Fix docker http server issues and reorganise entrypoints

Dockerfile.server

@@ -22,4 +22,4 @@ RUN pip install -r requirements.txt
 
 ENV PYTHONPATH='/src/:$PYTHONPATH'
 
-ENTRYPOINT ["entrypoints/entrypoint.server.sh"]
+ENTRYPOINT ["entrypoints/entrypoint.predict.server.sh"]

entrypoints/entrypoint.predict.server.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+set -e
+
+BASE_MODEL_NAME=$1
+WEIGHTS_FILE=$2
+
+# predict
+python -m evaluater.server \
+--base-model-name $BASE_MODEL_NAME \
+--weights-file $WEIGHTS_FILE

src/evaluater/predict.py

@@ -5,15 +5,11 @@
 from utils.utils import calc_mean_score, save_json
 from handlers.model_builder import Nima
 from handlers.data_generator import TestDataGenerator
-from keras import backend as K
-from PIL import ImageFile, Image
 
 
 def image_file_to_json(img_path):
     img_dir = os.path.dirname(img_path)
-    splits = os.path.basename(img_path).split('.')
-    splits.pop()
-    img_id = ".".join(splits)
+    img_id = os.path.basename(img_path).split('.')[0]
 
     return img_dir, [{'image_id': img_id}]
 
@@ -23,9 +19,7 @@ def image_dir_to_json(img_dir, img_type='jpg'):
 
     samples = []
     for img_path in img_paths:
-        splits = os.path.basename(img_path).split('.')
-        splits.pop()
-        img_id = ".".join(splits)
+        img_id = os.path.basename(img_path).split('.')[0]
         samples.append({'image_id': img_id})
 
     return samples
@@ -43,9 +37,6 @@ def main(base_model_name, weights_file, image_source, predictions_file, img_form
         image_dir = image_source
         samples = image_dir_to_json(image_dir, img_type='jpg')
 
-    ImageFile.LOAD_TRUNCATED_IMAGES = True
-    Image.MAX_IMAGE_PIXELS = None
-
     # build model and load weights
     nima = Nima(base_model_name, weights=None)
     nima.build()
@@ -57,17 +48,16 @@ def main(base_model_name, weights_file, image_source, predictions_file, img_form
 
     # get predictions
     predictions = predict(nima.nima_model, data_generator)
-    K.clear_session()
 
     # calc mean scores and add to samples
     for i, sample in enumerate(samples):
         sample['mean_score_prediction'] = calc_mean_score(predictions[i])
 
+    print(json.dumps(samples, indent=2))
+
     if predictions_file is not None:
         save_json(samples, predictions_file)
 
-    return samples
-
 
 if __name__ == '__main__':
 

src/evaluater/server.py

@@ -0,0 +1,86 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+import os
+from flask import Flask, request, jsonify
+from evaluater.predict import image_file_to_json, image_dir_to_json, predict
+import urllib
+import shutil
+import argparse
+from keras import backend as K
+from PIL import ImageFile, Image
+from handlers.model_builder import Nima
+from handlers.data_generator import TestDataGenerator
+
+app = Flask('server')
+
+def load_model(config):
+    global model
+    model = Nima(config.base_model_name)
+    model.build()
+    model.nima_model.load_weights(config.weights_file)
+    model.nima_model._make_predict_function()  # https://github.com/keras-team/keras/issues/6462
+    model.nima_model.summary()
+
+def main(image_source, predictions_file, img_format='jpg'):
+    # load samples
+    if os.path.isfile(image_source):
+        image_dir, samples = image_file_to_json(image_source)
+    else:
+        image_dir = image_source
+        samples = image_dir_to_json(image_dir, img_type='jpg')
+
+    ImageFile.LOAD_TRUNCATED_IMAGES = True
+    Image.MAX_IMAGE_PIXELS = None
+
+    # initialize data generator
+    data_generator = TestDataGenerator(samples, image_dir, 64, 10, model.preprocessing_function(),
+                                       img_format=img_format)
+
+    # get predictions
+    predictions = predict(model.nima_model, data_generator)
+    K.clear_session()
+
+    # calc mean scores and add to samples
+    for i, sample in enumerate(samples):
+        sample['mean_score_prediction'] = calc_mean_score(predictions[i])
+
+    if predictions_file is not None:
+        save_json(samples, predictions_file)
+
+    return samples
+
+@app.route('/prediction', methods=['POST'])
+def prediction():
+
+    global images
+
+    if request.method == 'POST':
+        images = request.json
+
+        if images:
+
+            shutil.rmtree('temp')
+            os.mkdir('temp')
+            for image in images:
+                filename_w_ext = os.path.basename(image)
+                try:
+                    urllib.request.urlretrieve(image, 'temp/'+ filename_w_ext)
+                except:
+                    print('An exception occurred :' + image)
+
+            result = main('temp', None)
+
+            return jsonify(result)
+
+        return jsonify({'error': 'Image is not available'})
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-b', '--base-model-name', help='CNN base model name', required=True)
+    parser.add_argument('-w', '--weights-file', help='path of weights file', required=True)
+    args = parser.parse_args()
+
+    load_model(args)
+    app.run(host='0.0.0.0', port=5005)