[人工智能]YOLO跌倒检测笔记

YOLO跌倒检测笔记

代码来源：

https://github.com/qiaoguan/Fall-detection/blob/master/demo.gif

介绍：

上面原始代码是python2/opencv2写的，会有一些函数引用会发生变化。代码是在darknet c++版本的基础上训练的，首先利用makefile文件，生成可执行文件及动态文件(.so/.a)，使用命令“sudo make”生成过程中可能会遇见编译错误的问题，一般是makefile中的前两行cuda与cudnn的问题，如果ubuntu中安装了cuda和cudnn，则设置为1，否则设置为0，设置为0时编译一般没有什么问题，但是设置为1时会出现问题，一般原因的cuda的调用路径不对导致的，把NVCC-nvcc更改为NVCC=/usr/local/cuda-9.0/bin/nvcc即可，具体更改代码如下。oepncv加载图像时或后面训练好后识别时，针对python2升级python3的问题，要在路径前加b或者后面加encode("utf-8").

如果遇到cuda的问题，一般是按装版本的问题，安装过程网上很多，安装完成后，一般添加bin环境变量，libs路径，和头文件路径：? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?cd ~ 进入根目录? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?vi .bashrc? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?export PATH="/usr/local/cuda-9.0/bin:$PATH"? 可执行文件? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?export PATH="/usr/local/cuda-9.0/include:$PATH"? 二进制文件? ?

export? LD_LIBRARY_PATH=$LD_LIBRARYPATH:/usr/local/cuda-9.0/lib64? ?库文件

source .bashrc? 激活

查看cuda版本：两种方式 驱动api和运行api版本

驱动api版本：nvidia-smi

运行api版本： cat /usr/local/cuda/version.txt

? ? ? ? ? ? ? nvcc -V

? ? ? ? ? ? ? cat /usr/local/cuda/include/cudnn.h

? ? ? ? ? ? ? nvcc--version?

ibcudart.so.8.0: cannot open shared object file: No such file or directory 的解决办法

libcudart.so.8.0: cannot open shared object file: No such file or directory 的解决办法_volcano_Lin 的博客-CSDN博客

成功解决AttributeError: module 'cv2.cv2' has no attribute 'CV_CAP_PROP_FPS'和 'CV_CAP_PROP_FRAME_WIDTH'

成功解决AttributeError: module 'cv2.cv2' has no attribute 'CV_CAP_PROP_FPS'和 'CV_CAP_PROP_FRAME_WIDTH'_一个处女座的程序猿-CSDN博客

Opencv中FOURCC详解

Opencv中FOURCC详解_持久决心的博客-CSDN博客_fourcc

Python opencv 调用摄像头时设置以MJPG等编码格式获取视频

Python opencv 调用摄像头时设置以MJPG等编码格式获取视频_向死而生zzz的博客-CSDN博客

解决ctypes.ArgumentError: argument 1: ＜class ‘TypeError‘＞: wrong type

解决ctypes.ArgumentError: argument 1: ＜class ‘TypeError‘＞: wrong type_门前大橋下丶-CSDN博客

/bin/sh: 1: nvcc: not found Makefile:89: recipe for target 'obj/convolutional_kernels.o' failed

/bin/sh: 1: nvcc: not found Makefile:89: recipe for target 'obj/convolutional_kernels.o' failed_hunzhangzui9837的博客-CSDN博客

pypi

Search results · PyPI

yolo

YOLO: Real-Time Object Detection

ps:

makefile

GPU=1
CUDNN=1
OPENCV=1
OPENMP=0
DEBUG=0

ARCH= -gencode arch=compute_30,code=sm_30 \
      -gencode arch=compute_35,code=sm_35 \
      -gencode arch=compute_50,code=[sm_50,compute_50] \
      -gencode arch=compute_52,code=[sm_52,compute_52]
#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify
# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/

CC=gcc
# NVCC=nvcc
NVCC=/usr/local/cuda-9.0/bin/nvcc
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC

ifeq ($(OPENMP), 1)
CFLAGS+= -fopenmp
endif

ifeq ($(DEBUG), 1)
OPTS=-O0 -g
endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), 1)
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv`
COMMON+= `pkg-config --cflags opencv`
endif

ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda-9.0/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda-9.0/lib64 -lcuda -lcudart -lcublas -lcurand
endif

ifeq ($(CUDNN), 1) 
COMMON+= -DCUDNN 
CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif

OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o attention.o darknet.o
ifeq ($(GPU), 1) 
LDFLAGS+= -lstdc++ 
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o
endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)
all: obj  results $(SLIB) $(ALIB) $(EXEC)


$(EXEC): $(EXECOBJ) $(ALIB)
	$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)
	$(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)
	$(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)
	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)
	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:
	mkdir -p obj
backup:
	mkdir -p backup
results:
	mkdir -p results

.PHONY: clean

clean:
	rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ)

gq1.py

#Author: qiaoguan(https://github.com/qiaoguan)
import cv2
import sys
sys.path.append('python')
import darknet as dn
import time

def array_to_image(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 = dn.c_array(dn.c_float, arr)
    im = dn.IMAGE(w,h,c,data)
    return im

def detect(net, meta, image, thresh=.24, hier_thresh=.5, nms=.45):
    boxes = dn.make_boxes(net)
    probs = dn.make_probs(net)
    num =   dn.num_boxes(net)
    dn.network_detect(net, image, thresh, hier_thresh, nms, boxes, probs)
    res = []
    for j in range(num):
        for i in range(meta.classes):
            if probs[j][i] > 0:
                res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
    res = sorted(res, key=lambda x: -x[1])
    dn.free_ptrs(dn.cast(probs, dn.POINTER(dn.c_void_p)), num)
    return res

def isFall(w,h):
    if float(w)/h>=1.1:
        return True
    else:
        return False

#open the input video file
input_movie=cv2.VideoCapture('cs4.mp4')

length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))

# Create an output movie file (make sure resolution/frame rate matches input video!)
#get fps the size 
fps = input_movie.get(cv2.CAP_PROP_FPS)
size = (int(input_movie.get(cv2.CAP_PROP_FRAME_WIDTH)),
        int(input_movie.get(cv2.CAP_PROP_FRAME_HEIGHT)))

#define the type of the output movie  
output_movie = cv2.VideoWriter('out_cs4.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, size)
#output_movie = cv2.VideoWriter('output_cs1.avi', -1, fps, size)

# load network and weights
print("load network and weights")
net = dn.load_net(b"./cfg/yolo.cfg", b"./yolo.weights", 0)
meta = dn.load_meta(b"./cfg/coco.data")

res=[]
frame_number=0
while True:
    # Grab a single frame of video
    ret, frame = input_movie.read()
    frame_number += 1

    # Quit when the input video file ends
    if not ret:
        break
    '''
    # detect per 2 frame
    if frame_number%2==0:
        continue
    '''
    # append all the coordinate of the detected person to res
    im = array_to_image(frame)

    start=time.time()
    ##########################
    # im="./data/cs1.png"
    # im = cv2.imread(im)
    # frame=im
    # im = array_to_image(im)
    # image= b"./data/cs1.png"
    # im = dn.load_image(image, 0, 0)
    ###########################
    r = detect(net, meta, im)
    print('the whole running time is: '+str(time.time()-start))
    print(r)
    res=[]
    for item in r:
        if item[0]==b'person' or item[0]==b'dog' or item[0]==b'cat' or item[0]==b'horse':
            res.append(item)
    # if multiple exist, and there also contains person,  preserve person only!
    print('--------------')
    print(res)
    # if len(res)>1:
    #     for item in res:
    #         if item[0]=='person':
    #             res=[]
    #             res.append(item)
    #             break
                
    # get the max rectangle
    result=[]
    maxArea=0
    if len(res)>1:
        for item in res:
            if item[2][2]*item[2][3]>maxArea:
                maxArea=item[2][2]*item[2][3]
                result=item
    elif len(res)==1:
        result=res[0]   
    #draw the result 
    if(len(result)>0):      
        # label the result
        left=int(result[2][0]-result[2][2]/2)
        top=int(result[2][1]-result[2][3]/2)
        right=int(result[2][0]+result[2][2]/2)
        bottom=int(result[2][1]+result[2][3]/2)
        
        #whether fall?
        if isFall(result[2][2],result[2][3]):
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
    
            # Draw a label with a name below the face
            cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 0, 255))
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, 'Warning!!!', (left + 6, bottom - 6), font, 0.5, (255, 0, 0), 1)
        else:
            cv2.rectangle(frame, (left, top), (right, bottom), (255, 0, 0), 2)
    '''
    # label the result
    for item in res:
        # Draw a box around the face
        name=item[0]
        
        left=int(item[2][0]-item[2][2]/2)
        top=int(item[2][1]-item[2][3]/2)
        right=int(item[2][0]+item[2][2]/2)
        bottom=int(item[2][1]+item[2][3]/2)
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 0, 255))
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)
    '''
    #Display the result
    cv2.imshow('Fall detection',frame)   
    # Write the resulting image to the output video file
    print("Writing frame {} / {}".format(frame_number, length))
    output_movie.write(frame)
    
    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
# All done!
input_movie.release()
cv2.destroyAllWindows()

darknet.py

from ctypes import *
import math
import random
import time
import cv2
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 i
    return len(probs)-1

def c_array(ctype, values):
    arr = (ctype*len(values))()
    arr[:] = values
    return arr

class BOX(Structure):
    _fields_ = [("x", c_float),
                ("y", c_float),
                ("w", c_float),
                ("h", c_float)]

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/ljj/share/tensorflow-yolov3-master/Fall-detection-master/libdarknet.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_int

predict = 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 = IMAGE

make_boxes = lib.make_boxes
make_boxes.argtypes = [c_void_p]
make_boxes.restype = POINTER(BOX)

free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]

num_boxes = lib.num_boxes
num_boxes.argtypes = [c_void_p]
num_boxes.restype = c_int

make_probs = lib.make_probs
make_probs.argtypes = [c_void_p]
make_probs.restype = POINTER(POINTER(c_float))

detect = lib.network_predict
detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(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_p

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 = IMAGE

load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA

load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE

rgbgr_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)

network_detect = lib.network_detect
network_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]

def classify(net, meta, im):
    out = predict_image(net, im)
    res = []
    for i in range(meta.classes):
        res.append((meta.names[i], out[i]))
    res = sorted(res, key=lambda x: -x[1])
    return res

def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
    print(type(image))
   # print(image.shape)
    im = load_image(image, 0, 0)
    print(type(im))
    boxes = make_boxes(net)
    probs = make_probs(net)
    num =   num_boxes(net)
    network_detect(net, im, thresh, hier_thresh, nms, boxes, probs)
    res = []
    for j in range(num):
        for i in range(meta.classes):
            if probs[j][i] > 0:
                res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
    res = sorted(res, key=lambda x: -x[1])
    free_image(im)
    free_ptrs(cast(probs, POINTER(c_void_p)), num)
    return res
    
if __name__ == "__main__":
    #net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
    #im = load_image("data/wolf.jpg", 0, 0)
    #meta = load_meta("cfg/imagenet1k.data")
    #r = classify(net, meta, im)
    #print r[:10]
    # net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
    net = load_net(b"../cfg/yolo.cfg", b"../yolo.weights", 0)
    meta = load_meta(b"../cfg/coco.data")
    start=time.time()
    # input_movie = cv.VideoCapture('Video1.avi')
    r = detect(net, meta, b"../data/cs1.png")
    print('the whole running time is: '+str(time.time()-start))
    print(r)
    res=[]
    for item in r:
        if item[0]=='person' or item[0]=='dog':
            res.append(item)
            print(item)
            print(item[0])
            print(item[1])
            print(item[2][0])
            print(item[2][1])
            print(item[2][2])
            print(item[2][3])

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