利用AidLux实现电离目标检测与实时锁定演示

# aidlux相关
from cvs import *
import aidlite_gpu
from utils import detect_postprocess, preprocess_img, draw_detect_res, scale_boxes
import time
import cv2
import os
import numpy as np
import glob
import argparse
from filterpy.kalman import KalmanFilter
 
np.random.seed(0)
def linear_assignment(cost_matrix):
    try:
        import lap
        _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
        return np.array([[y[i],i] for i in x if i >= 0]) #
    except ImportError:
        from scipy.optimize import linear_sum_assignment
        x, y = linear_sum_assignment(cost_matrix)
        return np.array(list(zip(x, y)))
 
 
def iou_batch(bb_test, bb_gt):
    """
    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
    """
    bb_gt = np.expand_dims(bb_gt, 0)
    bb_test = np.expand_dims(bb_test, 1)
    
    xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
    yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
    xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
    yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
    w = np.maximum(0., xx2 - xx1)
    h = np.maximum(0., yy2 - yy1)
    wh = w * h
    o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])                                      
        + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)                                              
    return(o)
 
 
def convert_bbox_to_z(bbox):
    """
    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
        [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
        the aspect ratio
    """
    w = bbox[2] - bbox[0]
    h = bbox[3] - bbox[1]
    x = bbox[0] + w/2.
    y = bbox[1] + h/2.
    s = w * h    #scale is just area
    r = w / float(h)
    return np.array([x, y, s, r]).reshape((4, 1))
 
 
def convert_x_to_bbox(x, score=None):
    """
    Takes a bounding box in the centre form [x, y, s, r] and returns it in the form
        [x1,y1,x2,y2] where x1, y1 is the top left and x2, y2 is the bottom right
    """
    w = np.sqrt(x[2] * x[3])
    h = x[2] / w
    if(score==None):
        return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2.]).reshape((1,4))
    else:
        return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2., score]).reshape((1,5))
 
 
class KalmanBoxTracker(object):
    """
    This class represents the internal state of individual tracked objects observed as bbox.
    """
    count = 0
    def __init__(self,bbox):
        """
        Initialises a tracker using initial bounding box.
        """
        #define constant velocity model
        self.kf = KalmanFilter(dim_x=7, dim_z=4) 
        self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0],  [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])
        self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])
 
        self.kf.R[2:,2:] *= 10.
        self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
        self.kf.P *= 10.
        self.kf.Q[-1,-1] *= 0.01
        self.kf.Q[4:,4:] *= 0.01
 
        self.kf.x[:4] = convert_bbox_to_z(bbox)
        self.time_since_update = 0
        self.id = KalmanBoxTracker.count
        KalmanBoxTracker.count += 1
        self.history = []
        self.hits = 0
        self.hit_streak = 0
        self.age = 0
 
    def update(self,bbox):
        """
        Updates the state vector with observed bbox.
        """
        self.time_since_update = 0
        self.history = []
        self.hits += 1
        self.hit_streak += 1
        self.kf.update(convert_bbox_to_z(bbox))
 
    def predict(self):
        """
        Advances the state vector and returns the predicted bounding box estimate.
        """
        if((self.kf.x[6]+self.kf.x[2])<=0):
            self.kf.x[6] *= 0.0
        self.kf.predict()
        self.age += 1
        if(self.time_since_update>0):
            self.hit_streak = 0
        self.time_since_update += 1
        self.history.append(convert_x_to_bbox(self.kf.x))
        return self.history[-1]
 
    def get_state(self):
        """
        Returns the current bounding box estimate.
        """
        return convert_x_to_bbox(self.kf.x)
 
 
def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
    """
    Assigns detections to tracked object (both represented as bounding boxes)
    Returns 3 lists of matches, unmatched_detections and unmatched_trackers
    """
    if(len(trackers)==0):
        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
 
    iou_matrix = iou_batch(detections, trackers)
 
    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-iou_matrix)
    else:
        matched_indices = np.empty(shape=(0,2))
 
    unmatched_detections = []
    for d, det in enumerate(detections):
        if(d not in matched_indices[:,0]):
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if(t not in matched_indices[:,1]):
            unmatched_trackers.append(t)
 
    #filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if(iou_matrix[m[0], m[1]]<iou_threshold):
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1,2))
    if(len(matches)==0):
        matches = np.empty((0,2),dtype=int)
    else:
        matches = np.concatenate(matches,axis=0)
 
    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
 
 
class Sort(object):
    def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
        """
        Sets key parameters for SORT
        """
        self.max_age = max_age  # time_since_update > max_age, track被清除
        self.min_hits = min_hits
        self.iou_threshold = iou_threshold
        self.trackers = []
        self.frame_count = 0
 
    def update(self, dets=np.empty((0, 5))):
        """
        Params:
        dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
        Returns the a similar array, where the last column is the object ID.
        NOTE: The number of objects returned may differ from the number of detections provided.
        """
        self.frame_count += 1
        # get predicted locations from existing trackers.
        trks = np.zeros((len(self.trackers), 5))
        to_del = []
        ret = []
        for t, trk in enumerate(trks):
            pos = self.trackers[t].predict()[0]
            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
            if np.any(np.isnan(pos)):
                to_del.append(t)
        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
        for t in reversed(to_del):
            self.trackers.pop(t)
        matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
 
        # update matched trackers with assigned detections
        for m in matched:
            self.trackers[m[1]].update(dets[m[0], :])
 
        # create and initialize new trackers for unmatched detections
        for i in unmatched_dets:
            trk = KalmanBoxTracker(dets[i,:])
            self.trackers.append(trk)
        i = len(self.trackers)
        for trk in reversed(self.trackers):
            d = trk.get_state()[0]
            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
                ret.append(np.concatenate((d, [trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
            i -= 1
            # remove dead tracklet
            if(trk.time_since_update > self.max_age):
                self.trackers.pop(i)
        if(len(ret)>0):
            return np.concatenate(ret)
        return np.empty((0,5))
 
 
if __name__ == '__main__':
    mot_tracker = Sort(max_age = 1,  # time_since_update>max_age, 清楚在跟目标
                       min_hits = 3,  # hit_streak>min_hits, 转为确认态
                       iou_threshold = 0.3) # create instance of the SORT tracker
    # tflite模型
    model_path = '/home/yolov8/models/8086_best_float32.tflite'
    # 定义输入输出shape
    in_shape = [1 * 640 * 640 * 3 * 4]  # HWC, float32
    out_shape = [1 * 8400 * 52 * 4]  # 8400: total cells, 52 = 48(num_classes) + 4(xywh), float32
 
    # AidLite初始化
    aidlite = aidlite_gpu.aidlite()
    # 载入模型
    res = aidlite.ANNModel(model_path, in_shape, out_shape, 4, 0)
    print(res)
 
    ''' 读取手机后置摄像头 '''
    cap = cvs.VideoCapture(0)
    frame_id = 0
    while True:
        frame = cap.read()
        if frame is None:
            continue
        frame_id += 1
        if frame_id % 3 != 0:
            continue
        time0 = time.time()
        # 预处理
        img = preprocess_img(frame, target_shape=(640, 640), div_num=255, means=None, stds=None)
 
        aidlite.setInput_Float32(img, 640, 640)
        # 推理
        aidlite.invoke()
        preds = aidlite.getOutput_Float32(0)
        preds = preds.reshape(1, 52, 8400)
        preds = detect_postprocess(preds, frame.shape, [640, 640, 3], conf_thres=0.25, iou_thres=0.45)
        print('1 batch takes {} s'.format(time.time() - time0))
        if len(preds) != 0:
            preds[:, :4] = scale_boxes([640, 640], preds[:, :4], frame.shape)
            ''' SORT锁定 '''
            preds_out = preds[:, :5]  # 数据切片，得到格式如[x1, y1, x2, y2, conf]的ndarray。
            trackers = mot_tracker.update(preds_out)  # predict -> associate -> update
            ''' 绘制结果 '''
            for d in trackers:
                cv2.putText(frame, str(int(d[4])), (int(d[0]), int(d[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 1)
                cv2.rectangle(frame, (int(d[0]), int(d[1])), (int(d[2]), int(d[3])), (0, 0, 255),thickness = 2)  
 
        cvs.imshow(frame)