def predict(self, image):
image = resizeNormalize(image, 32)
image = image.astype(np.float32)
image = np.array([[image]])
self.model.setInput(image)
preds = self.model.forward()
preds = preds.transpose(0, 2, 3, 1)
preds = preds[0]
preds = np.argmax(preds, axis=2).reshape((-1, ))
raw = strLabelConverter(preds, self.alphabet)
return raw
def predict(self,image):
image = resizeNormalize(image,32)
image = image.astype(np.float32)
image = np.array([[image]])
global graph
with graph.as_default():
preds = self.model.predict(image)
#preds = preds[0]
preds = np.argmax(preds,axis=2).reshape((-1,))
raw = strLabelConverter(preds,self.alphabet)
return raw
def predict(self, image):
image = resizeNormalize(image, 32)
image = image.astype(np.float32)
image = torch.from_numpy(image)
if torch.cuda.is_available() and self.GPU:
image = image.cuda()
else:
image = image.cpu()
image = image.view(1, 1, *image.size())
image = Variable(image)
preds = self(image)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
raw = strLabelConverter(preds, self.alphabet)
return raw
def predict_batch(self, boxes, batch_size=1):
"""
predict on batch
"""
N = len(boxes)
res = []
imgW = 0
batch = N // batch_size
if batch * batch_size != N:
batch += 1
for i in range(batch):
tmpBoxes = boxes[i * batch_size:(i + 1) * batch_size]
imageBatch = []
imgW = 0
for box in tmpBoxes:
img = box['img']
image = resizeNormalize(img, 32)
h, w = image.shape[:2]
imgW = max(imgW, w)
imageBatch.append(np.array([image]))
imageArray = np.zeros((len(imageBatch), 1, 32, imgW),
dtype=np.float32)
n = len(imageArray)
for j in range(n):
_, h, w = imageBatch[j].shape
imageArray[j][:, :, :w] = imageBatch[j]
image = torch.from_numpy(imageArray)
image = Variable(image)
if torch.cuda.is_available() and self.GPU:
image = image.cuda()
else:
image = image.cpu()
preds = self(image)
preds = preds.argmax(2)
n = preds.shape[1]
for j in range(n):
res.append(strLabelConverter(preds[:, j], self.alphabet))
for i in range(N):
boxes[i]['text'] = res[i]
return boxes
def predict_batch(self,boxes,batch_size=1):
"""
predict on batch
"""
N = len(boxes)
res = []
imgW = 0
batch = N//batch_size
if batch*batch_size!=N:
batch+=1
for i in range(batch):
tmpBoxes = boxes[i*batch_size:(i+1)*batch_size]
imageBatch =[]
imgW = 0
for box in tmpBoxes:
img = box['img']
image = resizeNormalize(img,32)
h,w = image.shape[:2]
imgW = max(imgW,w)
imageBatch.append(np.array([image]))
imageArray = np.zeros((len(imageBatch),1,32,imgW),dtype=np.float32)
n = len(imageArray)
for j in range(n):
_,h,w = imageBatch[j].shape
imageArray[j][:,:,:w] = imageBatch[j]
global graph
with graph.as_default():
preds = self.model.predict(imageArray,batch_size=batch_size)
preds = preds.argmax(axis=2)
n = preds.shape[0]
for j in range(n):
res.append(strLabelConverter(preds[j,].tolist(),self.alphabet))
for i in range(N):
boxes[i]['text'] = res[i]
return boxes
# ---------------Params---------------
IMAGE_PATH = "./demo_images/test.jpg"
CRNN_API_URL = "http://text_recognition:8501/v1/models/crnn:predict"
# ---------------Alphabet---------------
alphabet = alphabetChinese
nclass = len(alphabet) + 1
# ---------------Process image---------------
image = cv2.imread(IMAGE_PATH)
image = Image.fromarray(image)
image = image.convert('L')
image = resizeNormalize(image, 32)
image = image.astype(np.float32)
image = np.array([image])
# ---------------Build post---------------
post_json = {"instances": [{"input_image": image.tolist()}]}
# ---------------Test---------------
t0 = time.time()
response = requests.post(CRNN_API_URL, data=json.dumps(post_json))
print("forward time : {}".format(time.time() - t0))
response.raise_for_status()
prediction = response.json()["predictions"]
print(prediction)
