我正在尝试使用 easyocr 对单个 GPU 进行推理。 我试过运行以下代码：

Question

def EasyOcrTextbatch(self):
   batchsize=16
   reader = easyocr.Reader(['en'],cudnn_benchmark=True)
   # reader = easyocr.Reader(['en'],gpu=False)
   # dummy = np.zeros([8,512,384,3], dtype=np.uint8)
   # paragraph=reader.readtext_batched(dummy)
   paragraph=reader.readtext_batched(self.imglist,batch_size=batchsize)
   #paragraph = reader.readtext(self.imglist, batch_size=batchsize, paragraph=True, 
   detail=0)
   del reader
   gc.collect()
   torch.cuda.empty_cache()
   return paragraph

The above code does not speed up and to my surprise running it sequentially was faster.Below code is faster than the above one.

def EasyOcrTextSequence(self,):

    reader = easyocr.Reader(['en'])
    #reader = easyocr.Reader(['en'],cudnn_benchmark=False)
    # dummy = np.zeros([32, 256, 256, 1], dtype=np.uint8)
    k=[cv2.cvtColor(cv2.imread(i), cv2.COLOR_BGR2GRAY) for i in self.imglist]
    self.arr = np.array(k)
    j=[reader.readtext(i,paragraph=True,detail=0,batch_size=16) for i in self.arr]
    del reader
    return j

The average time for single images comes at.34 seconds and I want to reduce it a lot.Things I have tried:

1. Passing batch size while calling as shown in above code.
2. Tried passing Workers=1 and to my surprise the time taken doubles approx. using workers.
3. Have tried passing it sequentially and this is the fastest.
4. I have even tried vstacking 3 images into one and still no luck.
5. Size of my images are (512,384)

Please help me out if you have any suggestion for using easyocr in inference so that the latency should be lowest (Need to process as many images possible within a second).Also I am open for trying different open-source ocr,my only constraint is it should be very fast with good accuracy.

I am really stuck at this point.Please help.

Answer 1

List comprehensions are normally a good performance increaser. They can implemented like this

def EasyOcrTextSequence(self,):
   reader = easyocr.Reader(['en'])
   images = (cv2.imread(img) for img in self.imglist)
   images_grayscaled = (cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in images)
   self.arr = np.array(images_grayscaled)
   return (reader.readtext(i,paragraph=True,detail=0,batch_size=16) for i in self.arr)