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请问darknet的python接口更新后，make_boxes...这几个函数用什么替代? #679

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notobject opened this issue Apr 11, 2018 · 7 comments
Open

请问darknet的python接口更新后，make_boxes...这几个函数用什么替代? #679

notobject opened this issue Apr 11, 2018 · 7 comments

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@notobject
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notobject commented Apr 11, 2018 •
edited
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file: examples/detector-scipy-opencv.py

# Stupid python path shit.
# Instead just add darknet.py to somewhere in your python path
# OK actually that might not be a great idea, idk, work in progress
# Use at your own risk. or don't, i don't care

from scipy.misc import imread
import cv2
import sys, os
sys.path.append(os.path.join(os.getcwd(),'python/'))
import darknet as dn

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 detect2(net, meta, image, thresh=.5, 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



# Darknet
net = dn.load_net("cfg/yolov3.cfg", "yolov3.weights", 0)
meta = dn.load_meta("cfg/coco.data")
# r = dn.detect(net, meta, "data/dog.jpg")
# print r

# scipy
# arr= imread('data/dog.jpg')
# im = array_to_image(arr)
# r = detect2(net, meta, im)
# print r

# OpenCV
cap = cv2.VideoCapture(1)
while True:
    rect,frame = cap.read()
    im = array_to_image(frame)
    dn.rgbgr_image(im)
    r = detect2(net, meta, im)
    cv2.imshow("Demo",frame)
    print r

Error Messgae

.....
.....
Loading weights from yolov3.weights...Done!
Traceback (most recent call last):
  File "examples/detector-scipy-opencv.py", line 56, in <module>
    r = detect2(net, meta, im)
  File "examples/detector-scipy-opencv.py", line 23, in detect2
    boxes = dn.make_boxes(net)
AttributeError: 'module' object has no attribute 'make_boxes'
@lwplw
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lwplw commented Apr 12, 2018

同问

...
Traceback (most recent call last):
File "ship_demo.py", line 5, in
import darknet as dn
File "/media/lwp/lwplw/lwptuthinkjoy/demo/darknet.py", line 116, in
make_boxes = lib.make_boxes
File "/home/lwp/lwptuapp/anaconda3/lib/python3.6/ctypes/init.py", line 361, in getattr
func = self.getitem(name)
File "/home/lwp/lwptuapp/anaconda3/lib/python3.6/ctypes/init.py", line 366, in getitem
func = self._FuncPtr((name_or_ordinal, self))
AttributeError: libdarknet.so: undefined symbol: make_boxes

@khaerulumam42
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Are you try recompile with #289 ? Maybe @TheMikeyR post that code just for previous version. I tried like you do too, and get the same error.

Hello @TheMikeyR :)

@lwplw
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lwplw commented Apr 12, 2018

darknet.py文件中，我最后这样改写的
...

def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
    im = load_image(image, 0, 0)
    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].bbox
                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
    res = sorted(res, key=lambda x: -x[1])
    free_image(im)
    free_detections(dets, num)
    return res

def detect_np(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
    im = array_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].bbox
                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
    res = sorted(res, key=lambda x: -x[1])
    free_image(im)
    free_detections(dets, num)
    return res

...
array_to_image()是参考的/examples/detector-scipy-opencv.py中的：

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

所以没有再用到make_boxes...这几个函数，现在报错是这样的：
Loading weights from ship_classifier/shipcla_421.weights...Done!
*** Error in `python': double free or corruption (top): 0x000055cf89c15920 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fe48fc057e5]
/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fe48fc0e37a]
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fe48fc1253c]
/home/lwp/lwptuapp/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0xc06d)[0x7fe48d64806d]
python(+0x19dba6)[0x55cef8ecbba6]
python(+0xef0da)[0x55cef8e1d0da]

@TheMikeyR
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It seems like the python wrapper haven't been updated with yolov3 implementation, it still works with yolov2. When I run it, it doesn't recognize the new layer types.

python webcam.py
Frames per second using video.get(cv2.CAP_PROP_FPS) : 30.0
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32
    1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64
    2 conv     32  1 x 1 / 1   208 x 208 x  64   ->   208 x 208 x  32
    3 conv     64  3 x 3 / 1   208 x 208 x  32   ->   208 x 208 x  64
    4 Shortcut Layer: 1
    5 conv    128  3 x 3 / 2   208 x 208 x  64   ->   104 x 104 x 128
    6 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64
    7 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128
    8 Shortcut Layer: 5
    9 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64
   10 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128
   11 Shortcut Layer: 8
   12 conv    256  3 x 3 / 2   104 x 104 x 128   ->    52 x  52 x 256
   13 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   14 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   15 Shortcut Layer: 12
   16 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   17 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   18 Shortcut Layer: 15
   19 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   20 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   21 Shortcut Layer: 18
   22 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   23 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   24 Shortcut Layer: 21
   25 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   26 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   27 Shortcut Layer: 24
   28 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   29 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   30 Shortcut Layer: 27
   31 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   32 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   33 Shortcut Layer: 30
   34 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128
   35 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256
   36 Shortcut Layer: 33
   37 conv    512  3 x 3 / 2    52 x  52 x 256   ->    26 x  26 x 512
   38 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   39 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   40 Shortcut Layer: 37
   41 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   42 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   43 Shortcut Layer: 40
   44 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   45 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   46 Shortcut Layer: 43
   47 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   48 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   49 Shortcut Layer: 46
   50 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   51 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   52 Shortcut Layer: 49
   53 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   54 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   55 Shortcut Layer: 52
   56 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   57 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   58 Shortcut Layer: 55
   59 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256
   60 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512
   61 Shortcut Layer: 58
   62 conv   1024  3 x 3 / 2    26 x  26 x 512   ->    13 x  13 x1024
   63 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   64 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   65 Shortcut Layer: 62
   66 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   67 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   68 Shortcut Layer: 65
   69 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   70 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   71 Shortcut Layer: 68
   72 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   73 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   74 Shortcut Layer: 71
   75 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   76 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   77 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   78 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   79 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512
   80 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   81 conv     75  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x  75
   82 Type not recognized: [yolo]
Unused field: 'mask = 6,7,8'
Unused field: 'anchors = 10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326'
Unused field: 'classes = 20'
Unused field: 'num = 9'
Unused field: 'jitter = .3'
Unused field: 'ignore_thresh = .5'
Unused field: 'truth_thresh = 1'
Unused field: 'random = 1'
   83 route  79
   84 conv    256  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 256
   85 Type not recognized: [upsample]
Unused field: 'stride = 2'
   86 route  85 61
   87 python: [1]    16910 segmentation fault (core dumped)  python webcam.py

@mcximing
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mcximing commented Apr 18, 2018 •
edited
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Hello @lwplw,

I think the "double free" error is caused by the "free_image(im)" in the last of your "detect_np" function. Since the data of image here is managed by numpy, there is no need to free it as the original function does, or you will get the “double free” error. I deleted this "free_image(im)" and got a correct output. But I'm not sure if this will cause a memory leak. （一句话来说，我觉得是那个 free_image 重复释放了内存，导致你那个错误，我这删掉没报错）

@iloveOREO
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Hello @lwplw,

I think the "double free" error is caused by the "free_image(im)" in the last of your "detect_np" function. Since the data of image here is managed by numpy, there is no need to free it as the original function does, or you will get the “double free” error. I deleted this "free_image(im)" and got a correct output. But I'm not sure if this will cause a memory leak. （一句话来说，我觉得是那个 free_image 重复释放了内存，导致你那个错误，我这删掉没报错）

Thank you very much! It bothered me for few hours.
It did work for the "double free " problem.

@jiagh2010
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finally ，HOW TO SOLVE????_-------->>>>makebox error

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@TheMikeyR @mcximing @lwplw @iloveOREO @jiagh2010 @notobject @khaerulumam42