv2.resize(input_img, (self.input_width, self.input_height))
# Scale input pixel values to 0 to 1
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])
# Filter out object confidence scores below threshold
scores = predictions[:, 4]
predictions = predictions[scores > conf_threshold, :]
scores = scores[scores > conf_threshold]
if len(scores) == 0:
return [], [], []
# Get the class with the highest confidence
class_ids = predictions[:, 5].astype(int)
# Get bounding boxes for each object
boxes = self.extract_boxes(predictions)
return boxes, scores, class_ids
def extract_boxes(self, predictions):
# Extract boxes from predictions
boxes = predictions[:, :4]
# Scale boxes to original image dimensions
boxes = self.rescale_boxes(boxes)
# Convert boxes to xyxy format
#boxes = xywh2xyxy(boxes)
return boxes
def rescale_boxes(self, boxes):
# Rescale boxes to original image dimensions
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))]
if __name__ == "__main__":
import requests
import tempfile
from huggingface_hub import hf_hub_download
model_file = hf_hub_download(
repo_id="onnx-community/yolov10s", filename="onnx/model.onnx"
)
yolov8_detector = YOLOv10(model_file)
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
f.write(
requests.get(
"https://live.staticflickr.com/13/19041780_d6fd803de0_3k.jpg"
).content
)
f.seek(0)
img = cv2.imread(f.name)
# # Detect Objects
combined_image = yolov8_detector.detect_objects(img)
# Draw detections
cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
cv2.imshow("Output", combined_image)
cv2.waitKey(0)
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