run the inference

@@ -18,7 +18,7 @@ import shlex
 import json
 # Create your views here.
 import sys
-sys.path.insert(0, "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria")
+sys.path.insert(0, "/khuhub/2015104192/web/backend/yt8m/esot3ria")
 import inference_pb
 
 def with_ffprobe(filename):
@@ -76,14 +76,17 @@ class VideoFileUploadView(APIView):
         if file_serializer.is_valid():
             file_serializer.save()     
             # 동영상 길이 출력  
-            runTime = with_ffprobe('/'+file_serializer.data['file_save_name'])
-            print(runTime)
+            fileFullName = file_serializer.data['file_save_name']
+            fileProcessedName = fileFullName.replace("%3A",":")
+            runTime = with_ffprobe(fileProcessedName)
+            print('RUNTIME ::::==== ' + str(runTime))
             print(threshold)
-            process = subprocess.Popen(['./runMediaPipe.sh %s %s' %(file_serializer.data['file_save_name'],runTime,)], shell = True)
+            print('CURR DIR = ' + os.getcwd())
+            process = subprocess.Popen(['./runMediaPipe.sh %s %s' %(fileProcessedName,runTime,)], shell = True)
             process.wait()
 
 
-            result =  inference_pb.inference_pb('/tmp/mediapipe/features.pb', threshold)
+            result =  inference_pb.inference_pb('E:/khuhub/2015104192/web/backend/yt8m/featuremaps/features(baby_samoyed).pb', threshold)
 
             return Response(result, status=status.HTTP_201_CREATED)
         else:

 Django==3.0.5
 django-cors-headers==3.2.1
 djangorestframework==3.11.0
-tensorflow==1.15
+tensorflow==1.14
 pandas==1.0.4
 gensim==3.8.3
\ No newline at end of file

 #!/bin/bash
-cd ../../../mediapipe
+cd ../../../../mediapipe
 . venv/bin/activate
+echo "STEP1"
+bazel version && \
+alias bazel='bazel'
 
-/usr/local/bazel/2.0.0/lib/bazel/bin/bazel version && \
-alias bazel='/usr/local/bazel/2.0.0/lib/bazel/bin/bazel'
+echo "STEP2"
 
 python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \
   --path_to_input_video=/$1 \
   --clip_end_time_sec=$2
-
-GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
+echo "STEP3"
+GLOG_logtostderr=1 /mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
   --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \
   --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb  \
-  --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb
\ No newline at end of file
+  --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb
+echo "COMPLETED"
+
+sleep 5
\ No newline at end of file

+#-*- coding: utf-8 -*-
 import numpy as np
 import tensorflow as tf
 from tensorflow import logging
@@ -8,12 +9,12 @@ import video_recommender as recommender
 import video_util as videoutil
 
 # Define file paths.
-MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/model/inference_model/segment_inference_model"
-VOCAB_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/vocabulary.csv"
-VIDEO_TAGS_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/kaggle_solution_40k.csv"
-TAG_VECTOR_MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/tag_vectors.model"
-VIDEO_VECTOR_MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/video_vectors.model"
-SEGMENT_LABEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/segment_label_ids.csv"
+MODEL_PATH = "E:/khuhub/2015104192/web/backend/yt8m/esot3ria/model/inference_model/segment_inference_model"
+VOCAB_PATH = "E:/khuhub/2015104192/web/backend/yt8m/vocabulary.csv"
+VIDEO_TAGS_PATH = "E:/khuhub/2015104192/web/backend/yt8m/esot3ria/kaggle_solution_40k.csv"
+TAG_VECTOR_MODEL_PATH = "E:/khuhub/2015104192/web/backend/yt8m/esot3ria/tag_vectors.model"
+VIDEO_VECTOR_MODEL_PATH = "E:/khuhub/2015104192/web/backend/yt8m/esot3ria/video_vectors.model"
+SEGMENT_LABEL_PATH = "E:/khuhub/2015104192/web/backend/yt8m/segment_label_ids.csv"
 
 # Define parameters.
 TAG_TOP_K = 5
@@ -85,6 +86,7 @@ def normalize_tag(tag):
 
 
 def inference_pb(file_path, threshold):
+    print("started step 1")
     VIDEO_TOP_K = int(threshold)
     inference_result = {}
     with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
@@ -168,7 +170,7 @@ def inference_pb(file_path, threshold):
 
         # 3. Make vocabularies.
         voca_dict = {}
-        vocabs = open(VOCAB_PATH, 'r')
+        vocabs = open(VOCAB_PATH, 'r', encoding='UTF8')
         while True:
             line = vocabs.readline()
             if not line: break
@@ -176,7 +178,7 @@ def inference_pb(file_path, threshold):
             if vocab_dict_item[0] != "Index":
                 voca_dict[vocab_dict_item[0]] = vocab_dict_item[3]
         vocabs.close()
-
+        print("made vocabs")
         # 4. Make combined scores.
         combined_scores = {}
         for line in format_predictions(video_id_batch_val, predictions_val, TAG_TOP_K, whitelisted_cls_mask):

@@ -113,7 +113,6 @@ def make_exmp(id, audio, rgb):
 
 def convert_pb(filename):
     sequence_example = open(filename, 'rb').read()
-
     audio, rgb = parse_exmp(sequence_example)
     tmp_example = make_exmp('video', audio, rgb)
 

@@ -8,6 +8,7 @@ FLAGS = flags.FLAGS
 
 
 def main(unused_argv):
+  print("start mediapipe")
   # Get the input tensor names to be replaced.
   tf.reset_default_graph()
   meta_graph_location = FLAGS.checkpoint_file + ".meta"

@@ -17,5 +17,6 @@ module.exports = {
     'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
     'linebreak-style': 0,
     'max-len': ['error', { code: 200 }],
+    'import/no-extraneous-dependencies': ["error", {"devDependencies": true}],
   },
 };

@@ -11,12 +11,12 @@
     "@mdi/font": "^3.6.95",
     "axios": "^0.19.2",
     "core-js": "^3.6.5",
-    "moment": "^2.24.0",
+    "moment": "^2.29.1",
     "roboto-fontface": "*",
-    "vue": "^2.6.12",
-    "vue-router": "^3.1.6",
-    "vuetify": "^2.2.11",
-    "vuex": "^3.1.3"
+    "vue-router": "^3.4.7",
+    "vuetify": "^2.3.15",
+    "vuex": "^3.5.1",
+    "yarn": "^1.22.10"
   },
   "devDependencies": {
     "@vue/cli-plugin-babel": "~4.3.0",
@@ -24,16 +24,17 @@
     "@vue/cli-plugin-router": "~4.3.0",
     "@vue/cli-plugin-vuex": "~4.3.0",
     "@vue/cli-service": "~4.3.0",
-    "@vue/eslint-config-airbnb": "^5.0.2",
+    "@vue/eslint-config-airbnb": "^5.1.0",
     "babel-eslint": "^10.1.0",
     "eslint": "^6.7.2",
-    "eslint-plugin-import": "^2.20.2",
+    "eslint-plugin-import": "^2.22.1",
     "eslint-plugin-vue": "^6.2.2",
     "node-sass": "^4.12.0",
-    "sass": "^1.19.0",
+    "sass": "^1.27.0",
     "sass-loader": "^8.0.2",
-    "vue-cli-plugin-vuetify": "~2.0.5",
+    "vue": "^2.6.12",
+    "vue-cli-plugin-vuetify": "^2.0.7",
     "vue-template-compiler": "^2.6.11",
-    "vuetify-loader": "^1.3.0"
+    "vuetify-loader": "^1.6.0"
   }
 }