yolov3/tiny-yolov3训练和测试 python2/3
2024-05-07 05:54:30
yolov3
数据集准备
使用labelimg工具标记数据（voc格式）
把标记好的xml文件转成txt,转化脚本如下(python2.7)
1, 获取所有图像名
```
import os
dirlist = os.listdir("/home/room/mxj_workspace/data/voc_clock/train_img/")
fp = open("ImageID.txt","w")
for name in dirlist: fp.write(name)fp.write("\n")
fp.close()
```
2，提取xml中的坐标和label转化为txt，注意修改label名字和路径，新建好对应的ImageID文件夹，最后把生成的txt拷贝到train_img.
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join#sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
#classes = ["black_watch","box_watch","white_watch","light_watch","square_watch","sign","IO_State"]
classes = ["s_box"]def convert(size, box):dw = 1./(size[0])dh = 1./(size[1])x = (box[0] + box[1])/2.0 - 1y = (box[2] + box[3])/2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x*dww = w*dwy = y*dhh = h*dhreturn (x,y,w,h)def convert_annotation (image_id):in_file = open('/home/mahxn0/darknet/box/train_xml/%s.xml'%(image_id))out_file = open('/home/mahxn0/darknet/box/ImageID/%s.txt'%(image_id), 'w')tree=ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):cls = obj.find('name').textif cls not in classes :continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))bb = convert((w,h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')#wd = getcwd()#for year, image_set in sets:
if not os.path.exists('/home/mahxn0/darknet/box/img_file'):os.makedirs('/home/mahxn0/darknet/box/img_file/')
image_ids = open('/home/mahxn0/darknet/box/ImageID.txt').read().strip().split()
listtr_file = open('/home/mahxn0/darknet/box/train.list', 'w')
listte_file = open('/home/mahxn0/darknet/box/val.list', 'w')
i = 0
for image_id in image_ids:i = i+1if(i%10 == 0):listte_file.write('/home/mahxn0/darknet/box/train_img/%s.jpg\n'%( image_id.split('.')[0]))else:listtr_file.write('/home/mahxn0/darknet/box/train_img/%s.jpg\n'%( image_id.split('.')[0]))convert_annotation(image_id.split('.')[0])
listte_file.close()
listtr_file.close()#os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
#os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")- 修改cfg文件 关键：3*(classes+5)
- 找到cfg文件的三处classes位置，classes改成你的检测类别数，上一层filter修改为：3*(classes+5)
- 修改cfg/coco.data的类别数为你自己检测的类别数目,train.list和val.list改为你上面用label.py生成的，coco.names里面修改为你自己的label名字，backup是模型保存的位置
开始训练：
./darknet detector train cfg/coco.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0,1,2,3
Region 23 Avg IOU: 0.331109, Class: 0.552714, Obj: 0.017880, No Obj: 0.021078, .5R: 0.129032, .75R: 0.000000, count: 62219: 5.798628, 26.150927 avg loss, 0.000007 rate, 1.180564 seconds, 42048 images
Loaded: 12.885740 seconds
Region 16 Avg IOU: 0.210043, Class: 0.500716, Obj: 0.037469, No Obj: 0.031145, .5R: 0.000000, .75R: 0.000000, count: 3
Region 16 Avg IOU: 0.302149, Class: 0.318319, Obj: 0.086097, No Obj: 0.030979, .5R: 0.000000, .75R: 0.000000, count: 1
Region 16 Avg IOU: 0.203812, Class: 0.335673, Obj: 0.063994, No Obj: 0.031331, .5R: 0.000000, .75R: 0.000000, count: 1
Region 23 Avg IOU: 0.312156, Class: 0.556277, Obj: 0.012325, No Obj: 0.019171, .5R: 0.120000, .75R: 0.000000, count: 50
Region 23 Avg IOU: 0.373455, Class: 0.508114, Obj: 0.015595, No Obj: 0.019038, .5R: 0.203390, .75R: 0.000000, count: 59
Region 23 Avg IOU: 0.344760, Class: 0.490172, Obj: 0.013907, No Obj: 0.019223, .5R: 0.187500, .75R: 0.000000, count: 48
Region 16 Avg IOU: 0.454259, Class: 0.426787, Obj: 0.027839, No Obj: 0.031548, .5R: 0.000000, .75R: 0.000000, count: 1
Region 16 Avg IOU: 0.366378, Class: 0.445379, Obj: 0.043471, No Obj: 0.030944, .5R: 0.000000, .75R: 0.000000, count: 2
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.030927, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: 0.362018, Class: 0.513913, Obj: 0.014860, No Obj: 0.019196, .5R: 0.224138, .75R: 0.000000, count: 58
Region 23 Avg IOU: 0.278272, Class: 0.531918, Obj: 0.013913, No Obj: 0.019277, .5R: 0.065217, .75R: 0.000000, count: 46
Region 23 Avg IOU: 0.322512, Class: 0.549836, Obj: 0.016681, No Obj: 0.019718, .5R: 0.102564, .75R: 0.000000, count: 39tiny-yolov3
获取预训练模型
- 从官方地址下载yolov3-tiny.weights
- 下载 理论上并没有说提取多少层的特征合适，这里我们提取前15层当作与训练模型
./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15---layer filters size input output0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF15 conv 255 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF16 yolo17 route 1318 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 12820 route 19 821 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF23 yolo
Total BFLOPS 5.571
Loading weights from backup/yolov3-tiny.weights...seen 64
Done!
Saving weights to yolov3-tiny.conv.15
训练：
./darknet detector train cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.conv.15 15
问题汇总：
1> 多个模型报错out of memory，把cfg中的batch,sub设置为12>海康相机的解码："rtspsrc location=rtsp://admin:123qweasd@192.168.0.222:554/h264/ch1/main/av_stream latency=200 ! rtph264depay ! h264parse ! omxh264dec ! videoconvert ! appsink sync=false"3>刚开始出现nan正常现象，如果全部是nan,是xml生成的txt错误或者label名字错误，查看coco.names，生成的txt确定文件都正确4>2000张样本迭代2000-5000次最佳，10000样本迭代20000次左右(主要看学习率的下降和数据复杂度)
测试:
-c index 按照索引打开摄像头
-out_filename *.avi 保存结果到视频文件
-thresh 设置检测置信度
-ext_output < /media/mahxn0/DATA/tool/state3.list > result1.txt 批量测试图片准确度并且显示
python v2调用(已封装成python库)
- 注意：get_network_boxs函数最后的c_int是调节框的准确度的
- 模型的batchsize和subvision测试的时候改成1 ，否则检测结果会不准确，目前正在查找原因
- free image必须打开释放内存
#-*- coding=utf-8 -*-
from ctypes import *
import math
import random
import time
import cv2
import numpy as np
import re
import os
import sys
def sample(probs):s = sum(probs)probs = [a/s for a in probs]r = random.uniform(0, 1)for i in range(len(probs)):r = r - probs[i]if r <= 0:return ireturn len(probs)-1# def c_array(ctype, values):
# arr = (ctype*len(values))()
# arr[:] = values
# return arrdef c_array(ctype, values):return (ctype * len(values))(*values)class BOX(Structure):_fields_ = [("x", c_float),("y", c_float),("w", c_float),("h", c_float)]class DETECTION(Structure):_fields_ = [("bbox", BOX),("classes", c_int),("prob", POINTER(c_float)),("mask", POINTER(c_float)),("objectness", c_float),("sort_class", c_int)]class IMAGE(Structure):_fields_ = [("w", c_int),("h", c_int),("c", c_int),("data", POINTER(c_float))]class METADATA(Structure):_fields_ = [("classes", c_int),("names", POINTER(c_char_p))]#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
lib = CDLL("/home/mahxn0/darknet/darknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_intpredict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGEget_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int),c_int]
get_network_boxes.restype = POINTER(DETECTION)make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_pdo_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]free_image = lib.free_image
free_image.argtypes = [IMAGE]letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGEload_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATAload_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGErgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)ndarray_image = lib.ndarray_to_image
ndarray_image.argtypes = [POINTER(c_ubyte), POINTER(c_long), POINTER(c_long)]
ndarray_image.restype = IMAGE#net = load_net("/media/mahxn0/DATA/workspace/src/detectAndRecog/src/yolo_surface/data/robot/robot.cfg", "/media/mahxn0/DATA/workspace/src/detectAndRecog/src/yolo_surface/data/robot/robot_final.weights", 0)
#meta = load_meta("/media/mahxn0/DATA/workspace/src/detectAndRecog/src/yolo_surface/data/robot/robot.data")net = load_net("/home/mahxn0/darknet/yolo_box/box.cfg", "/home/mahxn0/darknet/yolo_box/backup/box_final.weights", 0)
meta = load_meta("/home/mahxn0/darknet/yolo_box/box.data")#net = load_net("/home/mahxn0/ROS_workspace/darknet/cfg/yolov3-tiny.cfg", "/home/mahxn0/ROS_workspace/darknet/yolov3-tiny.weights", 0)
#meta = load_meta("/home/mahxn0/ROS_workspace/darknet/cfg/coco.data")
#video =cv2.VideoCapture(0)class yolo_helmet(object):def __init__(self):passdef detect_pic(self, image, thresh=0.3, hier_thresh=.5, nms=.45):im = self.nparray_to_image(image)num = c_int(0)pnum = pointer(num)predict_image(net, im)dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)num = pnum[0]if (nms): do_nms_obj(dets, num, meta.classes, nms)res = []for j in range(num):for i in range(meta.classes):if dets[j].prob[i] > 0:b = dets[j].bboxleft=(b.x-b.w/2)right=(b.x+b.w/2)top=(b.y-b.h/2)bot=(b.y+b.h/2)if left < 0:left = 0if right > im.w-1:right = im.w-1if top < 0:top = 0if bot > im.h-1:bot = im.h-1res.append((meta.names[i], dets[j].prob[i],left,top,right,bot))res = sorted(res, key=lambda x: -x[1])free_image(im) #not sure if this will cause a memory leak.free_detections(dets, num)return resdef detect(self, image, thresh=.3, hier_thresh=.5, nms=.45):t0=time.time()#rgbgr_image(im)im = self.nparray_to_image(image)t1=time.time()num = c_int(0)pnum = pointer(num)predict_image(net, im)dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum,0)num = pnum[0]if (nms): do_nms_obj(dets, num, meta.classes, nms)res = []for j in range(num):for i in range(meta.classes):if dets[j].prob[i] > 0:b = dets[j].bboxleft=b.x-b.w/2right=b.x+b.w/2top=b.y-b.h/2bot=b.y+b.h/2if left < 0:left = 0if right > im.w-1:right = im.w-1if top < 0:top = 0if bot > im.h-1:bot = im.h-1res.append((meta.names[i], dets[j].prob[i],left,top,right,bot))res = sorted(res, key=lambda x: -x[1])free_image(im) #not sure if this will cause a memory leak.free_detections(dets, num)t2=time.time()print("detect take %d s",t2-t0);print("array_to_image take %d s",t1-t0)return resdef array_to_image(self,arr):arr = arr.transpose(2,0,1)c = arr.shape[0]h = arr.shape[1]w = arr.shape[2]arr = (arr/255.0).flatten()data = c_array(c_float, arr)im = IMAGE(w,h,c,data)return imdef nparray_to_image(self,img):data = img.ctypes.data_as(POINTER(c_ubyte))image = ndarray_image(data, img.ctypes.shape, img.ctypes.strides)return imagedef getXY(self,i):return int(i)
if __name__ == "__main__":picDir = '/home/mahxn0/Downloads/20181226/'print("path:",picDir)filenames = os.listdir(picDir)i=0r=yolo_helmet()font=cv2.FONT_HERSHEY_SCRIPT_COMPLEXfor name in filenames:filename = os.path.join(picDir,name)print(filename)image=cv2.imread(filename)out=r.detect(image)print(out)for res in out:x1=r.getXY(res[2])y1=r.getXY(res[3])x2=r.getXY(res[4])y2=r.getXY(res[5])if x1>300 and y1 > 300:frame_rect=image[(y1-80):(y2+80),(x1-80):(x2+80)]cv2.imwrite('/home/mahxn0/darknet/image/box_rect3/'+str(i)+'.jpg',frame_rect)i+=1# f_img=None# cap=cv2.VideoCapture()# cap.open("/media/mahxn0/Mahxn0/M_DataSets/jinan_data/Video/2018-07-07/192.168.0.222_01_20180707150311306.mp4")
# # cap.set(3,1280)
# # cap.set(4,720)
# # cap.set(5,60)
# # cap.set(10,-4)
# # cap.set(11,40)
# # cap.set(12,20)
# # cap.set(15,-2)# #cap.open('rtsp://admin:123qweasd@192.168.0.222:554/h264/ch1/main/av_stream')# cv2.namedWindow('YOLOV3')# r = yolo_helmet()# result = None# fileindex=0# font=cv2.FONT_HERSHEY_SCRIPT_COMPLEX# #font = cv2.CAP_PVAPI_PIXELFORMAT_MONO8 # 使用默认字体# while(cap.isOpened()):# rect,frame=cap.read()# frame_res=frame# if True:# out = r.detect(frame)# # cv2.imshow("YOLOV3",frame)# print(out)# for res in out:# x1=r.getXY(res[2])# y1=r.getXY(res[3])# x2=r.getXY(res[4])# y2=r.getXY(res[5])# frame_res=cv2.rectangle(frame, (x1,y1), (x2,y2), (87,255,123),4)# cv2.putText(frame_res, res[0] + ' ' + str(res[1]), (x1,y1), font,1,(0,0,255),2)# frame_rect=frame[x2:y2,x1:y1]# cv2.imwrite("test.jpg",frame_rect)# if frame_res is None:# print("frame_res is empty")# else:# cv2.imshow("YOLOV3",frame)# cv2.waitKey(1)
pythonv3调用:
from ctypes import *
import math
import random
import cv2
import time
import numpy as npdef sample(probs):s = sum(probs)probs = [a/s for a in probs]r = random.uniform(0, 1)for i in range(len(probs)):r = r - probs[i]if r <= 0:return ireturn len(probs)-1def c_array(ctype, values):arr = (ctype*len(values))()arr[:] = valuesreturn arrclass BOX(Structure):_fields_ = [("x", c_float),("y", c_float),("w", c_float),("h", c_float)]class DETECTION(Structure):_fields_ = [("bbox", BOX),("classes", c_int),("prob", POINTER(c_float)),("mask", POINTER(c_float)),("objectness", c_float),("sort_class", c_int)]class IMAGE(Structure):_fields_ = [("w", c_int),("h", c_int),("c", c_int),("data", POINTER(c_float))]class METADATA(Structure):_fields_ = [("classes", c_int),("names", POINTER(c_char_p))]#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
lib = CDLL("/home/mahxn0/darknet/darknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_intpredict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGEget_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
get_network_boxes.restype = POINTER(DETECTION)make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_pdo_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]free_image = lib.free_image
free_image.argtypes = [IMAGE]letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGEload_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATAload_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGErgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)net = load_net(b"model/yolo_box/box.cfg", b"model/yolo_box/box.weights", 0)
meta = load_meta(b"model/yolo_box/box.data")class yolo(object):def __init__(self):passdef convertBack(self,x, y, w, h):xmin = int(round(x - (w / 2)))xmax = int(round(x + (w / 2)))ymin = int(round(y - (h / 2)))ymax = int(round(y + (h / 2)))return xmin, ymin, xmax, ymaxdef array_to_image(self,arr):# need to return old values to avoid python freeing memoryarr = arr.transpose(2,0,1)c, h, w = arr.shape[0:3]arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0data = arr.ctypes.data_as(POINTER(c_float))im = IMAGE(w,h,c,data)return im, arrdef detect(self,image, thresh=.5, hier_thresh=.5, nms=.45):im, image = self.array_to_image(image)rgbgr_image(im)num = c_int(0)pnum = pointer(num)predict_image(net, im)dets = get_network_boxes(net, im.w, im.h, thresh,hier_thresh, None, 0, pnum)num = pnum[0]if nms: do_nms_obj(dets, num, meta.classes, nms)res = []for j in range(num):a = dets[j].prob[0:meta.classes]if any(a):ai = np.array(a).nonzero()[0]for i in ai:b = dets[j].bbox#res.append((meta.names[i], dets[j].prob[i],# (b.x, b.y, b.w, b.h)))left=(b.x-b.w/2)right=(b.x+b.w/2)top=(b.y-b.h/2)bot=(b.y+b.h/2)if left < 0:left = 0if right > im.w-1:right = im.w-1if top < 0:top = 0if bot > im.h-1:bot = im.h-1res.append((meta.names[i], dets[j].prob[i],left,top,right,bot))res = sorted(res, key=lambda x: -x[1])if isinstance(image, bytes): free_image(im)free_detections(dets, num)return resif __name__ == "__main__":# load video herecap = cv2.VideoCapture("board0.mp4")ret, img = cap.read()fps = cap.get(cv2.CAP_PROP_FPS)yolo=yolo()print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps))cv2.namedWindow("img", cv2.WINDOW_NORMAL)while(1):ret, img = cap.read()if ret:# r = detect_np(net, meta, img)r = yolo.detect(img)for i in r:x, y, w, h = i[2][0], i[2][1], i[2][2], i[2][3]xmin, ymin, xmax, ymax = yolo.convertBack(float(x), float(y), float(w), float(h))pt1 = (xmin, ymin)pt2 = (xmax, ymax)cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)cv2.putText(img, i[0].decode() + " [" + str(round(i[1] * 100, 2)) + "]", (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, [0, 255, 0], 4)cv2.imshow("img", img)if cv2.waitKey(1) & 0xFF == ord('q'):break
小礼物走一走，来简书关注我
作者：Mahxn0
链接：https://www.jianshu.com/p/9c87e039c949
来源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。
yolov3/tiny-yolov3训练和测试 python2/3相关推荐
- yolov3 tiny训练自己的数据集进行物体检测 (只检测行人)
说明:我的电脑是个16年3500元买的笔记本(勇气可嘉:) 环境:额外的包之类的,我用的anaconda,可以非常容易安装各类包,如果运行显示缺包,那就去environment去安装对应的包. 我的版 ...
- 比Tiny YOLOv3小8倍，性能提升11个点，4MB的网络也能做目标检测
在本文中,来自滑铁卢大学与 Darwin AI 的研究者提出了名为 YOLO Nano 的网络,他们通过人与机器协同设计模型架构大大提升了性能.YOLO Nano 大小只有 4.0MB 左右,比 Ti ...
- keras-yolov3训练及测试详解
本教程为keras-yolov3版本的训练及测试全过程实现,为保证对新手的友好性,不会过多解释原理,主要是让新手能对全过程有个比较清楚的概念和认识,方便训练自己的数据. 本教程一共有三个部分:一.数据 ...
- 基于YOLO的手部检测和计数实现（课程设计，训练和测试）（1）
基于YOLO的手部检测和计数实现(课程设计)支持YOLOV3和YOLOV3-TINY 训练测试代码.数据集.测试视频下载地址:下载地址 环境要求: * Python 3.7 * PyTorch > ...
- DL之YoloV3：YoloV3论文《YOLOv3: An Incremental Improvement》的翻译与解读
DL之YoloV3:YoloV3论文<YOLOv3: An Incremental Improvement>的翻译与解读 目录 YoloV3论文翻译与解读 Abstract 1. Intr ...
- NLP之词向量：利用word2vec对20类新闻文本数据集进行词向量训练、测试(某个单词的相关词汇)
NLP之词向量:利用word2vec对20类新闻文本数据集进行词向量训练.测试(某个单词的相关词汇) 目录 输出结果 设计思路 核心代码 输出结果 寻找训练文本中与morning最相关的10个词汇: ...
- fpga实战训练精粹pdf_tensorflow版PSENet 文本检测模型训练和测试
向AI转型的程序员都关注了这个号??? 机器学习AI算法工程 公众号:datayx psenet核心是为了解决基于分割的算法不能区分相邻文本的问题,以及对任意形状文本的检测问题. psenet依然 ...
- Caffe-windows入门学习，编译、训练、测试详细教程
####目录结构 一. 准备工作 二. 编译 2.1.开启相关caffe版本的编译开关配置内容 2.2.手动更改或者指定版本对应编译器目录 2.3.下载依赖文件dependencies文件到指定目录 ...
- caffe安装_目标检测之caffe-ssd模型训练与测试
最近把一个ssd网络的net..prototxt网络结构和自己生成的hdf5格式数据一起做训练时发现经常报错,因为ssd中一些层在caffe中并没有实现,需要自己写相应的.cpp,.cu文件重新编译, ...
最新文章
- 单片机练习-RC-5红外遥控器程序及简单制造DIY PC遥控器
- linux中的FTP服务配置详解
- ScriptManager控件声明的各个部分
- SpringBoot+thymeleaf实现文件下载（已实践，全流程）
- 单核工作法13：永不拖延（上）
- PyTorch基础(part4)
- videojs暂停时显示大按钮_紧急！西安老人扶梯上仰面向后晕倒，这个救命按钮很多人不知道...
- 特斯拉副总裁陶琳：Model Y 本月开始陆续交付
- Spring AOP 性能监控器
- 电脑版微信怎么看朋友圈_电脑上也可以看朋友圈，99%的人都不知道！
- excel生成多个sheet .net
- java数字后面加f_java 数字后面 f 和 l
- 计算机专业面试 英文,计算机专业英文面试自我介绍.doc
- mac配置adb环境变量
- 怎样让Windows便签的提醒事项一直留在锁定桌面上
- 安信可推荐 | 安信可ESP-C3模组和ESP32-S3模组的软件和硬件对比区别
- C++ Primer Plus 学习笔记(十一）
- 基本保险金额和保额的意思和区别是什么？
- 小票打印机ESC/POS命令集
- 【SSLGZ 1614】医院设置（Dijkstra）