def predict(filename):
channel = grpc.insecure_channel("localhost:8500")
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
# model_name
request.model_spec.name = "cropper_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
start = time.time()
"""
=========================================
===== Crop and align id card image
=========================================
"""
filepath = app.config["IMAGE_UPLOADS"] + "/" + filename
# preprocess image
img, original_image, original_width, original_height = preprocess_image(
filepath, Cropper.TARGET_SIZE)
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# request to cropper model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 9))
except Exception as e:
print(e)
cropper = Cropper()
cropper.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
# respone to client if image is invalid
if not cropper.respone_client(threshold_idcard=0.8):
return render_template('upload_image_again.html')
cropper.set_image(original_image=original_image)
# output of cropper part
aligned_image = getattr(cropper, "image_output")
cv2.imwrite('app/static/aligned_images/' + filename, aligned_image)
aligned_image = cv2.cvtColor(aligned_image, cv2.COLOR_BGR2RGB)
"""
===========================================
==== Detect informations in aligned image
===========================================
"""
# preprocess aligned image
original_height, original_width, _ = aligned_image.shape
img = cv2.resize(aligned_image, Detector.TARGET_SIZE)
img = np.float32(img / 255.)
# model_name
request.model_spec.name = "detector_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# new request to detector model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 13))
except Exception as e:
print(e)
detector = Detector()
detector.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
detector.set_info_images(original_image=aligned_image)
# output of detector part
info_images = getattr(detector, "info_images")
"""
=====================================
==== Reader infors from infors image
=====================================
"""
keys = list(info_images.keys())
keys.remove("thoi_han")
keys.remove("chan_dung")
infors = dict()
# init default value of quoc_tich, dan_toc
infors['quoc_tich'] = ""
infors['dan_toc'] = ""
if "quoc_tich" in keys:
infors['quoc_tich'] = ["Việt Nam"]
keys.remove("quoc_tich")
if "sex" in keys:
info_image = info_images["sex"]
infors["sex"] = list()
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
if "Na" in s:
infors["sex"].append("Nam")
else:
infors["sex"].append("Nữ")
keys.remove("sex")
if "dan_toc" in keys:
info_image = info_images["dan_toc"]
infors["dan_toc"] = list()
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
s = s.split(" ")[-1]
infors["dan_toc"].append(s)
keys.remove("dan_toc")
for key in keys:
infors[key] = list()
info_image = info_images[key]
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
infors[key].append(s)
que_quan_0 = infors['que_quan'][0]
que_quan_1 = ''
noi_thuong_tru_0 = infors['noi_thuong_tru'][0]
noi_thuong_tru_1 = ''
if len(infors['que_quan']) == 2:
que_quan_1 = infors['que_quan'][1]
if len(infors['noi_thuong_tru']) == 2:
noi_thuong_tru_1 = infors['noi_thuong_tru'][1]
print("total_time:{}".format(time.time() - start))
return render_template('predict.html',
id=infors['id'][0].replace(" ", ""),
full_name=infors['full_name'][0],
date_of_birth=infors['date_of_birth'][0],
sex=infors['sex'][0],
quoc_tich=infors['quoc_tich'],
dan_toc=infors['dan_toc'],
que_quan_0=que_quan_0,
que_quan_1=que_quan_1,
noi_thuong_tru_0=noi_thuong_tru_0,
noi_thuong_tru_1=noi_thuong_tru_1,
filename=str(filename))
def predictcmnd(self, image):
# check cccd
"""
=========================================
===== Crop and align id card image
=========================================
"""
request = predict_pb2.PredictRequest()
# model_name
request.model_spec.name = "cropper_cmnd_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
# preprocess image
img, original_image, original_width, original_height = preprocess_image(
image, Cropper.TARGET_SIZE)
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# request to cropper model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = self.stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 9))
except Exception as e:
print("Cropper cmnd = ", e)
cropper = Cropper()
cropper.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
# respone to client if image is invalid
if cropper.respone_client(threshold_idcard=0.8) == -1:
# print(cropper.respone_client(threshold_idcard=0.8))
return
elif cropper.respone_client(threshold_idcard=0.8) == 0:
# print("no cropper")
# cv2.imwrite('app/static/aligned_images/' + filename, original_image)
aligned_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
else:
# print("cropper image")
cropper.set_image(original_image=original_image)
# print("Cropper cmnd end")
# output of cropper part
aligned_image = getattr(cropper, "image_output")
cv2.imwrite('storage/c.jpg', aligned_image)
aligned_image = cv2.cvtColor(aligned_image, cv2.COLOR_BGR2RGB)
"""
===========================================
==== Detect informations in aligned image
===========================================
"""
# preprocess aligned image
original_height, original_width, _ = aligned_image.shape
img = cv2.resize(aligned_image, Detector.TARGET_SIZE)
img = np.float32(img / 255.)
# model_name
request.model_spec.name = "detector_cmnd_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# new request to detector model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
# print("Detect cmnd = ok")
result = self.stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 13))
# print("Detect cmnd = end")
except Exception as e:
print("Detect cmnd = ", e)
detector = Detector()
detector.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
detector.set_info_images(original_image=aligned_image)
# output of detector part
info_images = getattr(detector, "info_images")
return info_images
