基于yolov8實(shí)現(xiàn)目標(biāo)檢測(cè)功能匪傍，結(jié)合sort算法賦予id政溃，實(shí)現(xiàn)電力目標(biāo)鎖定功能。
部分代碼如下：
# aidlux相關(guān)
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, 清楚在跟目標(biāo)
? ? ? ? ? ? ? ? ? ? ? min_hits = 3,? # hit_streak>min_hits, 轉(zhuǎn)為確認(rèn)態(tài)
? ? ? ? ? ? ? ? ? ? ? iou_threshold = 0.3) # create instance of the SORT tracker
? ? # tflite模型
? ? model_path = '/home/yolov8/models/8086_best_float32.tflite'
? ? # 定義輸入輸出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()
? ? # 載入模型
? ? res = aidlite.ANNModel(model_path, in_shape, out_shape, 4, 0)
? ? print(res)
? ? ''' 讀取手機(jī)后置攝像頭 '''
? ? 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()
? ? ? ? # 預(yù)處理
? ? ? ? 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鎖定 '''
? ? ? ? ? ? preds_out = preds[:, :5]? # 數(shù)據(jù)切片，得到格式如[x1, y1, x2, y2, conf]的ndarray誉结。
? ? ? ? ? ? trackers = mot_tracker.update(preds_out)? # predict -> associate -> update
? ? ? ? ? ? ''' 繪制結(jié)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)
本視頻為基于AidLux的演示視頻，在安卓端實(shí)現(xiàn)yolov8實(shí)時(shí)檢測(cè)以及sort電力目標(biāo)鎖定功能券躁。
點(diǎn)擊下方演示視頻即可觀看惩坑。
