无法使用 GPU（暗网）执行对象检测
Could not perform object detection using GPU (darknet)
我刚刚设置了对象检测规范。这是我构建的：
- 来自源代码的 OpenCV (OpenCV tutorial)
- 建立暗网（Build Darknet）
我的笔记本电脑的规格是：
- Windows 10 专业版
- RTX 3060Ti（CUDA 11.1、CUDNN 8.6）
- Python 3.7.9
编译后，我根据教程测试了darknet，它检测到了我的GPU，图片如下：
Success, using darknet.
但是，由于执行暗网检测需要很多参数，我构建了另一个 python 程序，可以让我轻松地 运行 该程序。
所以问题来了，当我运行“from_video.py”时，执行的检测是在CPU。文件排列如下New folder。让我们拿一个 python 程序，“from_video.py” 进行测试，“main.py” 将加载模型，检查 GPU。 python 个文件的代码如下。
from_video.py
import cv2
from main import *
videoPath = "./data/test_sampah1.mp4"
darknetmain = darknet_main()
darknetmain.setGPU(is_GPU=True)
video = cv2.VideoCapture(videoPath)
width = int(video.get(3))
height = int(video.get(4))
size = (width,height)
# result = cv2.VideoWriter('sampah1_output.mpeg',cv2.VideoWriter_fourcc(*'MPJG'),20,size)
if video.isOpened():
while(True):
res, cv_img = video.read()
if res==False:
break
imcaptions, boundingBoxs = darknetmain.performDetect(cv_img)
if len(imcaptions)>0:
if len(imcaptions) > 0:
for i in range(len(imcaptions)):
name = imcaptions[i]
name = name[:5]
print(name + " is found")
cv_img = cv2.rectangle(cv_img, boundingBoxs[i][0], boundingBoxs[i][2], (0, 255, 0), 2)
cv_img = cv2.putText(cv_img, imcaptions[i], boundingBoxs[i][0], cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255))
cv2.imshow("result", cv_img)
#result.write(cv_img)
cv2.waitKey(1)
else:
print("no result")
#result.release()
video.release()
else:
print("Cannot read the video file")
main.py
from ctypes import *
import math
import random
import os
import numpy as np
import cv2
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),
("uc", POINTER(c_float)),
("points", c_int),
("embeddings", POINTER(c_float)),
("embedding_size", c_int),
("sim", c_float),
("track_id", 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))]
class darknet_main():
def __init__(self):
self.netMain = None
self.metaMain = None
self.altNames = None
self.thresh = 0.5
self.configPath = './model/yolov3_custom_fyp_testing.cfg'
self.weightPath = './model/yolov3_custom_fyp_last.weights'
self.metaPath = "./model/obj_fyp.data"
self.frame = None
def setGPU(self, is_GPU):
self.hasGPU = is_GPU
if self.hasGPU:
self.lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL)
else:
self.lib = CDLL("yolo_cpp_dll_nogpu.dll", RTLD_GLOBAL)
self.lib.network_width.argtypes = [c_void_p]
self.lib.network_width.restype = c_int
self.lib.network_height.argtypes = [c_void_p]
self.lib.network_height.restype = c_int
self.predict = self.lib.network_predict
self.predict.argtypes = [c_void_p, POINTER(c_float)]
self.predict.restype = POINTER(c_float)
if self.hasGPU:
self.set_gpu = self.lib.cuda_set_device
self.set_gpu.argtypes = [c_int]
self.make_image = self.lib.make_image
self.make_image.argtypes = [c_int, c_int, c_int]
self.make_image.restype = IMAGE
self.get_network_boxes = self.lib.get_network_boxes
self.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]
self.get_network_boxes.restype = POINTER(DETECTION)
self.make_network_boxes = self.lib.make_network_boxes
self.make_network_boxes.argtypes = [c_void_p]
self.make_network_boxes.restype = POINTER(DETECTION)
self.free_detections = self.lib.free_detections
self.free_detections.argtypes = [POINTER(DETECTION), c_int]
self.free_ptrs = self.lib.free_ptrs
self.free_ptrs.argtypes = [POINTER(c_void_p), c_int]
self.network_predict = self.lib.network_predict
self.network_predict.argtypes = [c_void_p, POINTER(c_float)]
self.reset_rnn = self.lib.reset_rnn
self.reset_rnn.argtypes = [c_void_p]
self.load_net = self.lib.load_network
self.load_net.argtypes = [c_char_p, c_char_p, c_int]
self.load_net.restype = c_void_p
self.load_net_custom = self.lib.load_network_custom
self.load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
self.load_net_custom.restype = c_void_p
self.do_nms_obj = self.lib.do_nms_obj
self.do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
self.do_nms_sort = self.lib.do_nms_sort
self.do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
self.free_image = self.lib.free_image
self.free_image.argtypes = [IMAGE]
self.letterbox_image = self.lib.letterbox_image
self.letterbox_image.argtypes = [IMAGE, c_int, c_int]
self.letterbox_image.restype = IMAGE
self.load_meta = self.lib.get_metadata
self.lib.get_metadata.argtypes = [c_char_p]
self.lib.get_metadata.restype = METADATA
self.load_image = self.lib.load_image_color
self.load_image.argtypes = [c_char_p, c_int, c_int]
self.load_image.restype = IMAGE
self.rgbgr_image = self.lib.rgbgr_image
self.rgbgr_image.argtypes = [IMAGE]
self.predict_image = self.lib.network_predict_image
self.predict_image.argtypes = [c_void_p, IMAGE]
self.predict_image.restype = POINTER(c_float)
self.netMain = self.load_net_custom(self.configPath.encode("ascii"), self.weightPath.encode("ascii"), 0,
1) # batch size = 1
self.metaMain = self.load_meta(self.metaPath.encode("ascii"))
try:
with open(self.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:
self.namesList = namesFH.read().strip().split("\n")
self.altNames = [x.strip() for x in self.namesList]
except TypeError:
pass
except Exception:
pass
def sample(self, 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(self, ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr
def array_to_image(self, 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(self, net, meta, im):
out = self.predict_image(net, im)
res = []
for i in range(meta.classes):
if self.altNames is None:
nameTag = meta.names[i]
else:
nameTag = self.altNames[i]
res.append((nameTag, out[i]))
res = sorted(res, key=lambda x: -x[1])
return res
def detect(self, net, meta, cv_im, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
# im = self.load_image(image, 0, 0)
# debug=True
custom_image = cv2.cvtColor(cv_im, cv2.COLOR_BGR2RGB)
h, w, c_ = custom_image.shape
custom_image = cv2.resize(custom_image,(self.lib.network_width(net), self.lib.network_height(net)), interpolation = cv2.INTER_LINEAR)
im, arr = self.array_to_image(custom_image) # you should comment line below: free_image(im)
if debug: print("Loaded image")
num = c_int(0)
if debug: print("Assigned num")
pnum = pointer(num)
if debug: print("Assigned pnum")
self.predict_image(net, im)
if debug: print("did prediction")
dets = self.get_network_boxes(net, w, h, self.thresh, hier_thresh, None, 0, pnum, 0) # OpenCV
# dets = self.get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0)
if debug: print("Got dets")
num = pnum[0]
if debug: print("got zeroth index of pnum")
if nms:
self.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 self.altNames is None:
nameTag = self.meta.names[i]
else:
nameTag = self.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")
self.free_detections(dets, num)
if debug: print("freed detections")
return res
def performDetect(self, cv_img):
# Do the detection
detections = self.detect(self.netMain, self.metaMain, cv_img, self.thresh)
imcaptions = []
boundingBoxs=[]
for detection in detections:
label = detection[0]
confidence = detection[1]
pstring = label+": "+str(np.rint(100 * confidence))+"%"
imcaptions.append(pstring)
print(pstring)
bounds = detection[2]
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)
]
boundingBoxs.append(boundingBox)
# cv_img = cv2.rectangle(cv_img, boundingBox[0], boundingBox[2], (0,0, 255), 1)
#
# cv2.imshow("image", cv_img)
# cv2.waitKey(0)
return imcaptions, boundingBoxs
if __name__ == "__main__":
darknetmain = darknet_main()
darknetmain.setGPU(is_GPU=True)
imagePath = "./data/bottle_8.png"
cv_img = cv2.imread(imagePath)
darknetmain.performDetect(cv_img)
这似乎有点复杂，当您尝试从源代码构建 openCV 时，请确保您的 CUDA arch bin 支持您的版本或更高版本。在我的示例中，8.0 和 8.6。然后，当您尝试从源代码编译 darknet 时，请确保您的 compute_,sm_ 对于 8.0 和 8.6
我刚刚设置了对象检测规范。这是我构建的：
- 来自源代码的 OpenCV (OpenCV tutorial)
- 建立暗网（Build Darknet）
我的笔记本电脑的规格是：
- Windows 10 专业版
- RTX 3060Ti（CUDA 11.1、CUDNN 8.6）
- Python 3.7.9
编译后，我根据教程测试了darknet，它检测到了我的GPU，图片如下： Success, using darknet.
但是，由于执行暗网检测需要很多参数，我构建了另一个 python 程序，可以让我轻松地 运行 该程序。
所以问题来了，当我运行“from_video.py”时，执行的检测是在CPU。文件排列如下New folder。让我们拿一个 python 程序，“from_video.py” 进行测试，“main.py” 将加载模型，检查 GPU。 python 个文件的代码如下。
from_video.py
import cv2
from main import *
videoPath = "./data/test_sampah1.mp4"
darknetmain = darknet_main()
darknetmain.setGPU(is_GPU=True)
video = cv2.VideoCapture(videoPath)
width = int(video.get(3))
height = int(video.get(4))
size = (width,height)
# result = cv2.VideoWriter('sampah1_output.mpeg',cv2.VideoWriter_fourcc(*'MPJG'),20,size)
if video.isOpened():
while(True):
res, cv_img = video.read()
if res==False:
break
imcaptions, boundingBoxs = darknetmain.performDetect(cv_img)
if len(imcaptions)>0:
if len(imcaptions) > 0:
for i in range(len(imcaptions)):
name = imcaptions[i]
name = name[:5]
print(name + " is found")
cv_img = cv2.rectangle(cv_img, boundingBoxs[i][0], boundingBoxs[i][2], (0, 255, 0), 2)
cv_img = cv2.putText(cv_img, imcaptions[i], boundingBoxs[i][0], cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255))
cv2.imshow("result", cv_img)
#result.write(cv_img)
cv2.waitKey(1)
else:
print("no result")
#result.release()
video.release()
else:
print("Cannot read the video file")
main.py
from ctypes import *
import math
import random
import os
import numpy as np
import cv2
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),
("uc", POINTER(c_float)),
("points", c_int),
("embeddings", POINTER(c_float)),
("embedding_size", c_int),
("sim", c_float),
("track_id", 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))]
class darknet_main():
def __init__(self):
self.netMain = None
self.metaMain = None
self.altNames = None
self.thresh = 0.5
self.configPath = './model/yolov3_custom_fyp_testing.cfg'
self.weightPath = './model/yolov3_custom_fyp_last.weights'
self.metaPath = "./model/obj_fyp.data"
self.frame = None
def setGPU(self, is_GPU):
self.hasGPU = is_GPU
if self.hasGPU:
self.lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL)
else:
self.lib = CDLL("yolo_cpp_dll_nogpu.dll", RTLD_GLOBAL)
self.lib.network_width.argtypes = [c_void_p]
self.lib.network_width.restype = c_int
self.lib.network_height.argtypes = [c_void_p]
self.lib.network_height.restype = c_int
self.predict = self.lib.network_predict
self.predict.argtypes = [c_void_p, POINTER(c_float)]
self.predict.restype = POINTER(c_float)
if self.hasGPU:
self.set_gpu = self.lib.cuda_set_device
self.set_gpu.argtypes = [c_int]
self.make_image = self.lib.make_image
self.make_image.argtypes = [c_int, c_int, c_int]
self.make_image.restype = IMAGE
self.get_network_boxes = self.lib.get_network_boxes
self.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]
self.get_network_boxes.restype = POINTER(DETECTION)
self.make_network_boxes = self.lib.make_network_boxes
self.make_network_boxes.argtypes = [c_void_p]
self.make_network_boxes.restype = POINTER(DETECTION)
self.free_detections = self.lib.free_detections
self.free_detections.argtypes = [POINTER(DETECTION), c_int]
self.free_ptrs = self.lib.free_ptrs
self.free_ptrs.argtypes = [POINTER(c_void_p), c_int]
self.network_predict = self.lib.network_predict
self.network_predict.argtypes = [c_void_p, POINTER(c_float)]
self.reset_rnn = self.lib.reset_rnn
self.reset_rnn.argtypes = [c_void_p]
self.load_net = self.lib.load_network
self.load_net.argtypes = [c_char_p, c_char_p, c_int]
self.load_net.restype = c_void_p
self.load_net_custom = self.lib.load_network_custom
self.load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
self.load_net_custom.restype = c_void_p
self.do_nms_obj = self.lib.do_nms_obj
self.do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
self.do_nms_sort = self.lib.do_nms_sort
self.do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
self.free_image = self.lib.free_image
self.free_image.argtypes = [IMAGE]
self.letterbox_image = self.lib.letterbox_image
self.letterbox_image.argtypes = [IMAGE, c_int, c_int]
self.letterbox_image.restype = IMAGE
self.load_meta = self.lib.get_metadata
self.lib.get_metadata.argtypes = [c_char_p]
self.lib.get_metadata.restype = METADATA
self.load_image = self.lib.load_image_color
self.load_image.argtypes = [c_char_p, c_int, c_int]
self.load_image.restype = IMAGE
self.rgbgr_image = self.lib.rgbgr_image
self.rgbgr_image.argtypes = [IMAGE]
self.predict_image = self.lib.network_predict_image
self.predict_image.argtypes = [c_void_p, IMAGE]
self.predict_image.restype = POINTER(c_float)
self.netMain = self.load_net_custom(self.configPath.encode("ascii"), self.weightPath.encode("ascii"), 0,
1) # batch size = 1
self.metaMain = self.load_meta(self.metaPath.encode("ascii"))
try:
with open(self.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:
self.namesList = namesFH.read().strip().split("\n")
self.altNames = [x.strip() for x in self.namesList]
except TypeError:
pass
except Exception:
pass
def sample(self, 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(self, ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr
def array_to_image(self, 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(self, net, meta, im):
out = self.predict_image(net, im)
res = []
for i in range(meta.classes):
if self.altNames is None:
nameTag = meta.names[i]
else:
nameTag = self.altNames[i]
res.append((nameTag, out[i]))
res = sorted(res, key=lambda x: -x[1])
return res
def detect(self, net, meta, cv_im, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
# im = self.load_image(image, 0, 0)
# debug=True
custom_image = cv2.cvtColor(cv_im, cv2.COLOR_BGR2RGB)
h, w, c_ = custom_image.shape
custom_image = cv2.resize(custom_image,(self.lib.network_width(net), self.lib.network_height(net)), interpolation = cv2.INTER_LINEAR)
im, arr = self.array_to_image(custom_image) # you should comment line below: free_image(im)
if debug: print("Loaded image")
num = c_int(0)
if debug: print("Assigned num")
pnum = pointer(num)
if debug: print("Assigned pnum")
self.predict_image(net, im)
if debug: print("did prediction")
dets = self.get_network_boxes(net, w, h, self.thresh, hier_thresh, None, 0, pnum, 0) # OpenCV
# dets = self.get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0)
if debug: print("Got dets")
num = pnum[0]
if debug: print("got zeroth index of pnum")
if nms:
self.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 self.altNames is None:
nameTag = self.meta.names[i]
else:
nameTag = self.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")
self.free_detections(dets, num)
if debug: print("freed detections")
return res
def performDetect(self, cv_img):
# Do the detection
detections = self.detect(self.netMain, self.metaMain, cv_img, self.thresh)
imcaptions = []
boundingBoxs=[]
for detection in detections:
label = detection[0]
confidence = detection[1]
pstring = label+": "+str(np.rint(100 * confidence))+"%"
imcaptions.append(pstring)
print(pstring)
bounds = detection[2]
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)
]
boundingBoxs.append(boundingBox)
# cv_img = cv2.rectangle(cv_img, boundingBox[0], boundingBox[2], (0,0, 255), 1)
#
# cv2.imshow("image", cv_img)
# cv2.waitKey(0)
return imcaptions, boundingBoxs
if __name__ == "__main__":
darknetmain = darknet_main()
darknetmain.setGPU(is_GPU=True)
imagePath = "./data/bottle_8.png"
cv_img = cv2.imread(imagePath)
darknetmain.performDetect(cv_img)
这似乎有点复杂，当您尝试从源代码构建 openCV 时，请确保您的 CUDA arch bin 支持您的版本或更高版本。在我的示例中，8.0 和 8.6。然后，当您尝试从源代码编译 darknet 时，请确保您的 compute_,sm_ 对于 8.0 和 8.6