在Windows 10上的Python中使用OpenCV尝试在某些图像上使用EAST text detector时,出现以下错误:
cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\dnn\src\dnn.cpp:835: error: (-215:Assertion failed) ld.inputBlobs[0]->total() == total(shapes[index]) in function 'cv::dnn::dnn4_v20180917::BlobManager::allocateBlobsForLayer'
(奇怪的是,该路径在我的文件系统上不存在)
我从出色的tutorial到Adrian Rosebrock开始。这是一个代码片段(在最后一行失败):
# sFileName is the path to the image, previously set
oInputImage = cv.imread(sFileName)
aiShape = oInputImage.shape
(iH, iW) = aiShape[:2]
iRequiredUnit = 32
# check if the image height is enough
iHr = iH % iRequiredUnit
iBottom = 0
iHr = iH % iRequiredUnit
if 0 < iHr:
# calculate how much padding is necessary
iBottom = iRequiredUnit - iHr
# check if the image width is enough
iRight = 0
iWr = iW % iRequiredUnit
if 0 < iWr:
# calculate how much padding is necessary
iRight = iRequiredUnit - iWr
if iBottom > 0 or iRight > 0:
# add padding to make the image proportions correct
oImage = cv.copyMakeBorder(
src=oInputImage,
top=iTop,
bottom=iBottom,
left=iLeft,
right=iRight,
borderType=cv.BORDER_CONSTANT,
value=[0, 0, 0]
)
else:
# no need to add padding
oImage = oInputImage.copy()
(iH, iW) = oImage.shape[:2])
ib, ig, ir, _ = cv.mean(oImage)
oBlob = cv.dnn.blobFromImage(
oImage, 1.0, (iW, iH), (ib, ig, ir),
swapRB=True, crop=False
)
# load the EAST network
# EAST_path initialized appropriately previously
oNet = cv.dnn.readNet(EAST_path)
oNet.setInput(oBlob)
asLayerNames = [
"feature_fusion/Conv_7/Sigmoid",
"feature_fusion/concat_3"]
(afScores, aoGeometry) = oNet.forward(asLayerNames)
我做了一些修改,例如我重新计算了平均值,而不是使用示例中显示的硬编码值。我还尝试了无意地(或示例中的默认值)和swapRB=False
来调用blobFromImage
,但是错误不断发生。
问题是某些文件(here's an example)有系统地发生,而其他文件则没有,EAST可以平稳地运行。我无法确定造成图像麻烦的特征,但是我倾向于认为该错误与调整图像大小无关,因为必须能够调整没有问题的大多数图像。
我还没有找到有关此问题的任何文档,也无法从源头(我想是this)轻易地重建问题。
如何防止该错误?
答案 0 :(得分:1)
我在另一个应用程序中遇到了类似的问题。这与输入大小有关。我使用了到目前为止尚未失败的以下解决方法:
orig_height, orig_width = image.shape[:2]
while True:
height, width = image.shape[:2]
blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(width, height),mean=(104.00698793, 116.66876762, 122.67891434),swapRB=False, crop=False)
self.net.setInput(blob)
try:
prediction = self.net.forward()
break
except:
pass
if width*height < 100:
raise
image = cv2.resize(image, (int(width*0.9), int(height*0.9)))
prediction = cv2.resize(prediction[0, 0], (orig_width, orig_height))