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import argparse
import json
import gdown
import os
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
from pathlib import Path
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
from tensorflow.keras.models import load_model
@st.cache(show_spinner=False)
def download_weights(model_choice):
"""
Downloads model weights for deployment
"""
# Create directory
save_dest = Path("models")
save_dest.mkdir(exist_ok=True)
# Download weights for the chosen model
if model_choice == "DenseNet (baseline)":
url = "https://drive.google.com/uc?id=10-TWkCW_BAZLpGXkxPqXFV8lg-jnWNJD"
output = "models/densenet.h5"
if not Path(output).exists():
with st.spinner("Model weights were not found, downloading them. This may take a while."):
gdown.download(url, output, quiet=False)
elif model_choice == "VGG16 (baseline)":
url = "https://drive.google.com/uc?id=1UaNIHQ-HYeN5v6egV9kAdwU0Nb4CfLBF"
output = "models/vgg16.h5"
if not Path(output).exists():
with st.spinner("Model weights were not found, downloading them. This may take a while."):
gdown.download(url, output, quiet=False)
elif model_choice == "DenseNet (best)":
url = "https://drive.google.com/uc?id=1JUvuzyGQpScHyq2q25yhG962g3PMJ1eu"
output = "models/densenet_best.h5"
if not Path(output).exists():
with st.spinner("Model weights were not found, downloading them. This may take a while."):
gdown.download(url, output, quiet=False)
elif model_choice == "VGG16 (best)":
url = "https://drive.google.com/uc?id=19iu-Qhaofczgl6iMt6DSB_OHDBs9ggsr"
output = "models/vgg16_best.h5"
if not Path(output).exists():
with st.spinner("Model weights were not found, downloading them. This may take a while."):
gdown.download(url, output, quiet=False)
else:
raise ValueError("Unknown model: {}".format(model_choice))
def preprocess_image(image_file):
"""Preprocess image"""
x, _ = process_path(image_file)
x = np.expand_dims(x, axis=0)
return x
def app_dof_predict(model_choice, image_file):
# Download weights for the chosen model
download_weights(model_choice)
image = preprocess_image(image_file)
prediction = {}
if model_choice == "DenseNet (baseline)":
model = load_model("models/densenet.h5", compile=False)
elif model_choice == "VGG16 (baseline)":
model = load_model("models/vgg16.h5", compile=False)
elif model_choice == "DenseNet (best)":
model = load_model("models/densenet_best.h5", compile=False)
elif model_choice == "VGG16 (best)":
model = load_model("models/vgg16_best.h5", compile=False)
preds = model.predict(image)
prediction = {
"class": int(np.argmax(preds)),
"probability": float(preds[0][np.argmax(preds)]),
}
return prediction
def decode_img(img):
"""Decode image and resize"""
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, [200, 300])
return img
def process_path(file_path):
"""Process input path"""
img = tf.io.read_file(file_path)
img = decode_img(img)
return img, file_path
def plot_results(infer_images, inference_predicted_class, inference_predictions, class_names=["bokeh", "no bokeh"]):
"""Plot four images with predicted class and probabilities"""
plt.figure(figsize=(40, 60))
for i, (infer_img, _) in enumerate(infer_images.take(10)):
ax = plt.subplot(2, 5, i + 1)
plt.imshow(infer_img.numpy() / 255)
# Find the predicted class from predictions
m = "Predicted: {}, {:.2f}%".format(class_names[inference_predicted_class[i]], inference_predictions[i] * 100)
plt.title(m)
plt.axis("off")
plt.show()
def dof_predict(infer_images, model_path):
trained_model = load_model(model_path, compile=False)
inference_predicted_class = []
inference_predictions = []
results = {}
for infer_img, img_name in infer_images:
print(img_name)
preds = trained_model.predict(tf.expand_dims(infer_img, axis=0))
inference_predicted_class.append(np.argmax(preds))
print(preds)
inference_predictions.append(preds[0][np.argmax(preds)])
results[str(img_name.numpy().decode("utf8").split("/")[-1])] = {
"class": int(np.argmax(preds)),
"prob": float(preds[0][np.argmax(preds)]),
}
plot_results(infer_images, inference_predicted_class, inference_predictions)
return results
def save_results(results):
"""Save results to json"""
json.dump(results, open("results.json", "w"))
def main(test_dir, model_path):
# get the count of image files in the train directory
inference_ds = tf.data.Dataset.list_files(test_dir + "/*", shuffle=False)
infer_images = inference_ds.map(process_path)
# inference
results = dof_predict(infer_images, model_path)
# save results
save_results(results)
if __name__ == "__main__":
# Initiate the parser
parser = argparse.ArgumentParser()
parser.add_argument("-data", action="store", help="Dataset path")
parser.add_argument("-model", action="store", help="Model path")
arguments = parser.parse_args()
dataset = arguments.data
model = arguments.model
main(dataset, model)
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