YOLOv8从数据准备到模型预测 - 简书 (jianshu.com)
训练好了模型，可以导出onnx部署
model.export(format='onnx') # dynamic=True
onnx模型只给出了预测框的中间结果，是一个 1x(4+class_num)x8400的数组，即8400个检测框，每个框的xywh和每个类别的score。还需要：
1.计算每个预测框的最大得分
2.根据阈值过滤掉不符合条件的框
3.计算每个预测框的类别（最大score的index）
4.使用NMS过滤得到最终的结果
import time
import cv2
import numpy as np
import onnxruntime
class YOLOv8:
def __init__(self, path, conf_thres=0.7, iou_thres=0.7):
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 initialize_model(self, path):
self.session = onnxruntime.InferenceSession(path,providers=['CUDAExecutionProvider','CPUExecutionProvider'])
# Get model info
self.get_input_details()
self.get_output_details()
def detect_objects(self, image):
input_tensor,ratio = 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,ratio)
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图片不要直接使用resize，需要按比例缩放，空白区域填空纯色即可
input_img,ratio = self.ratioresize(input_img)
# 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,ratio
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,ratio):
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,ratio)
# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
indices = self.nms(boxes, scores, self.iou_threshold)
return boxes[indices], scores[indices], class_ids[indices]
def extract_boxes(self, predictions,ratio):
# Extract boxes from predictions
boxes = predictions[:, :4]
# Scale boxes to original image dimensions
# boxes = self.rescale_boxes(boxes)
boxes *= ratio
# Convert boxes to xyxy format
boxes = self.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 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))]
#等比例缩放图片
def ratioresize(self,im, color=114):
shape = im.shape[:2]
new_h, new_w = self.input_height, self.input_width
padded_img = np.ones((new_h, new_w, 3), dtype=np.uint8) * color
# Scale ratio (new / old)
r = min(new_h / shape[0], new_w / shape[1])
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
if shape[::-1] != new_unpad:
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
padded_img[: new_unpad[1], : new_unpad[0]] = im
padded_img = np.ascontiguousarray(padded_img)
return padded_img, 1 / r
def nms(self, boxes, scores, iou_threshold):
# Sort by score
sorted_indices = np.argsort(scores)[::-1]
keep_boxes = []
while sorted_indices.size > 0:
# Pick the last box
box_id = sorted_indices[0]
keep_boxes.append(box_id)
# Compute IoU of the picked box with the rest
ious = self.compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
# Remove boxes with IoU over the threshold
keep_indices = np.where(ious < iou_threshold)[0]
# print(keep_indices.shape, sorted_indices.shape)
sorted_indices = sorted_indices[keep_indices + 1]
return keep_boxes
def compute_iou(self, box, boxes):
# Compute xmin, ymin, xmax, ymax for both boxes
xmin = np.maximum(box[0], boxes[:, 0])
ymin = np.maximum(box[1], boxes[:, 1])
xmax = np.minimum(box[2], boxes[:, 2])
ymax = np.minimum(box[3], boxes[:, 3])
# Compute intersection area
intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
# Compute union area
box_area = (box[2] - box[0]) * (box[3] - box[1])
boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
union_area = box_area + boxes_area - intersection_area
# Compute IoU
iou = intersection_area / union_area
return iou
def xywh2xyxy(self, x):
# Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
y = np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2
y[..., 1] = x[..., 1] - x[..., 3] / 2
y[..., 2] = x[..., 0] + x[..., 2] / 2
y[..., 3] = x[..., 1] + x[..., 3] / 2
return y
if __name__ == "__main__":
yolov8_detector = YOLOv8(model_path, conf_thres=0.7, iou_thres=0.7)
image = cv2.imread()
boxes, scores, class_ids = yolov8_detector(image)
print(boxes, scores, class_ids)
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