158 lines
5.5 KiB
Python
158 lines
5.5 KiB
Python
import os
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import cv2
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import numpy as np
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import time
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from ais_bench.infer.interface import InferSession
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# ==================== 硬编码配置区域 ====================
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MODEL_PATH = "./yolo26_8t.om" # 你的 YOLO26 模型路径
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CONF_THRESHOLD = 0.25 # 置信度过滤阈值 (YOLO26 推荐 0.25)
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CAMERA_INDEX = 0 # 摄像头索引 (默认0，如果打不开可以尝试1或2)
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# ========================================================
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class YOLO26CameraInfer:
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def __init__(self, model_path, device_id=0):
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self.device_id = device_id
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self.model_path = model_path
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# 自动检查模型文件是否存在，防止硬编码路径写错导致程序崩溃
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"找不到硬编码的模型文件: {model_path}，请检查路径是否正确。")
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# 创建 ais_bench 推理会话
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self.session = InferSession(device_id, model_path)
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self.input_width = 640
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self.input_height = 640
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def letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114)):
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shape = img.shape[:2]
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
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dw = new_shape[1] - new_unpad[0]
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dh = new_shape[0] - new_unpad[1]
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left = dw // 2
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right = dw - left
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top = dh // 2
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bottom = dh - top
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if shape[::-1] != new_unpad:
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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return img, r, (left, top)
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def preprocess_frame(self, img_bgr):
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self.original_height, self.original_width = img_bgr.shape[:2]
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(
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img_rgb, (self.input_width, self.input_height)
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)
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img_input = (img_resized.astype(np.float32) / 255.0).astype(np.float32)
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img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, :, :, :])
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return img_input
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def infer(self, img_input):
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return self.session.infer([img_input])
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def postprocess(self, infer_result, conf_threshold):
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detections = []
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if isinstance(infer_result, (list, tuple)):
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output = infer_result[0]
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else:
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output = infer_result
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if output.ndim == 3:
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output = output[0]
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for det in output:
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confidence = det[4]
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if confidence < conf_threshold:
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continue
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x1_scale, y1_scale, x2_scale, y2_scale = det[0], det[1], det[2], det[3]
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cls_id = int(det[5])
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x1 = (x1_scale - self.pad_left) / self.ratio
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y1 = (y1_scale - self.pad_top) / self.ratio
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x2 = (x2_scale - self.pad_left) / self.ratio
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y2 = (y2_scale - self.pad_top) / self.ratio
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x1 = max(0, min(x1, self.original_width))
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y1 = max(0, min(y1, self.original_height))
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x2 = max(0, min(x2, self.original_width))
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y2 = max(0, min(y2, self.original_height))
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detections.append([x1, y1, x2, y2, confidence, cls_id])
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return detections
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def draw_boxes(image, detections):
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for det in detections:
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x1, y1, x2, y2, conf, cls_id = det
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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label = f"Class: {cls_id} {conf:.2f}"
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(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv2.rectangle(image, (x1, y1 - label_h - 5), (x1 + label_w, y1), (0, 255, 0), -1)
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cv2.putText(image, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
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return image
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if __name__ == "__main__":
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print("正在初始化 YOLOv26 离线推理引擎...")
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# 传入硬编码的模型路径
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yolo_infer = YOLO26CameraInfer(MODEL_PATH)
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# 打开硬编码指定的摄像头
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cap = cv2.VideoCapture(CAMERA_INDEX)
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if not cap.isOpened():
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print(f"错误：无法打开摄像头索引 {CAMERA_INDEX}，请检查物理连接。")
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exit()
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# 尝试设置合适的分辨率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
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print(f"初始化成功！当前模型：{MODEL_PATH} | 过滤阈值：{CONF_THRESHOLD}")
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print("开始实时检测，在画面窗口按下 'q' 键可退出...")
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fps_start_time = 0
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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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continue
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# 预处理与 NPU 推理
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input_tensor = yolo_infer.preprocess_frame(frame)
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raw_result = yolo_infer.infer(input_tensor)
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# 后处理（使用硬编码的置信度）
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detections = yolo_infer.postprocess(raw_result, conf_threshold=CONF_THRESHOLD)
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# 绘制结果
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frame = draw_boxes(frame, detections)
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# FPS 实时计算与显示
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fps_end_time = time.time()
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time_diff = fps_end_time - fps_start_time
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fps = 1 / time_diff if time_diff > 0 else 0
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fps_start_time = fps_end_time
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cv2.putText(frame, f"FPS: {fps:.1f}", (20, 40),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
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cv2.imshow("YOLOv26 Real-time Detection", frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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print("检测已结束。") |