import time
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import cv2
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import numpy as np
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import onnxruntime
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class YOLOv8:
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def __init__(self, path, conf_thres=0.7, iou_thres=0.7):
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self.conf_threshold = conf_thres
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self.iou_threshold = iou_thres
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# Initialize model
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self.initialize_model(path)
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def __call__(self, image):
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return self.detect_objects(image)
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def initialize_model(self, path):
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self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
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# Get model info
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self.get_input_details()
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self.get_output_details()
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def detect_objects(self, image):
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input_tensor, ratio = self.prepare_input(image)
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# Perform inference on the image
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outputs = self.inference(input_tensor)
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self.boxes, self.scores, self.class_ids = self.process_output(outputs, ratio)
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return self.boxes, self.scores, self.class_ids
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def prepare_input(self, image):
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self.img_height, self.img_width = image.shape[:2]
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input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# 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
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input_img, ratio = self.ratioresize(input_img)
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# Scale input pixel values to 0 to 1
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input_img = input_img / 255.0
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input_img = input_img.transpose(2, 0, 1)
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input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
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return input_tensor, ratio
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def inference(self, input_tensor):
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start = time.perf_counter()
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outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor})
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# print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms")
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return outputs
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def process_output(self, output, ratio):
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predictions = np.squeeze(output[0]).T
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# Filter out object confidence scores below threshold
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scores = np.max(predictions[:, 4:], axis=1)
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predictions = predictions[scores > self.conf_threshold, :]
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scores = scores[scores > self.conf_threshold]
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if len(scores) == 0:
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return [], [], []
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# Get the class with the highest confidence
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class_ids = np.argmax(predictions[:, 4:], axis=1)
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# Get bounding boxes for each object
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boxes = self.extract_boxes(predictions, ratio)
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# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
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indices = self.nms(boxes, scores, self.iou_threshold)
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return boxes[indices], scores[indices], class_ids[indices]
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def extract_boxes(self, predictions, ratio):
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# Extract boxes from predictions
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boxes = predictions[:, :4]
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# Scale boxes to original image dimensions
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# boxes = self.rescale_boxes(boxes)
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boxes *= ratio
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# Convert boxes to xyxy format
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boxes = self.xywh2xyxy(boxes)
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return boxes
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def rescale_boxes(self, boxes):
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# Rescale boxes to original image dimensions
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input_shape = np.array([self.input_width, self.input_height, self.input_width, self.input_height])
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boxes = np.divide(boxes, input_shape, dtype=np.float32)
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boxes *= np.array([self.img_width, self.img_height, self.img_width, self.img_height])
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return boxes
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def get_input_details(self):
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model_inputs = self.session.get_inputs()
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self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
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self.input_shape = model_inputs[0].shape
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self.input_height = self.input_shape[2]
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self.input_width = self.input_shape[3]
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def get_output_details(self):
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model_outputs = self.session.get_outputs()
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self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]
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# 等比例缩放图片
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def ratioresize(self, im, color=114):
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shape = im.shape[:2]
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new_h, new_w = self.input_height, self.input_width
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padded_img = np.ones((new_h, new_w, 3), dtype=np.uint8) * color
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# Scale ratio (new / old)
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r = min(new_h / shape[0], new_w / shape[1])
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# Compute padding
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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if shape[::-1] != new_unpad:
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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padded_img[: new_unpad[1], : new_unpad[0]] = im
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padded_img = np.ascontiguousarray(padded_img)
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return padded_img, 1 / r
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def nms(self, boxes, scores, iou_threshold):
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# Sort by score
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sorted_indices = np.argsort(scores)[::-1]
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keep_boxes = []
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while sorted_indices.size > 0:
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# Pick the last box
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box_id = sorted_indices[0]
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keep_boxes.append(box_id)
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# Compute IoU of the picked box with the rest
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ious = self.compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
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# Remove boxes with IoU over the threshold
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keep_indices = np.where(ious < iou_threshold)[0]
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# print(keep_indices.shape, sorted_indices.shape)
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sorted_indices = sorted_indices[keep_indices + 1]
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return keep_boxes
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def compute_iou(self, box, boxes):
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# Compute xmin, ymin, xmax, ymax for both boxes
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xmin = np.maximum(box[0], boxes[:, 0])
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ymin = np.maximum(box[1], boxes[:, 1])
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xmax = np.minimum(box[2], boxes[:, 2])
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ymax = np.minimum(box[3], boxes[:, 3])
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# Compute intersection area
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intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
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# Compute union area
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box_area = (box[2] - box[0]) * (box[3] - box[1])
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boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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union_area = box_area + boxes_area - intersection_area
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# Compute IoU
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iou = intersection_area / union_area
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return iou
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def xywh2xyxy(self, x):
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# Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
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y = np.copy(x)
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y[..., 0] = x[..., 0] - x[..., 2] / 2
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y[..., 1] = x[..., 1] - x[..., 3] / 2
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y[..., 2] = x[..., 0] + x[..., 2] / 2
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y[..., 3] = x[..., 1] + x[..., 3] / 2
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return y
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if __name__ == "__main__":
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yolov8_detector = YOLOv8('model/detect/best.onnx', conf_thres=0.7, iou_thres=0.7)
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# 摄像头索引号，通常为0表示第一个摄像头
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camera_index = 0
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# 打开摄像头
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cap = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
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# 设置分辨率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 3840) # 宽度
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2160) # 高度
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# 检查摄像头是否成功打开
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if not cap.isOpened():
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print("无法打开摄像头")
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exit()
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width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
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height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
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print("摄像头分辨率:", width, "x", height)
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# 目标图像尺寸
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target_width = 1024
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target_height = 768
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# 循环读取摄像头画面
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while True:
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ret, frame = cap.read()
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if not ret:
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print("无法读取摄像头画面")
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break
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# 1920*1080的图像，中心裁剪640*480的区域
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cropped_frame = frame[int(height / 2 - target_height / 2):int(height / 2 + target_height / 2),
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int(width / 2 - target_width / 2):int(width / 2 + target_width / 2)]
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# 调整图像尺寸
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resized_frame = cv2.resize(cropped_frame, (target_width, target_height))
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boxes, scores, class_ids = yolov8_detector(resized_frame)
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print(boxes, scores, class_ids)
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