Comments (3)
I am not sure but tensorflow provides utilities to convert a model to tflite. Otherwise you can retrain the model and save it as a tflite model.
from image-segmentation-u-2-net.
I dont have any experience with tflite models. As for the prediction script make changes to the below script to suit your needs :
import cv2
import numpy as np
from keras.models import load_model
from sklearn.preprocessing import binarize
from tensorflow.keras.preprocessing.image import array_to_img
model = load_model('models/model_DUTS.h5', compile=False)
img = cv2.imread('images/spawn.png', cv2.IMREAD_COLOR)
img = cv2.resize(img, (256, 256))
img = np.expand_dims(img, axis=0)
preds = model.predict(img)
preds = np.squeeze(preds)
# For thresholding
for i in range(len(preds)):
shape = preds[i, :, :].shape
frame = binarize(preds[1, :, :], threshold=0.5)
frame = np.reshape(frame, (shape[0], shape[1]))
frame = np.expand_dims(np.array(preds[i,:,:]),axis=-1)
frame = array_to_img(frame)
frame.save('img'+str(i)+'.png')
from image-segmentation-u-2-net.
here is the conversion code
import os
from keras.models import load_model
import tensorflow as tf
model = load_model('models/model_DUTS.h5', compile=False)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open ("model.tflite" , "wb") .write(tfmodel)
print("Sucess!")
and this is for getting inference from converted tflite model
import tensorflow as tf
import PIL.Image as Image
import numpy as np
from sklearn.preprocessing import binarize
from tensorflow.keras.preprocessing.image import array_to_img
import cv2
def preprocess(img_path, dim):
img = Image.open(img_path)
img = img.resize(dim, Image.BILINEAR)
img = np.array(img).astype(np.float32)
img = np.expand_dims(img, axis=0)
return img
w = 256
h = 256
dim = (w,h)
model = "model.tflite"
img_path = 'images/boat.jpg'
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
#Preprocess the image to required size and cast
img = preprocess(img_path, dim)
input_shape = input_details[0]['shape']
input_tensor= np.array(np.expand_dims(img,0))
input_tensor = input_tensor.reshape(input_shape)
#set the tensor to point to the input data to be inferred
input_index = interpreter.get_input_details()[0]["index"]
interpreter.set_tensor(input_index, input_tensor)
#Run the inference
interpreter.invoke()
preds = interpreter.get_tensor(output_details[0]['index'])
preds = np.squeeze(preds)
#For thresholding
for i in range(len(preds)):
shape = preds[i,:,:].shape
frame = binarize(preds[1,:,:], threshold = 0.5)
frame = np.reshape(frame,(shape[0], shape[1]))
#For saving all the frames
for i in range(len(preds)):
img = np.expand_dims(np.array(preds[i,:,:]),axis=-1)
img = array_to_img(img)
img.save('img'+str(i)+'.png')
from image-segmentation-u-2-net.
Related Issues (8)
- Threshold useless? HOT 7
- Do I need to do training to improve prediction results? HOT 1
- Training code is not giving any output HOT 2
- Data loader takes too long to load data HOT 1
- error from the predict file HOT 3
- predict.py TypeError: __init__() got an unexpected keyword argument 'ragged' HOT 2
- Apply u2net to cell instance segmentation HOT 23
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from image-segmentation-u-2-net.