为什么Opencv3.4.0使用make编译时出现错误?

环境:Debian(Raspiberry Pi Buster)

cmake version: 3.0.2

gcc version: 8.2.0

python version: 3.7.2

make version: 4.2.1

本来计划用opencv基于python进行图像辨别

在使用cmake配置完成后使用make命令编译:

sudo make

编译到大约90%时,出现如下错误:

#这是之前很多条控制台信息中的warning
warning: cast between incompatible function types from ‘PyObject* (*)(PyObject*, PyObject*, PyObject*)’ {aka ‘_object* (*)(_object*, _object*, _object*)’} to ‘PyCFunction’ {aka ‘_object* (*)(_object*, _object*)’} [-Wcast-function-type]

#make错误,退出
make[2]: *** [modules/python3/CMakeFiles/opencv_python3.dir/build.make:56: modules/python3/CMakeFiles/opencv_python3.dir/__/src2/cv2.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:21149: modules/python3/CMakeFiles/opencv_python3.dir/all] Error 2
make: *** [Makefile:138: all] Error 2

如图(图1中使用了make -i选项忽略错误,继续编译剩余部分,但open_cv python3没有成功编译):

图片说明

(图2显示了make的错误)

图片说明

#忽略错误后出现的c++error
c++: error: Cmakefiles/opencv_python3.dir/__/src2/cv2.cpp.o No such file or dictionary

在网上看到的编译错误的例子都和这个有所区别,没有能够很好地解决问题。有的说这是gcc编译器的问题

希望各位大佬们能够帮忙分析一下错误的原因,因为我的项目急需配置好opencv,也没有其它更好的替代,谢谢~

weixin_42641927
星城爵迹 回复qq_37760746: 大佬。我想问问这个问题你解决了吗?
2 个月之前 回复
qq_37760746
qq_37760746 大佬,你这问题解决了吗?
3 个月之前 回复
weixin_43418076
weixin_43418076 博主你好,我也遇到了同样的问题,请问你解决了吗
4 个月之前 回复
ToothlessTheFury
\home\Anthony_cao\Anthony_cao.c https://github.com/TommyZihao/Zihao-Blog/blob/master/E5%AD%90%E8%B1%AA%E5%85%84%E6%95%99%E4%BD%A0%E5%9C%A8%E6%A0%91%E8%8E%93%E6%B4%BE%E4%B8%8A%E5%AE%89%E8%A3%85OpenCV.md(我编译基于的教程)
6 个月之前 回复

3个回答

我和小伙伴解决了这个问题,我们的设备是树莓派3B。
方案一:如果你不需要opencv4.X的版本。先需要安装opencv的依赖库,再使用sudo apt-get install python3-opencv,就可以安装opencv,
并且不需要编译,opencv的版本是3.2.
方案二:如果需要opencv4.1.0版本,编译的过程比较顺利,安装成功。具体的步骤可以在网上找找,注意最好使用32G的内存卡,不然后面会卡住。当然了,我们也没弄懂那个错误的原因,但是还是解决了错误。
如果有大佬知道原因的话欢迎帮忙回答哈。

编译的问题没有解决,但找到了知乎上一位大佬编译的预装opencv的镜像(同时预装TensorFlow)
链接:Here:
毕竟我的树莓派上没有重要的数据。。。

注意以下三点:
1. 压缩包中后缀为.z01的压缩文件要和第二个.zip文件一起解压(上传时拆分为两个)
2. SD卡需格式化为FAT32, 最好使用镜像烧写工具,如Etcher下载地址
3. 启动后使用Raspi-config扩展文件系统,否则存储空间会不准确

同时也希望知道对于问题中错误的解决方案的人能够回答以下这个问题。毕竟不是每个人的数据都输得起啊!

用手机截make过程中的图(跳太快了),看到前面编译的时候error 错误出在cv2.cpp里面 899行 将 char*=xxxx改为 const char*=xxxx 即可编译

qq_42763137
qq_42763137 请问那些警告就可以不管了吗
20 天之前 回复
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Arm Cortex-A7 NXP i.MX6Q 5. 已经sudo为root 执行过程 cd opencv-3.4.1 mkdir build cd build cmakd -D CMAKE_BUILD_TYPE=Relase -D CMAKE_INSTALL_PREFIX=/usr/local .. make make install 出错提示: [ 0%] Built target gen-pkgconfig [ 9%] Built target libwebp [ 14%] Built target IlmImf [ 20%] Built target libprotobuf [ 23%] Built target carotene_objs [ 24%] Built target tegra_hal [ 24%] Built target opencv_ts_pch_dephelp [ 24%] Built target pch_Generate_opencv_ts [ 25%] Built target opencv_core_pch_dephelp [ 25%] Built target pch_Generate_opencv_core [ 31%] Built target opencv_core [ 31%] Built target opencv_imgproc_pch_dephelp [ 31%] Built target pch_Generate_opencv_imgproc [ 31%] Building CXX object modules/imgproc/CMakeFiles/opencv_imgproc.dir/src/smooth.cpp.o /home/linaro/opencv-3.4.1/modules/imgproc/src/smooth.cpp: In function ‘void cv::vlineSmooth1N(const FT* const*, const FT*, int, ET*, int) [with ET = unsigned char, FT = {anonymous}::ufixedpoint16]’: 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yolo3 darknet.py问题
我用darknetAB https://github.com/AlexeyAB/darknet 编译gpu版本后生成darknet.py文件 然后我也编译了yolo_cpp_dll.sln文件 生成dll文件 然后运行darknet.py文件 不显示图片 异常退出 ![图片说明](https://img-ask.csdn.net/upload/201911/02/1572688446_628910.png) 百度了这个问题 有人说要换python3.5版本 我也尝试了 但是也是不行 不会显示图片。请问各位大佬到底怎么解决??急!!!谢谢!!! ``` #!python3 """ Python 3 wrapper for identifying objects in images Requires DLL compilation Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll". On a GPU system, you can force CPU evaluation by any of: - Set global variable DARKNET_FORCE_CPU to True - Set environment variable CUDA_VISIBLE_DEVICES to -1 - Set environment variable "FORCE_CPU" to "true" To use, either run performDetect() after import, or modify the end of this file. See the docstring of performDetect() for parameters. Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py @author: Philip Kahn @date: 20180503 """ #pylint: disable=R, W0401, W0614, W0703 from ctypes import * import math import random import os 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 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("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) os.environ['PATH'] = cwd + ';' + os.environ['PATH'] winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll") winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll") envKeys = list() for k, v in os.environ.items(): envKeys.append(k) try: try: tmp = os.environ["FORCE_CPU"].lower() if tmp in ["1", "true", "yes", "on"]: raise ValueError("ForceCPU") else: print("Flag value '"+tmp+"' not forcing CPU mode") except KeyError: # We never set the flag if 'CUDA_VISIBLE_DEVICES' in envKeys: if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0: raise ValueError("ForceCPU") try: global DARKNET_FORCE_CPU if DARKNET_FORCE_CPU: raise ValueError("ForceCPU") except NameError: pass # print(os.environ.keys()) # print("FORCE_CPU flag undefined, proceeding with GPU") if not os.path.exists(winGPUdll): raise ValueError("NoDLL") lib = CDLL(winGPUdll, RTLD_GLOBAL) except (KeyError, ValueError): hasGPU = False if os.path.exists(winNoGPUdll): lib = CDLL(winNoGPUdll, RTLD_GLOBAL) print("Notice: CPU-only mode") else: # Try the other way, in case no_gpu was # compile but not renamed lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") else: lib = CDLL("./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 copy_image_from_bytes = lib.copy_image_from_bytes copy_image_from_bytes.argtypes = [IMAGE,c_char_p] def network_width(net): return lib.network_width(net) def network_height(net): return lib.network_height(net) predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) if hasGPU: 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 get_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_ptr 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_p load_net_custom = lib.load_network_custom load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] load_net_custom.restype = c_void_p do_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 = 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) predict_image_letterbox = lib.network_predict_image_letterbox predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) def array_to_image(arr): import numpy as np # need to return old values to avoid python freeing memory arr = arr.transpose(2,0,1) c = arr.shape[0] h = arr.shape[1] w = arr.shape[2] arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 data = arr.ctypes.data_as(POINTER(c_float)) im = IMAGE(w,h,c,data) return im, arr def classify(net, meta, im): out = predict_image(net, im) res = [] for i in range(meta.classes): if altNames is None: nameTag = meta.names[i] else: nameTag = altNames[i] res.append((nameTag, out[i])) res = sorted(res, key=lambda x: -x[1]) return res def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ Performs the meat of the detection """ #pylint: disable= C0321 im = load_image(image, 0, 0) if debug: print("Loaded image") ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug) free_image(im) if debug: print("freed image") return ret def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import cv2 #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) num = c_int(0) if debug: print("Assigned num") pnum = pointer(num) if debug: print("Assigned pnum") predict_image(net, im) letter_box = 0 #predict_image_letterbox(net, im) #letter_box = 1 if debug: print("did prediction") # dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box) if debug: print("Got dets") num = pnum[0] if debug: print("got zeroth index of pnum") if nms: do_nms_sort(dets, num, meta.classes, nms) if debug: print("did sort") res = [] if debug: print("about to range") for j in range(num): if debug: print("Ranging on "+str(j)+" of "+str(num)) if debug: print("Classes: "+str(meta), meta.classes, meta.names) for i in range(meta.classes): if debug: print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i])) if dets[j].prob[i] > 0: b = dets[j].bbox if altNames is None: nameTag = meta.names[i] else: nameTag = altNames[i] if debug: print("Got bbox", b) print(nameTag) print(dets[j].prob[i]) print((b.x, b.y, b.w, b.h)) res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) if debug: print("did range") res = sorted(res, key=lambda x: -x[1]) if debug: print("did sort") free_detections(dets, num) if debug: print("freed detections") return res netMain = None metaMain = None altNames = None def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): """ Convenience function to handle the detection and returns of objects. Displaying bounding boxes requires libraries scikit-image and numpy Parameters ---------------- imagePath: str Path to the image to evaluate. Raises ValueError if not found thresh: float (default= 0.25) The detection threshold configPath: str Path to the configuration file. Raises ValueError if not found weightPath: str Path to the weights file. Raises ValueError if not found metaPath: str Path to the data file. Raises ValueError if not found showImage: bool (default= True) Compute (and show) bounding boxes. Changes return. makeImageOnly: bool (default= False) If showImage is True, this won't actually *show* the image, but will create the array and return it. initOnly: bool (default= False) Only initialize globals. Don't actually run a prediction. Returns ---------------------- When showImage is False, list of tuples like ('obj_label', confidence, (bounding_box_x_px, bounding_box_y_px, bounding_box_width_px, bounding_box_height_px)) The X and Y coordinates are from the center of the bounding box. Subtract half the width or height to get the lower corner. Otherwise, a dict with { "detections": as above "image": a numpy array representing an image, compatible with scikit-image "caption": an image caption } """ # Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again global metaMain, netMain, altNames #pylint: disable=W0603 assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" if not os.path.exists(configPath): raise ValueError("Invalid config path `"+os.path.abspath(configPath)+"`") if not os.path.exists(weightPath): raise ValueError("Invalid weight path `"+os.path.abspath(weightPath)+"`") if not os.path.exists(metaPath): raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") if netMain is None: netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 if metaMain is None: metaMain = load_meta(metaPath.encode("ascii")) if altNames is None: # In Python 3, the metafile default access craps out on Windows (but not Linux) # Read the names file and create a list to feed to detect try: with open(metaPath) as metaFH: metaContents = metaFH.read() import re match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE) if match: result = match.group(1) else: result = None try: if os.path.exists(result): with open(result) as namesFH: namesList = namesFH.read().strip().split("\n") altNames = [x.strip() for x in namesList] except TypeError: pass except Exception: pass if initOnly: print("Initialized detector") return None if not os.path.exists(imagePath): raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") # Do the detection #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) if showImage: try: from skimage import io, draw import numpy as np image = io.imread(imagePath) print("*** "+str(len(detections))+" Results, color coded by confidence ***") imcaption = [] for detection in detections: label = detection[0] confidence = detection[1] pstring = label+": "+str(np.rint(100 * confidence))+"%" imcaption.append(pstring) print(pstring) bounds = detection[2] shape = image.shape # x = shape[1] # xExtent = int(x * bounds[2] / 100) # y = shape[0] # yExtent = int(y * bounds[3] / 100) yExtent = int(bounds[3]) xEntent = int(bounds[2]) # Coordinates are around the center xCoord = int(bounds[0] - bounds[2]/2) yCoord = int(bounds[1] - bounds[3]/2) boundingBox = [ [xCoord, yCoord], [xCoord, yCoord + yExtent], [xCoord + xEntent, yCoord + yExtent], [xCoord + xEntent, yCoord] ] # Wiggle it around to make a 3px border rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape= shape) rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape= shape) rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape= shape) boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0) draw.set_color(image, (rr, cc), boxColor, alpha= 0.8) draw.set_color(image, (rr2, cc2), boxColor, alpha= 0.8) draw.set_color(image, (rr3, cc3), boxColor, alpha= 0.8) draw.set_color(image, (rr4, cc4), boxColor, alpha= 0.8) draw.set_color(image, (rr5, cc5), boxColor, alpha= 0.8) if not makeImageOnly: io.imshow(image) io.show() detections = { "detections": detections, "image": image, "caption": "\n<br/>".join(imcaption) } except Exception as e: print("Unable to show image: "+str(e)) return detections if __name__ == "__main__": print(performDetect()) ```
懂交叉编译或ld.so.conf或者或者zedboard或者动态库相关的请进来。。
小弟最近没事儿,又研究了一下交叉编译opencv的事情。 第一步:在电脑上交叉编译opencv,make,make install之后,我把相关的include/和/lib和/share分别拷贝到zedboard板上的/usr/local/include ,/usr/local/lib/ ,/usr/local/share。在板子上运行的是linaro桌面系统. 第二步:在电脑上我用cmake指定交叉编译工具,库文件和头文件(都是交叉编译出来的,也就是arm平台的头文件和库文件),编译了源代码DisplayImage.cpp(这个代码其实就是《zynq协同 设计指南》这本书里面的那个egedetect.cpp的代码),生成可执行程序DisplayImage。 第三步:将DisplayImage拷贝到板子上运行: root@linaro-ubuntu-desktop:/mnt#./DisplayImage lena.jpg lena1.jpg ./DisplayImage: error while loading shared libraries: libopencv_calib3d.so.2.4: cannot open shared object file: No such file or directory 第四步:以上原因相信大家都知道,找不到共享库,于是我在/etc/ld.so.conf文件中添加了一行,如下可以看出: root@linaro-ubuntu-desktop:/mnt# cat /etc/ld.so.conf include /etc/ld.so.conf.d/*.conf /usr/local/lib 而我们需要的库libopencv_calib3d.so.2.4就在/usr/local/lib下面:如下可以看出: root@linaro-ubuntu-desktop:/mnt# ls /usr/local/lib/ |grep calib3d* libopencv_calib3d.so libopencv_calib3d.so.2.4 libopencv_calib3d.so.2.4.6 这些都确认好了之后,执行root@linaro-ubuntu-desktop:/mnt# /sbin/ldconfig 按理说,应该就是一切OK了赛,但是执行程序的时候效果如下: root@linaro-ubuntu-desktop:/mnt# /sbin/ldconfig root@linaro-ubuntu-desktop:/mnt# ./DisplayImage lena.jpg lena1.jpg ./DisplayImage: error while loading shared libraries: libopencv_calib3d.so.2.4: cannot open shared object file: No such file or directory **呵呵呵呵。。。还是不得行。然后我看了一下已经加载的共享库:** root@linaro-ubuntu-desktop:/mnt# ldconfig -p | grep calib3d libopencv_calib3d.so.2.4 (libc6) => /usr/local/lib/libopencv_calib3d.so.2.4 libopencv_calib3d.so (libc6) => /usr/local/lib/libopencv_calib3d.so 可以看出是已经加载了这个共享库的哦。 但是程序执行的时候怎么都找不到。 这是怎么回事:?????? 第五步:我们换种方法试试看,用LD_LIBRARY_PATH试试,具体如下: root@linaro-ubuntu-desktop:/mnt# export LD_LIBRARY_PATH=/usr/local/lib 然后执行程序看看什么情况,如下: root@linaro-ubuntu-desktop:/mnt# ./DisplayImage lena.jpg lena1.jpg root@linaro-ubuntu-desktop:/mnt# ls ?? AutoUnattend.xml backup lena.jpg sources ?? DisplayImage boot new support ??? WIN7Activation_1.7_XiaZaiBa.zip bootmgr setup.exe test ????? autorun.inf efi slope.cpp upgrade **这次程序执行时没得错误了,但是没得结果啊,这个程序时将lena.jpg采用什么边缘算子之后输出之后的图像lena1.jpg。。 查看了,没有生成lena1.jpg..** 这又是怎么回事儿》》????? **第六步:问题就来了,问什么用了ld.so.con这种方法,还是找不到库,而是用LD_LIBRARY_PAHT之后就可以?按理说应该都可以的问题就来了,为什么执行程序不出错,但是没有保存图像啊。。** 附录:DisplayImage.cpp的代码. #include "cv.h" #include "highgui.h" IplImage* doCanny( IplImage* in, double lowThresh, double highThresh, double aperture) { if (in->nChannels != 1) return(0); // Canny only handles gray scale images IplImage* out = cvCreateImage( cvGetSize( in ), in->depth, //IPL_DEPTH_8U, 1); cvCanny( in, out, lowThresh, highThresh, aperture ); return( out ); }; int main( int argc, char** argv ) { if(argc!= 3)printf("arguments error! format origin_image.bmp target_image.bmp\n"); IplImage* img_rgb = cvLoadImage( argv[1] ); IplImage* img_gry = cvCreateImage( cvSize( img_rgb->width,img_rgb->height ), img_rgb->depth, 1); cvCvtColor(img_rgb, img_gry ,CV_BGR2GRAY); // cvNamedWindow("Example Gray", CV_WINDOW_AUTOSIZE ); // cvNamedWindow("Example Canny", CV_WINDOW_AUTOSIZE ); // cvShowImage("Example Gray", img_gry ); IplImage* img_cny = doCanny( img_gry, 10, 100, 3 ); if(cvSaveImage(argv[2],img_cny,0)!=0) printf("Save Image Successful\n"); // cvShowImage("Example Canny", img_cny ); // cvWaitKey(0); cvReleaseImage( &img_rgb); cvReleaseImage( &img_gry); cvReleaseImage( &img_cny); // cvDestroyWindow("Example Gray"); // cvDestroyWindow("Example Canny"); return 0; }
opencv移植到arm上的问题
参考1:http://www.ebaina.com/bbs/forum. ... &extra=page%3D1 参考2: http://ebaina.com/bbs/forum.php? ... mp;highlight=opencv 我用的系统是ubuntu12.04.4 64位,安装了海思HI3515官方配套的SDK,gcc版本是4.6.3,cmake是2.8.10.1,然后新建一个build和output文件夹: root@duhao-virtual-machine:/home/study/hi-opencv-2.4.10# ls build opencv-2.4.10 output test 进入opencv-2.4.10,进行cmake-gui,然后配置是按参考2中的配置,输出选成上面的output目录 之后进入build目录,开始make和make install,最后在output里面生成 include,lib,share,LICENSE 。没有生成bin文件。: root@duhao-virtual-machine:/home/study/hi-opencv-2.4.10/output# ls include lib LICENSE share 然后将include下的opencv和opencv2拷贝到交叉编译器的include下 把lib下的文件也拷贝到交叉编译器的lib文件下 arm-hisiv100-linux-uclibcgnueabi-g++ face.cpp -I/home/study/hi-opencv-2.4.10/output/include/opencv -lopencv_highgui -lopencv_imgproc -lopencv_core -lpthread -lrt -ljpeg -lpng -lxml2 -lopencv_objdetect -o face 来编译以下参考1的程序 /***********Author:9crk 2014-12-24*****************************/ #include "cv.h" #include "highgui.h" #include "stdio.h" /******************for time mesurement*************************/ #include <sys/time.h> struct timeval tpstart,tpend; unsigned long timeuses; void timeRec() { gettimeofday(&tpstart,0); } int timeRep() { gettimeofday(&tpend,0); timeuses=(tpend.tv_sec-tpstart.tv_sec)*1000000+tpend.tv_usec-tpstart.tv_usec; printf("use time: %uus\n",timeuses); return timeuses; } /********************end**************************************/ int main(int argc, char* argv[]) { IplImage* img = NULL; IplImage* cutImg = NULL; CvMemStorage* storage = cvCreateMemStorage(0); CvHaarClassifierCascade* cascade = (CvHaarClassifierCascade*)cvLoad("./haarcascade_frontalface_alt2.xml", 0, 0, 0); CvSeq* faces; img = cvLoadImage(argv[1], 0); timeRec(); faces = cvHaarDetectObjects(img, cascade, storage, 1.2, 2, 0, cvSize(25,25) ); timeRep(); if (faces->total == 0){ printf("no face!\n"); } cvSetImageROI(img, *((CvRect*)cvGetSeqElem( faces, 0))); cvSaveImage("face.bmp", img); cvResetImageROI(img); printf("face detected! in face.bmp!\n"); } 出现以下错误: /usr/local/hi-arm/arm-hisiv100-linux/bin/../lib/gcc/arm-hisiv100-linux-uclibcgnueabi/4.4.1/../../../../arm-hisiv100-linux-uclibcgnueabi/lib/libxml2.so: warning: gethostbyname is obsolescent, use getnameinfo() instead. /usr/local/hi-arm/arm-hisiv100-linux/bin/../lib/gcc/arm-hisiv100-linux-uclibcgnueabi/4.4.1/../../../../arm-hisiv100-linux-uclibcgnueabi/lib/libopencv_objdetect.a(haar.cpp.obj): In function `cvHaarDetectObjectsForROC(void const*, CvHaarClassifierCascade*, CvMemStorage*, std::vector<int, std::allocator<int> >&, std::vector<double, std::allocator<double> >&, double, int, int, CvSize, CvSize, bool)': haar.cpp.text._Z25cvHaarDetectObjectsForROCPKvP23CvHaarClassifierCascadeP12CvMemStorageRSt6vectorIiSaIiEERS5_IdSaIdEEdii6CvSizeSC_b+0x538): undefined reference to `cvCvtColor' haar.cpp.text._Z25cvHaarDetectObjectsForROCPKvP23CvHaarClassifierCascadeP12CvMemStorageRSt6vectorIiSaIiEERS5_IdSaIdEEdii6CvSizeSC_b+0x16e8): undefined reference to `cvCanny' /usr/local/hi-arm/arm-hisiv100-linux/bin/../lib/gcc/arm-hisiv100-linux-uclibcgnueabi/4.4.1/../../../../arm-hisiv100-linux-uclibcgnueabi/lib/libopencv_objdetect.a(cascadedetect.cpp.obj): In function `cv::CascadeClassifier::detectMultiScale(cv::Mat const&, std::vector<cv::Rect_<int>, std::allocator<cv::Rect_<int> > >&, std::vector<int, std::allocator<int> >&, std::vector<double, std::allocator<double> >&, double, int, int, cv::Size_<int>, cv::Size_<int>, bool)': cascadedetect.cpp.text._ZN2cv17CascadeClassifier16detectMultiScaleERKNS_3MatERSt6vectorINS_5Rect_IiEESaIS6_EERS4_IiSaIiEERS4_IdSaIdEEdiiNS_5Size_IiEESH_b+0x2b4): undefined reference to `cv::cvtColor(cv::_InputArray const&, cv::_OutputArray const&, int, int)' collect2: ld returned 1 exit status 这几个错误不懂要怎么改。希望大神来指导一下,感激不尽啊!!! 上面cmake-gui时的配置如下: ![图片说明](https://img-ask.csdn.net/upload/201506/07/1433672253_982754.png) ![图片说明](https://img-ask.csdn.net/upload/201506/07/1433672221_224787.png) 只是输出改成了上面新建的文件夹output。。。望大神指点一下啊,上面的问题要怎么解决额。
qt上编译opencv程序出错
编译提取棋盘格角点的的程序,但是一直显示cvFindChessboardCorners这个函数有问题,请大神指教! 错误如下: ``` ../qt_subpix1/main.cpp: In function 'int main(int, char**)': ../qt_subpix1/main.cpp:14:19: warning: deprecated conversion from string constant to 'char*' [-Wwrite-strings] char* filename="1.jpg"; ^ ../qt_subpix1/main.cpp:20:12: warning: unused variable 'c' [-Wunused-variable] char c=getchar(); ^ ../qt_subpix1/main.cpp:37:9: error: 'CV_CALIB_CB_ADAPTIVE_THRESH' was not declared in this scope CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS// check page 382-383. ^ ../qt_subpix1/main.cpp:37:37: error: 'CV_CALIB_CB_FILTER_QUADS' was not declared in this scope CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS// check page 382-383. ^ ../qt_subpix1/main.cpp:38:4: error: 'cvFindChessboardCorners' was not declared in this scope ); ^ ../qt_subpix1/main.cpp:58:4: error: 'cvDrawChessboardCorners' was not declared in this scope ); ^ ../qt_subpix1/main.cpp: At global scope: ../qt_subpix1/main.cpp:11:5: warning: unused parameter 'argc' [-Wunused-parameter] int main(int argc, char** argv) ^ ../qt_subpix1/main.cpp:11:5: warning: unused parameter 'argv' [-Wunused-parameter] make: *** [main.o] Error 1 ``` 程序源码如下: ``` /* hellochess.cpp */ #include <iostream> #include <stdlib.h> #include <cv.h> #include <cxcore.h> #include <highgui.h> #include <math.h> using namespace std; int main(int argc, char** argv) { cout << "Draw Chess OpenCV!"<<endl; char* filename="1.jpg"; IplImage* imgRGB = cvLoadImage(filename); IplImage* imgGrey = cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE); if (imgGrey==NULL){//image validation cout << "No valid image input"<<endl; char c=getchar(); return 1; } //-------find chessboard corners-------------- int corner_row=9;//interior number of row corners.(this can be countered by fingers.) int corner_col=11;//interior number of column corners. int corner_n=corner_row*corner_col; CvSize pattern_size=cvSize(corner_row,corner_col); CvPoint2D32f* corners=new CvPoint2D32f(corner_n); int corner_count; int found=cvFindChessboardCorners(//returning non-zero means sucess. imgGrey,// 8-bit single channel greyscale image. pattern_size,//how many INTERIOR corners in each row and column of the chessboard. corners,//a pointer to an array where the corner locations can be recorded. &corner_count,// optional, if non-NULL, its a point to an integer where the nuber of corners found can be recorded. CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS// check page 382-383. ); //-------------obtain subpixel accuracy on those corners----------------------- int half_win_size=3;//the window size will be 3+1+3=7 int iteration=20; double epislon=0.1; cvFindCornerSubPix( imgGrey, corners, corner_count, cvSize(half_win_size,half_win_size), cvSize(-1,-1),//no ignoring the neighbours of the center corner cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,iteration,epislon) ); //-------Draw the corner pattern------- cvDrawChessboardCorners( imgRGB, pattern_size, corners, corner_count, found ); //to summary a bit of findings. //<<&quot;found=&quot;<<found<<endl; //cout<<&quot;x=&quot;<<corners&#91;2&#93;.x; //cout<<&quot;,y=&quot;<<corners&#91;2&#93;.y<<endl; cvNamedWindow("Find and Draw ChessBoard", CV_WINDOW_AUTOSIZE ); cvShowImage( "Find and Draw ChessBoard", imgRGB ); cvWaitKey(0); cvReleaseImage(&imgGrey); cvReleaseImage(&imgRGB); cvDestroyWindow("Find and Draw ChessBoard"); return 0; } ```
ubantu在IDE中运行caffe-ssd报错, invalid pointer 但是使用终端可以运行
环境:ubantu 16.04 caffe-ssd cuda 8 cudnn 5.1 python 2.7 无论是在终端还是在IDE中都可以make 编译通过,运行时报错。笔者认为是程序的版本问题,所依赖的库不一样。google了多次终于在终端不报错了。原因是没有环境变量, 在在环境变量中添加上一句: export LD_PRELOAD="/usr/lib/libtcmalloc_minimal.so.4" 终端便可以运行 但是使用IDE是还是报一模一样的错误。。。求助!!!!!! 问题错误报告如下: *** Error in `/home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/tools/caffe-d': free(): invalid pointer: 0x00000000012c32c0 *** ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fb47c36a7e5] /lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fb47c37337a] /lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fb47c37753c] /usr/lib/x86_64-linux-gnu/libprotobuf.so.9(_ZN6google8protobuf8internal28DestroyDefaultRepeatedFieldsEv+0x1f)[0x7fb47d6e38af] /usr/lib/x86_64-linux-gnu/libprotobuf.so.9(_ZN6google8protobuf23ShutdownProtobufLibraryEv+0x8b)[0x7fb47d6e2b3b] /usr/lib/x86_64-linux-gnu/libmirprotobuf.so.3(+0x233b9)[0x7fb457b6b3b9] /lib64/ld-linux-x86-64.so.2(+0x10de7)[0x7fb47eeb2de7] /lib/x86_64-linux-gnu/libc.so.6(+0x39ff8)[0x7fb47c32cff8] /lib/x86_64-linux-gnu/libc.so.6(+0x3a045)[0x7fb47c32d045] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf7)[0x7fb47c313837] /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/tools/caffe-d(_start+0x29)[0x435439] ======= Memory map: ======== 00400000-0045b000 r-xp 00000000 08:13 12717978 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/tools/caffe-d 0065a000-0065b000 r--p 0005a000 08:13 12717978 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/tools/caffe-d 0065b000-0065c000 rw-p 0005b000 08:13 12717978 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/tools/caffe-d 012b1000-0149a000 rw-p 00000000 00:00 0 [heap] 7fb44c000000-7fb44c021000 rw-p 00000000 00:00 0 7fb44c021000-7fb450000000 ---p 00000000 00:00 0 7fb453368000-7fb453369000 ---p 00000000 00:00 0 7fb453369000-7fb453b69000 rwxp 00000000 00:00 0 7fb453b69000-7fb453b6c000 r-xp 00000000 08:12 267232 /lib/x86_64-linux-gnu/libkeyutils.so.1.5 7fb453b6c000-7fb453d6b000 ---p 00003000 08:12 267232 /lib/x86_64-linux-gnu/libkeyutils.so.1.5 7fb453d6b000-7fb453d6c000 r--p 00002000 08:12 267232 /lib/x86_64-linux-gnu/libkeyutils.so.1.5 7fb453d6c000-7fb453d6d000 rw-p 00003000 08:12 267232 /lib/x86_64-linux-gnu/libkeyutils.so.1.5 7fb453d6d000-7fb453d77000 r-xp 00000000 08:12 1058069 /usr/lib/x86_64-linux-gnu/libkrb5support.so.0.1 7fb453d77000-7fb453f76000 ---p 0000a000 08:12 1058069 /usr/lib/x86_64-linux-gnu/libkrb5support.so.0.1 7fb453f76000-7fb453f77000 r--p 00009000 08:12 1058069 /usr/lib/x86_64-linux-gnu/libkrb5support.so.0.1 7fb453f77000-7fb453f78000 rw-p 0000a000 08:12 1058069 /usr/lib/x86_64-linux-gnu/libkrb5support.so.0.1 7fb453f78000-7fb453f7b000 r-xp 00000000 08:12 267180 /lib/x86_64-linux-gnu/libcom_err.so.2.1 7fb453f7b000-7fb45417a000 ---p 00003000 08:12 267180 /lib/x86_64-linux-gnu/libcom_err.so.2.1 7fb45417a000-7fb45417b000 r--p 00002000 08:12 267180 /lib/x86_64-linux-gnu/libcom_err.so.2.1 7fb45417b000-7fb45417c000 rw-p 00003000 08:12 267180 /lib/x86_64-linux-gnu/libcom_err.so.2.1 7fb45417c000-7fb4541a8000 r-xp 00000000 08:12 1058059 /usr/lib/x86_64-linux-gnu/libk5crypto.so.3.1 7fb4541a8000-7fb4543a7000 ---p 0002c000 08:12 1058059 /usr/lib/x86_64-linux-gnu/libk5crypto.so.3.1 7fb4543a7000-7fb4543a9000 r--p 0002b000 08:12 1058059 /usr/lib/x86_64-linux86_64-linux-gnu/libgme.so.0.6.0 7fb459f8e000-7fb45a18e000 ---p 0004a000 08:12 1052793 /usr/lib/x86_64-linux-gnu/libgme.so.0.6.0 7fb45a18e000-7fb45a191000 r--p 0004a000 08:12 1052793 /usr/lib/x86_64-linux-gnu/libgme.so.0.6.0 7fb45a191000-7fb45a192000 rw-p 0004d000 08:12 1052793 /usr/lib/x86_64-linux-gnu/libgme.so.0.6.0 7fb45a192000-7fb45a1dc000 r-xp 00000000 08:12 1052795 /usr/lib/x86_64-linux-gnu/libmodplug.so.1.0.0 7fb45a1dc000-7fb45a3dc000 ---p 0004a000 08:12 1052795 /usr/lib/x86_64-linux-gnu/libmodplug.so.1.0.0 7fb45a3dc000-7fb45a3dd000 r--p 0004a000 08:12 1052795 /usr/lib/x86_64-linux-gnu/libmodplug.so.1.0.0 7fb45a3dd000-7fb45a3de000 rw-p 0004b000 08:12 1052795 /usr/lib/x86_64-linux-gnu/libmodplug.so.1.0.0 7fb45a3de000-7fb45a51d000 rw-p 00000000 00:00 0 7fb45a51d000-7fb45a538000 r-xp 00000000 08:12 1058384 /usr/lib/x86_64-linux-gnu/librtmp.so.1 7fb45a538000-7fb45a737000 ---p 0001b000 08:12 1058384 /usr/lib/x86_64-linux-gnu/librtmp.so.1 7fb45a737000-7fb45a738000 r--p 0001a000 08:12 1058384 /usr/lib/x86_64-linux-gnu/librtmp.so.1 7fb45a738000-7fb45a739000 rw-p 0001b000 08:12 1058384 /usr/lib/x86_64-linux-gnu/librtmp.so.1 7fb45a739000-7fb45a781000 r-xp 00000000 08:12 1052797 /usr/lib/x86_64-linux-gnu/libssh-gcrypt.so.4.4.1 7fb45a781000-7fb45a980000 ---p 00048000 08:12 1052797 /usr/lib/x86_64-linux-gnu/libssh-gcrypt.so.4.4.1 7fb45a980000-7fb45a981000 r--p 00047000 08:12 1052797 /usr/lib/x86_64-linux-gnu/libssh-gcrypt.so.4.4.1 7fb45a981000-7fb45a982000 rw-p 00048000 08:12 1052797 /usr/lib/x86_64-linux-gnu/libssh-gcrypt.so.4.4.1 7fb45a982000-7fb45a99c000 r-xp 00000000 08:12 1052753 /usr/lib/x86_64-linux-gnu/libcrystalhd.so.3.6 7fb45a99c000-7fb45ab9b000 ---p 0001a000 08:12 1052753 /usr/lib/x86_64-linux-gnu/libcrystalhd.so.3.6 7fb45ab9b000-7fb45ab9c000 r--p 00019000 08:12 1052753 /usr/lib/x86_64-linux-gnu/libcrystalhd.so.3.6 7fb45ab9c000-7fb45ab9d000 rw-p 0001a000 08:12 1052753 /usr/lib/x86_64-linux-gnu/libcrystalhd.so.3.6 7fb45ab9d000-7fb45abaa000 r-xp 00000000 08:12 1052755 /usr/lib/x86_64-linux-gnu/libgsm.so.1.0.12 7fb45abaa000-7fb45ada9000 ---p 0000d000 08:12 1052755 /usr/lib/x86_64-linux-gnu/libgsm.so.1.0.12 7fb45ada9000-7fb45adaa000 r--p 0000c000 08:12 1052755 /usr/lib/x86_64-linux-gnu/libgsm.so.1.0.12 7fb45adaa000-7fb45adab000 rw-p 0000d000 08:12 1052755 /usr/lib/x86_64-linux-gnu/libgsm.so.1.0.12 7fb45adab000-7fb45adf0000 r-xp 00000000 08:12 1052757 /usr/lib/x86_64-linux-gnu/libmp3lame.so.0.0.0 7fb45adf0000-7fb45aff0000 ---p 00045000 08:12 1052757 /usr/lib/x86_64-linux-gnu/libmp3lame.so.0.0.0 7fb45aff0000-7fb45aff1000 r--p 00045000 08:12 1052757 /usr/lib/x86_64-linux-gnu/libmp3lame.so.0.0.0 7fb45aff1000-7fb45aff2000 rw-p 00046000 08:12 1052757 /usr/lib/x86_64-linux-gnu/libmp3lame.so.0.0.0 7fb45aff2000-7fb45b020000 rw-p 00000000 00:00 0 7fb45b020000-7fb45b042000 r-xp 00000000 08:12 1052760 /usr/lib/x86_64-linux-gnu/libopenjpeg.so.1.5.2 7fb45b042000-7fb45b241000 ---p 00022000 08:12 1052760 /usr/lib/x86_64-linux-gnu/libopenjpeg.so.1.5.2 7fb45b241000-7fb45b242000 r--p 00021000 08:12 1052760 /usr/lib/x86_64-linux-gnu/libopenjpeg.so.1.5.2 7fb45b242000-7fb45b243000 rw-p 00022000 08:12 1052760 /usr/lib/x86_64-linux-gnu/libopenjpeg.so.1.5.2 7fb45b243000-7fb45b28c000 r-xp 00000000 08:12 1058240 /usr/lib/x86_64-linux-gnu/libopus.so.0.5.2 7fb45b28c000-7fb45b48b000 ---p 00049000 08:12 1058240 /usr/lib/x86_64-linux-gnu/libopus.so.0.5.2 7fb45b48b000-7fb45b48c000 r--p 00048000 08:12 1058240 /usr/lib/x86_64-linux-gnu/libopus.so.0.5.2 7fb45b48c000-7fb45b48d000 rw-p 00049000 08:12 1058240 /usr/lib/x86_64-linux-gnu/libopus.so.0.5.2 7fb45b48d000-7fb45b55e000 r-xp 00000000 08:12 1052763 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1058499 /usr/lib/x86_64-linux-gnu/libtheoraenc.so.1.1.2 7fb45bfe1000-7fb45bfff000 r-xp 00000000 08:12 1052775 /usr/lib/x86_64-linux-gnu/libtwolame.so.0.0.0 7fb45bfff000-7fb45c1fe000 ---p 0001e000 08:12 1052775 /usr/lib/x86_64-linux-gnu/libtwolame.so.0.0.0 7fb45c1fe000-7fb45c1ff000 r--p 0001d000 08:12 1052775 /usr/lib/x86_64-linux-gnu/libtwolame.so.0.0.0 7fb45c1ff000-7fb45c200000 rw-p 0001e000 08:12 1052775 /usr/lib/x86_64-linux-gnu/libtwolame.so.0.0.0 7fb45c200000-7fb45c204000 rw-p 00000000 00:00 0 7fb45c204000-7fb45c22e000 r-xp 00000000 08:12 1052328 /usr/lib/x86_64-linux-gnu/libvorbis.so.0.4.8 7fb45c22e000-7fb45c42d000 ---p 0002a000 08:12 1052328 /usr/lib/x86_64-linux-gnu/libvorbis.so.0.4.8 7fb45c42d000-7fb45c42e000 r--p 00029000 08:12 1052328 /usr/lib/x86_64-linux-gnu/libvorbis.so.0.4.8 7fb45c42e000-7fb45c42f000 rw-p 0002a000 08:12 1052328 /usr/lib/x86_64-linux-gnu/libvorbis.so.0.4.8 7fb45c42f000-7fb45c4bc000 r-xp 00000000 08:12 1052326 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---p 000a4000 08:12 1057741 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1 7fb45fc98000-7fb45fc9e000 r--p 000a3000 08:12 1057741 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1 7fb45fc9e000-7fb45fc9f000 rw-p 000a9000 08:12 1057741 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1 7fb45fc9f000-7fb45fd3e000 r-xp 00000000 08:12 1058292 /usr/lib/x86_64-linux-gnu/libpixman-1.so.0.33.6 7fb45fd3e000-7fb45ff3e000 ---p 0009f000 08:12 1058292 /usr/lib/x86_64-linux-gnu/libpixman-1.so.0.33.6 7fb45ff3e000-7fb45ff46000 r--p 0009f000 08:12 1058292 /usr/lib/x86_64-linux-gnu/libpixman-1.so.0.33.6 7fb45ff46000-7fb45ff47000 rw-p 000a7000 08:12 1058292 /usr/lib/x86_64-linux-gnu/libpixman-1.so.0.33.6 7fb45ff47000-7fb45ff58000 r-xp 00000000 08:12 1057316 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0 7fb45ff58000-7fb460157000 ---p 00011000 08:12 1057316 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0 7fb460157000-7fb460158000 r--p 00010000 08:12 1057316 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0 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7fb47de03000-7fb47de06000 r-xp 00000000 08:12 1057455 /usr/lib/x86_64-linux-gnu/libboost_system.so.1.58.0 7fb47de06000-7fb47e005000 ---p 00003000 08:12 1057455 /usr/lib/x86_64-linux-gnu/libboost_system.so.1.58.0 7fb47e005000-7fb47e006000 r--p 00002000 08:12 1057455 /usr/lib/x86_64-linux-gnu/libboost_system.so.1.58.0 7fb47e006000-7fb47e007000 rw-p 00003000 08:12 1057455 /usr/lib/x86_64-linux-gnu/libboost_system.so.1.58.0 7fb47e007000-7fb47ec5d000 r-xp 00000000 08:13 12717974 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/lib/libcaffe-d.so.1.0.0-rc3 7fb47ec5d000-7fb47ee5d000 ---p 00c56000 08:13 12717974 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/lib/libcaffe-d.so.1.0.0-rc3 7fb47ee5d000-7fb47ee76000 r--p 00c56000 08:13 12717974 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/lib/libcaffe-d.so.1.0.0-rc3 7fb47ee76000-7fb47eea0000 rw-p 00c6f000 08:13 12717974 /home/zsy/DeepLearning/caffe-ssd/cmake-build-debug/lib/libcaffe-d.so.1.0.0-rc3 7fb47eea0000-7fb47eea2000 rw-p 00000000 00:00 0 7fb47eea2000-7fb47eec8000 r-xp 00000000 08:12 273854 /lib/x86_64-linux-gnu/ld-2.23.so 7fb47efa9000-7fb47efc4000 rw-p 00000000 00:00 0 7fb47efc4000-7fb47f044000 r-xp 00000000 08:12 267144 /lib/x86_64-linux-gnu/libsystemd.so.0.14.0 7fb47f044000-7fb47f047000 r--p 0007f000 08:12 267144 /lib/x86_64-linux-gnu/libsystemd.so.0.14.0 7fb47f047000-7fb47f048000 rw-p 00082000 08:12 267144 /lib/x86_64-linux-gnu/libsystemd.so.0.14.0 7fb47f048000-7fb47f04f000 rw-p 00000000 00:00 0 7fb47f04f000-7fb47f06d000 r-xp 00000000 08:12 266771 /lib/x86_64-linux-gnu/libudev.so.1.6.4 7fb47f06d000-7fb47f06e000 r--p 0001d000 08:12 266771 /lib/x86_64-linux-gnu/libudev.so.1.6.4 7fb47f06e000-7fb47f06f000 rw-p 0001e000 08:12 266771 /lib/x86_64-linux-gnu/libudev.so.1.6.4 7fb47f06f000-7fb47f0a2000 rw-p 00000000 00:00 0 7fb47f0c5000-7fb47f0c7000 rw-p 00000000 00:00 0 7fb47f0c7000-7fb47f0c8000 r--p 00025000 08:12 273854 /lib/x86_64-linux-gnu/ld-2.23.so 7fb47f0c8000-7fb47f0c9000 rw-p 00026000 08:12 273854 /lib/x86_64-linux-gnu/ld-2.23.so 7fb47f0c9000-7fb47f0ca000 rw-p 00000000 00:00 0 7ffd3aaac000-7ffd3aacb000 rwxp 00000000 00:00 0 [stack] 7ffd3aacb000-7ffd3aacd000 rw-p 00000000 00:00 0 7ffd3abb3000-7ffd3abb5000 r--p 00000000 00:00 0 [vvar] 7ffd3abb5000-7ffd3abb7000 r-xp 00000000 00:00 0 [vdso] ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall] *** Aborted at 1537413282 (unix time) try "date -d @1537413282" if you are using GNU date *** PC: @ 0x7fb47c328428 gsignal *** SIGABRT (@0x3e800007a97) received by PID 31383 (TID 0x7fb47efb1b00) from PID 31383; stack trace: *** @ 0x7fb47c3284b0 (unknown) @ 0x7fb47c328428 gsignal @ 0x7fb47c32a02a abort @ 0x7fb47c36a7ea (unknown) @ 0x7fb47c37337a (unknown) @ 0x7fb47c37753c cfree @ 0x7fb47d6e38af google::protobuf::internal::DestroyDefaultRepeatedFields() @ 0x7fb47d6e2b3b google::protobuf::ShutdownProtobufLibrary() @ 0x7fb457b6b3b9 (unknown) @ 0x7fb47eeb2de7 (unknown) @ 0x7fb47c32cff8 (unknown) @ 0x7fb47c32d045 exit @ 0x7fb47c313837 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