Spaces:
Runtime error
Runtime error
File size: 4,294 Bytes
6907879 aaa1b64 6907879 aaa1b64 6907879 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
import time
import cv2
import numpy as np
import onnxruntime
from app.detector.yolov8.utils import xywh2xyxy, draw_detections, multiclass_nms
class YOLOv8:
def __init__(self, path, conf_thres=0.7, iou_thres=0.5):
self.conf_threshold = conf_thres
self.iou_threshold = iou_thres
# Initialize model
self.initialize_model(path)
def __call__(self, image):
return self.detect_objects(image)
def set_conf_threshold(self, conf_thres):
self.conf_threshold = conf_thres
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):
input_tensor = self.prepare_input(image)
# Perform inference on the image
outputs = self.inference(input_tensor)
self.boxes, self.scores, self.class_ids = self.process_output(outputs)
return self.boxes, self.scores, self.class_ids
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, input_tensor):
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")
return outputs
def process_output(self, output):
predictions = np.squeeze(output[0]).T
# Filter out object confidence scores below threshold
scores = np.max(predictions[:, 4:], axis=1)
predictions = predictions[scores > self.conf_threshold, :]
scores = scores[scores > self.conf_threshold]
if len(scores) == 0:
return [], [], []
# Get the class with the highest confidence
class_ids = np.argmax(predictions[:, 4:], axis=1)
# Get bounding boxes for each object
boxes = self.extract_boxes(predictions)
# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
# indices = nms(boxes, scores, self.iou_threshold)
indices = multiclass_nms(boxes, scores, class_ids, self.iou_threshold)
return boxes[indices], scores[indices], class_ids[indices]
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, draw_scores=True, mask_alpha=0.4):
return draw_detections(
image, self.boxes, self.scores, self.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))]
|