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utils.py
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utils.py
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import os
import pickle
import cv2
import numpy as np
import streamlit as st
import tensorflow as tf
import grpc
from tensorflow_serving.apis import (
prediction_service_pb2_grpc,
predict_pb2
)
from consts import (
TRAIN_FD,
TRAIN_PKL_FP,
TRAIN_LABEL_FP
)
@st.cache
def load_prec_embs():
with open(TRAIN_PKL_FP, "rb") as f:
train_embs = pickle.load(f)
with open(TRAIN_LABEL_FP, "rb") as f:
train_labels = pickle.load(f)
train_img_fps = wfile(TRAIN_FD)
assert len(train_img_fps) == train_embs.shape[0]
return train_img_fps, train_embs, train_labels
def wfile(root):
img_fps = []
for path, subdirs, files in os.walk(root):
for name in files:
img_fps.append(os.path.join(path, name))
return sorted(img_fps)
class FlowerArc:
def __init__(self,
host="localhost",
port=8500,
model_name="flower",
model_signature="flower_signature",
input_name="input_image",
output_name="emb_pred"):
self.host = host
self.port = port
self.channel = grpc.insecure_channel("{}:{}".format(
self.host, self.port
))
self.stub = prediction_service_pb2_grpc.PredictionServiceStub(
self.channel
)
self.input_name = input_name
self.output_name = output_name
self.request = predict_pb2.PredictRequest()
self.request.model_spec.name = model_name
self.request.model_spec.signature_name = model_signature
def norm_mean_std(self,
img):
img = img / 255
img = img.astype('float32')
mean = np.mean(img, axis=(0, 1, 2))
std = np.std(img, axis=(0, 1, 2))
img = (img - mean) / std
return img
def test_preprocess(self,
img,
img_size=(384, 384),
expand=True):
img = cv2.resize(img, img_size)
# normalize image
img = self.norm_mean_std(img)
if expand:
img = np.expand_dims(img, axis=0)
return img
def predict(self, img):
assert img.ndim == 3
img = self.test_preprocess(img)
self.request.inputs[self.input_name].CopyFrom(
tf.contrib.util.make_tensor_proto(
img,
dtype=tf.float32,
shape=img.shape
)
)
result = self.stub.Predict(self.request, 10.0)
emb_pred = tf.contrib.util.make_ndarray(
result.outputs[self.output_name]
)
return emb_pred
class Saliency:
def __init__(self,
host="localhost",
port=8500,
model_name="saliency",
model_signature="serving_default",
input_name="input_image",
output_name="pred_mask"):
self.host = host
self.port = port
self.channel = grpc.insecure_channel("{}:{}".format(
self.host, self.port
))
self.stub = prediction_service_pb2_grpc.PredictionServiceStub(
self.channel
)
self.input_name = input_name
self.output_name = output_name
self.request = predict_pb2.PredictRequest()
self.request.model_spec.name = model_name
self.request.model_spec.signature_name = model_signature
def test_preprocess(self,
img,
img_size=(320, 240),
expand=True):
img = cv2.resize(img, img_size)
if expand:
img = np.expand_dims(img, axis=0)
return img
def predict(self, img):
assert img.ndim == 3
img = self.test_preprocess(img)
self.request.inputs[self.input_name].CopyFrom(
tf.contrib.util.make_tensor_proto(
img,
dtype=tf.float32,
shape=img.shape
)
)
result = self.stub.Predict(self.request, 10.0)
pred_mask = tf.contrib.util.make_ndarray(
result.outputs[self.output_name]
)
return pred_mask