657 lines
22 KiB
Python
657 lines
22 KiB
Python
# from turtle import right
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import cv2
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import threading
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import sys
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import os
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import argparse
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import numpy as np
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import serial
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import time
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from surround_view import FisheyeCameraModel, BirdView
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import surround_view.param_settings as settings
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# 串口雷达配置
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RADAR_SERIAL_PORT = '/dev/ttyS0'
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RADAR_BAUDRATE = 115200 # 根据实际雷达配置调整
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RADAR_TIMEOUT = 0.1
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# 距离阈值配置（单位：mm）
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DISTANCE_THRESHOLD = 3000 # 距离过近阈值，可以根据需要调整
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# 雷达ID与方向映射
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RADAR_ID_MAP = {1: 'front', 2: 'left', 3: 'back', 4: 'right'}
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def parse_radar_data(raw_data):
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"""
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解析雷达原始数据
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数据格式: 0xA5 0x5A [雷达ID-距离高字节-距离低字节]... 0x5A 0xA5
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"""
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try:
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# 查找帧头位置
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frame_start = raw_data.find(b'\xA5\x5A')
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if frame_start == -1:
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return None
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# 查找帧尾位置
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frame_end = raw_data.find(b'\x5A\xA5', frame_start)
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if frame_end == -1:
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return None
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# 提取完整帧数据
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frame_data = raw_data[frame_start+2:frame_end]
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# 检查数据长度是否正确（每个雷达3字节 × 4雷达 = 12字节）
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if len(frame_data) != 12:
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return None
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# 解析每个雷达数据
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radar_distances = {}
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for i in range(4):
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radar_id = frame_data[i*3]
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if radar_id not in RADAR_ID_MAP:
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continue
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distance_high = frame_data[i*3+1]
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distance_low = frame_data[i*3+2]
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distance = (distance_high << 8) + distance_low
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direction = RADAR_ID_MAP[radar_id]
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radar_distances[direction] = distance
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return radar_distances
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except Exception as e:
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print(f"[ERROR] 雷达数据解析失败: {e}")
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return None
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sys.path.append(os.path.dirname(__file__)) # 确保能导入 py_utils
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from py_utils.coco_utils import COCO_test_helper
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from py_utils.rknn_executor import RKNN_model_container # 假设使用 RKNN
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from mjpeg_streamer import MJPEGServer
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from multiprocessing import Pool, Manager
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import cv2
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# 启用 OpenCL（Mali-G610）
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if cv2.ocl.haveOpenCL():
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cv2.ocl.setUseOpenCL(True)
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print("✅ OpenCL is ON — using Mali-G610 GPU for acceleration")
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else:
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print("⚠️ OpenCL not available")
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# ------YOLO 配置-----------
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# YOLO 配置
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YOLO_MODEL_PATH = './yolov5s-640-640.rknn'
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OBJ_THRESH = 0.6
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NMS_THRESH = 0.6
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IMG_SIZE = (640, 640) # (w, h)
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CLASSES = ("person",) # 只关心 person
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# 加载 anchors
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ANCHORS_FILE = './model/anchors_yolov5.txt'
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with open(ANCHORS_FILE, 'r') as f:
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values = [float(_v) for _v in f.readlines()]
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ANCHORS = np.array(values).reshape(3, -1, 2).tolist()
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import threading
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import numpy as np
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class SharedFrameBuffer:
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def __init__(self):
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self._frame = None
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self._lock = threading.Lock()
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self._has_frame = False
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def update(self, frame: np.ndarray):
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"""主线程调用：更新最新帧"""
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with self._lock:
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self._frame = frame.copy()
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self._has_frame = True
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def get_frame(self):
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"""YUYV 线程调用：获取最新帧"""
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with self._lock:
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if self._has_frame and self._frame is not None:
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return True, self._frame.copy()
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else:
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return False, None
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# ---------- YOLO 处理函数 ----------
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def filter_boxes(boxes, box_confidences, box_class_probs):
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box_confidences = box_confidences.reshape(-1)
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class_max_score = np.max(box_class_probs, axis=-1)
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classes = np.argmax(box_class_probs, axis=-1)
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_class_pos = np.where(class_max_score * box_confidences >= OBJ_THRESH)
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scores = (class_max_score * box_confidences)[_class_pos]
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boxes = boxes[_class_pos]
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classes = classes[_class_pos]
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return boxes, classes, scores
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def nms_boxes(boxes, scores):
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x = boxes[:, 0]
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y = boxes[:, 1]
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w = boxes[:, 2] - boxes[:, 0]
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h = boxes[:, 3] - boxes[:, 1]
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areas = w * h
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x[i], x[order[1:]])
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yy1 = np.maximum(y[i], y[order[1:]])
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xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
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yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
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w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
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h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
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inter = w1 * h1
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= NMS_THRESH)[0]
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order = order[inds + 1]
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keep = np.array(keep)
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return keep
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def box_process(position, anchors):
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grid_h, grid_w = position.shape[2:4]
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col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h))
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col = col.reshape(1, 1, grid_h, grid_w)
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row = row.reshape(1, 1, grid_h, grid_w)
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grid = np.concatenate((col, row), axis=1)
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stride = np.array([IMG_SIZE[1] // grid_h, IMG_SIZE[0] // grid_w]).reshape(1, 2, 1, 1)
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col = col.repeat(len(anchors), axis=0)
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row = row.repeat(len(anchors), axis=0)
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anchors = np.array(anchors)
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anchors = anchors.reshape(*anchors.shape, 1, 1)
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box_xy = position[:, :2, :, :] * 2 - 0.5
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box_wh = pow(position[:, 2:4, :, :] * 2, 2) * anchors
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box_xy += grid
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box_xy *= stride
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box = np.concatenate((box_xy, box_wh), axis=1)
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xyxy = np.copy(box)
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xyxy[:, 0, :, :] = box[:, 0, :, :] - box[:, 2, :, :] / 2
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xyxy[:, 1, :, :] = box[:, 1, :, :] - box[:, 3, :, :] / 2
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xyxy[:, 2, :, :] = box[:, 0, :, :] + box[:, 2, :, :] / 2
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xyxy[:, 3, :, :] = box[:, 1, :, :] + box[:, 3, :, :] / 2
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return xyxy
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def post_process(input_data, anchors):
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boxes, scores, classes_conf = [], [], []
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input_data = [_in.reshape([len(anchors[0]), -1] + list(_in.shape[-2:])) for _in in input_data]
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for i in range(len(input_data)):
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boxes.append(box_process(input_data[i][:, :4, :, :], anchors[i]))
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scores.append(input_data[i][:, 4:5, :, :])
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classes_conf.append(input_data[i][:, 5:, :, :])
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def sp_flatten(_in):
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ch = _in.shape[1]
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_in = _in.transpose(0, 2, 3, 1)
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return _in.reshape(-1, ch)
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boxes = [sp_flatten(_v) for _v in boxes]
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classes_conf = [sp_flatten(_v) for _v in classes_conf]
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scores = [sp_flatten(_v) for _v in scores]
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boxes = np.concatenate(boxes)
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classes_conf = np.concatenate(classes_conf)
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scores = np.concatenate(scores)
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boxes, classes, scores = filter_boxes(boxes, scores, classes_conf)
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nboxes, nclasses, nscores = [], [], []
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for c in set(classes):
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inds = np.where(classes == c)
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b = boxes[inds]
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c = classes[inds]
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s = scores[inds]
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keep = nms_boxes(b, s)
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if len(keep) != 0:
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nboxes.append(b[keep])
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nclasses.append(c[keep])
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nscores.append(s[keep])
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if not nclasses and not nscores:
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return None, None, None
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boxes = np.concatenate(nboxes)
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classes = np.concatenate(nclasses)
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scores = np.concatenate(nscores)
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return boxes, classes, scores
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def draw_detections(image, boxes, scores, classes):
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"""在图像上绘制检测框"""
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if boxes is None:
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return image
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for box, score, cl in zip(boxes, scores, classes):
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# 只绘制人的检测框
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if CLASSES[cl] != "person":
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continue
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top, left, right, bottom = [int(_b) for _b in box]
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# 绘制矩形框
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cv2.rectangle(image, (top, left), (right, bottom), (0, 255, 0), 20)
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# 绘制标签背景
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label = f'person: {score:.2f}'
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(label_width, label_height), baseline = cv2.getTextSize(
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label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 15
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)
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# 绘制标签矩形
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cv2.rectangle(
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image,
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(top, left - label_height - 5),
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(top + label_width, left),
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(0, 255, 0),
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-1
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)
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# # 绘制标签文字
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cv2.putText(
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image,
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label,
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(top, left - 5),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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(0, 0, 0),
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2
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)
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return image
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# ------------------------
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class MultiCameraBirdView:
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def __init__(self):
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self.running = True
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self.names = settings.camera_names # e.g., ['front', 'back', 'left', 'right']
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self.yamls = [os.path.join(os.getcwd(), "yaml", name + ".yaml") for name in self.names]
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self.camera_models = [
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FisheyeCameraModel(camera_file, camera_name)
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for camera_file, camera_name in zip(self.yamls, self.names)
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]
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# 摄像头设备映射
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self.which_cameras = {"front": 0, "back": 2, "left": 1, "right": 3}
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self.caps = []
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print("[INFO] 初始化摄像头...")
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for name in self.names:
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cap = cv2.VideoCapture(self.which_cameras[name], cv2.CAP_V4L2)
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cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"YUYV"))
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) # 最小缓冲，降低延迟
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# cap.set(cv2.CAP_PROP_FPS, 30) # 设置帧率为 30 FPS
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if not cap.isOpened():
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print(f"[ERROR] 无法打开 {name} 摄像头 (设备 {self.which_cameras[name]})", file=sys.stderr)
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self.running = False
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return
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self.caps.append(cap)
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self.birdview = BirdView()
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self._initialize_weights()
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# 新增：预警状态
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self.alerts = {
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"front": False,
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"back": False,
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"left": False,
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"right": False
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}
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# 雷达数据
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self.radar_distances = {
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"front": 0,
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"back": 0,
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"left": 0,
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"right": 0
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}
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self.radar_serial = None
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self.radar_thread = None
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self.radar_running = False
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# === 新增：YOLO 人体检测模型 ===
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try:
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self.yolo_model = RKNN_model_container(YOLO_MODEL_PATH, target='rk3588')
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print("[INFO] YOLO 模型加载成功")
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# 初始化COCO助手用于图像预处理
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self.co_helper = COCO_test_helper(enable_letter_box=True)
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except Exception as e:
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print(f"[ERROR] YOLO 模型加载失败: {e}")
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self.yolo_model = None
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self.shared_buffer = SharedFrameBuffer() # 👈 新增
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# 初始化雷达串口
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self._init_radar_serial()
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def overlay_alert(self, birdview_img):
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"""在鸟瞰图上叠加半透明红色预警区域"""
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h, w = birdview_img.shape[:2]
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overlay = birdview_img.copy()
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alpha = 0.2 # 透明度
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red = (0, 0, 200)
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margin_f_b = int(min(h, w) * 0.07) # 预警区域宽度（约7%）前后
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margin_l_r = int(min(h, w) * 0.15) # 预警区域宽度（约15%）左右
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if self.alerts["front"]:
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cv2.rectangle(overlay, (0, 0), (w, margin_f_b), red, -1)
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if self.alerts["back"]:
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cv2.rectangle(overlay, (0, h - margin_f_b), (w, h), red, -1)
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if self.alerts["left"]:
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cv2.rectangle(overlay, (0, 0), (margin_l_r, h), red, -1)
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if self.alerts["right"]:
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cv2.rectangle(overlay, (w - margin_l_r, 0), (w, h), red, -1)
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# 混合原图与覆盖层
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blended = cv2.addWeighted(birdview_img, 1 - alpha, overlay, alpha, 0)
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return blended
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def detect_persons(self, image):
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"""使用YOLO模型检测图像中的人体"""
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if self.yolo_model is None:
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return image, [], []
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try:
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# 保存原始图像尺寸
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orig_h, orig_w = image.shape[:2]
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# 预处理图像
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pad_color = (0, 0, 0)
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img_preprocessed = self.co_helper.letter_box(
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im=image.copy(),
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new_shape=(IMG_SIZE[1], IMG_SIZE[0]),
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pad_color=pad_color
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)
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# img_preprocessed = cv2.cvtColor(img_preprocessed, cv2.COLOR_BGR2RGB)
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# 推理
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outputs = self.yolo_model.run([np.expand_dims(img_preprocessed, 0)])
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# 后处理
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boxes, classes, scores = post_process(outputs, ANCHORS)
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if boxes is not None:
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# 将检测框转换回原始图像坐标
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real_boxes = self.co_helper.get_real_box(boxes)
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# 筛选出人的检测框
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person_boxes = []
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person_scores = []
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for i in range(len(real_boxes)):
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if classes[i] < len(CLASSES) and CLASSES[classes[i]] == "person":
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# 确保坐标在图像范围内
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box = real_boxes[i].copy()
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box[0] = max(0, min(box[0], orig_w))
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box[1] = max(0, min(box[1], orig_h))
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box[2] = max(0, min(box[2], orig_w))
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box[3] = max(0, min(box[3], orig_h))
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person_boxes.append(box)
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person_scores.append(scores[i])
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# 在图像上绘制检测框
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if person_boxes:
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image = draw_detections(image, np.array(person_boxes),
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np.array(person_scores),
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np.zeros(len(person_boxes), dtype=int))
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# 打印检测信息
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# print(f"[YOLO] 检测到 {len(person_boxes)} 个人体")
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# for box, score in zip(person_boxes, person_scores):
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# print(f" 位置: ({int(box[0])}, {int(box[1])}, {int(box[2])}, {int(box[3])}), 置信度: {score:.2f}")
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return image, person_boxes, person_scores
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else:
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return image, [], []
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except Exception as e:
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print(f"[ERROR] YOLO检测失败: {e}")
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return image, [], []
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def _init_radar_serial(self):
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"""
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初始化雷达串口并启动数据读取线程
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"""
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try:
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self.radar_serial = serial.Serial(
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port=RADAR_SERIAL_PORT,
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baudrate=RADAR_BAUDRATE,
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timeout=RADAR_TIMEOUT
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)
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print(f"✅ 已打开雷达串口 {RADAR_SERIAL_PORT}")
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# 启动雷达数据读取线程
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self.radar_running = True
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self.radar_thread = threading.Thread(target=self._read_radar_data, daemon=True)
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self.radar_thread.start()
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print("[INFO] 雷达数据读取线程已启动")
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except serial.SerialException as e:
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print(f"❌ 无法打开雷达串口 {RADAR_SERIAL_PORT}: {e}")
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self.radar_serial = None
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except Exception as e:
|
||
print(f"[ERROR] 雷达初始化失败: {e}")
|
||
self.radar_serial = None
|
||
|
||
def _read_radar_data(self):
|
||
"""
|
||
雷达数据读取线程函数
|
||
"""
|
||
buffer = b''
|
||
while self.radar_running and self.radar_serial and self.radar_serial.is_open:
|
||
try:
|
||
# 读取可用数据
|
||
if self.radar_serial.in_waiting > 0:
|
||
data = self.radar_serial.read(self.radar_serial.in_waiting)
|
||
buffer += data
|
||
|
||
# 尝试解析数据
|
||
parsed_data = parse_radar_data(buffer)
|
||
|
||
if parsed_data:
|
||
print(f"[RADAR] 前:{parsed_data.get('front', 0):4d}cm 后:{parsed_data.get('back', 0):4d}cm 左:{parsed_data.get('left', 0):4d}cm 右:{parsed_data.get('right', 0):4d}cm")
|
||
|
||
# 更新雷达距离数据
|
||
self.radar_distances.update(parsed_data)
|
||
# 清空已解析的缓冲区
|
||
buffer = buffer[buffer.find(b'\x5A\xA5')+2:]
|
||
|
||
time.sleep(0.01) # 降低CPU占用
|
||
|
||
except serial.SerialException as e:
|
||
print(f"❌ 雷达串口异常: {e}")
|
||
break
|
||
except Exception as e:
|
||
print(f"[ERROR] 雷达数据读取失败: {e}")
|
||
time.sleep(0.1)
|
||
|
||
def _initialize_weights(self):
|
||
try:
|
||
images = [os.path.join(os.getcwd(), "images", name + ".png") for name in self.names]
|
||
static_frames = []
|
||
for img_path, cam_model in zip(images, self.camera_models):
|
||
img = cv2.imread(img_path)
|
||
if img is None:
|
||
print(f"[WARN] 静态图缺失: {img_path}，用黑图代替")
|
||
img = np.zeros((1080, 1920, 3), dtype=np.uint8)
|
||
img = cam_model.undistort(img)
|
||
img = cam_model.project(img)
|
||
img = cam_model.flip(img)
|
||
static_frames.append(img)
|
||
if len(static_frames) == 4:
|
||
self.birdview.get_weights_and_masks(static_frames)
|
||
print("[INFO] 权重矩阵初始化成功")
|
||
except Exception as e:
|
||
print(f"[ERROR] 权重初始化失败: {e}", file=sys.stderr)
|
||
|
||
def process_frame_once(self, frame, model):
|
||
"""只处理一次：去畸变 + 投影 + 翻转"""
|
||
frame = model.undistort(frame)
|
||
frame = model.project(frame)
|
||
frame = model.flip(frame)
|
||
return frame
|
||
|
||
def process_frame_undistort(self, frame, model):
|
||
"""只处理一次：去畸变"""
|
||
frame = model.undistort(frame)
|
||
return frame
|
||
|
||
def run(self):
|
||
current_view = "front"
|
||
frame_count = 0
|
||
detection_interval = 3 # 每5帧进行一次检测，避免性能问题
|
||
|
||
# 雷达触发视图切换的方向优先级（距离过近时优先显示哪个方向）
|
||
radar_priority = ['front', 'left', 'right', 'back']
|
||
|
||
while self.running:
|
||
raw_frames = {}
|
||
processed_frames = []
|
||
|
||
|
||
for i, (cap, model, name) in enumerate(zip(self.caps, self.camera_models, self.names)):
|
||
ret, frame = cap.read()
|
||
|
||
raw_frames[name] = frame
|
||
|
||
|
||
p_frame = self.process_frame_once(frame, model)
|
||
|
||
processed_frames.append(p_frame)
|
||
|
||
|
||
# 更新鸟瞰图
|
||
self.birdview.update_frames(processed_frames)
|
||
self.birdview.stitch_all_parts()
|
||
self.birdview.make_white_balance()
|
||
self.birdview.copy_car_image()
|
||
|
||
# 检查雷达距离，自动切换视图
|
||
auto_switch_view = None
|
||
for direction in radar_priority:
|
||
distance = self.radar_distances.get(direction, 0)
|
||
if distance > 0 and distance < DISTANCE_THRESHOLD:
|
||
auto_switch_view = direction
|
||
break
|
||
|
||
if auto_switch_view:
|
||
current_view = auto_switch_view
|
||
print(f"[INFO] 雷达检测到{auto_switch_view}方向距离过近 ({self.radar_distances[auto_switch_view]}cm)，自动切换视图")
|
||
|
||
# 获取单路图像（仅去畸变）
|
||
single_img = self.process_frame_undistort(
|
||
raw_frames[current_view],
|
||
self.camera_models[self.names.index(current_view)]
|
||
)
|
||
|
||
# 在单路图像上进行人体检测
|
||
|
||
frame_count += 1
|
||
if frame_count % detection_interval == 0 and self.yolo_model is not None:
|
||
single_img, person_boxes, person_scores = self.detect_persons(single_img)
|
||
|
||
# 根据检测结果自动触发预警
|
||
if person_boxes:
|
||
# 可以根据人体的位置和数量来触发预警
|
||
|
||
self.alerts[current_view] = True
|
||
# 重置其他视图的预警
|
||
for view in self.alerts:
|
||
if view != current_view:
|
||
self.alerts[view] = False
|
||
else:
|
||
# 没有检测到人，清除所有预警
|
||
for view in self.alerts:
|
||
self.alerts[view] = False
|
||
|
||
|
||
# 叠加预警区域
|
||
birdview_with_alert = self.overlay_alert(self.birdview.image)
|
||
|
||
# 拼接显示：左侧鸟瞰图（1/3），右侧单路（2/3）
|
||
h_display, w_display = 720, 1280
|
||
w_bird = w_display // 3
|
||
w_single = w_display - w_bird
|
||
|
||
bird_resized = cv2.resize(birdview_with_alert, (w_bird, h_display))
|
||
single_resized = cv2.resize(single_img, (w_single, h_display))
|
||
display = np.hstack((bird_resized, single_resized))
|
||
self.shared_buffer.update(display)
|
||
# 全屏显示
|
||
cv2.namedWindow('Video', cv2.WND_PROP_FULLSCREEN)
|
||
cv2.setWindowProperty('Video', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
|
||
cv2.imshow("Video", display)
|
||
|
||
key = cv2.waitKey(1) & 0xFF
|
||
|
||
|
||
# 清理雷达资源
|
||
self.radar_running = False
|
||
if self.radar_thread and self.radar_thread.is_alive():
|
||
self.radar_thread.join(timeout=1.0)
|
||
if self.radar_serial and self.radar_serial.is_open:
|
||
self.radar_serial.close()
|
||
print("🔌 雷达串口已关闭")
|
||
|
||
for cap in self.caps:
|
||
cap.release()
|
||
cv2.destroyAllWindows()
|
||
|
||
|
||
def main():
|
||
print("🚀 启动实时四路环视系统...")
|
||
print("操作说明:")
|
||
print(" 1-4: 切换单路视图（前/后/左/右）")
|
||
print(" 5-8: 触发前/后/左/右 接近预警")
|
||
print(" 0 : 清除所有预警")
|
||
print(" d : 手动触发人体检测")
|
||
print(" q : 退出程序")
|
||
|
||
multi_cam = MultiCameraBirdView()
|
||
|
||
# ===== 启动视频流 =====
|
||
try:
|
||
from mjpeg_streamer import MJPEGServer
|
||
mjpeg_server = MJPEGServer(multi_cam.shared_buffer, host="0.0.0.0", port=8080)
|
||
mjpeg_server.start()
|
||
except Exception as e:
|
||
print(f"[WARN] YUYV 流启动失败: {e}")
|
||
|
||
if multi_cam.running:
|
||
multi_cam.run()
|
||
else:
|
||
print("[ERROR] 摄像头初始化失败")
|
||
|
||
if __name__ == "__main__":
|
||
main() |