code cleanup - dataq

api.py

@@ -18,11 +18,17 @@ app.config["DEBUG"] = True
 def testApi():
     return "<h1>The API is working</h1>"
 
+##DATAQ
 
 @app.route('/')
 def home():
     return render_template('homePage.html')
 
+@app.route('/cam')
+def openCam():
+    os.system('python dataq\detect.py')
+
+## END DATAQ
 
 # REVENG
 @app.route('/tts')

dataq/dataq.py

-import os
-
-PATH = os.path.dirname(__file__)
-FILEPATH = os.path.join(PATH,'detect.py')
-
-exec(open(FILEPATH).read())
\ No newline at end of file

dataq/detect.py

@@ -8,6 +8,8 @@ from PIL import Image
 from tensorflow import keras
 sys.path.append('.')
 from translation.modelUp import sign_predict
+
+PATH = os.path.dirname(os.path.abspath(__file__))
 if StrictVersion(tf.__version__) < StrictVersion('1.15.3'):
   raise ImportError('Please upgrade your TensorFlow installation to v1.15.3 or later!')
 
@@ -15,14 +17,13 @@ print(tf.__version__)
 
 
 
-from object_detection.utils import label_map_util 
-
+from object_detection.utils import label_map_util
 from object_detection.utils import visualization_utils as vis_util
 
 
-MODEL_NAME = 'D:\manobran technologies\SLIIT 4th\cdap\\2020_077\dataq\inference_graph'
+MODEL_NAME = PATH +'\inference_graph'
 PATH_TO_FROZEN_GRAPH = MODEL_NAME + '\\frozen_inference_graph.pb'
-PATH_TO_LABELS = os.path.join('D:\manobran technologies\SLIIT 4th\cdap\\2020_077\dataq\\training','labelmap.pbtxt')
+PATH_TO_LABELS = os.path.join(PATH +'\\training','labelmap.pbtxt')
 
 
 #load tensorflow model to memory

dataq/testDetect.py

-import numpy as np
-import os
-import six.moves.urllib as urllib
-import sys
-import tensorflow as tf
-import zipfile
-import cv2
-from distutils.version import StrictVersion
-from collections import defaultdict
-from io import StringIO
-from PIL import Image
-
-
-if StrictVersion(tf.__version__) < StrictVersion('1.15.3'):
-  raise ImportError('Please upgrade your TensorFlow installation to v1.15.3 or later!')
-
-print(tf.__version__)
-sys.path.append('..')
-
-from object_detection.utils import label_map_util 
-
-from object_detection.utils import visualization_utils as vis_util
-
-
-MODEL_NAME = 'D:\manobran technologies\SLIIT 4th\cdap\\2020_077\dataq\inference_graph'
-PATH_TO_FROZEN_GRAPH = MODEL_NAME + '\\frozen_inference_graph.pb'
-PATH_TO_LABELS = os.path.join('D:\manobran technologies\SLIIT 4th\cdap\\2020_077\dataq\\training','labelmap.pbtxt')
-
-
-#load tensorflow model to memory
-
-detection_graph = tf.Graph()
-
-with detection_graph.as_default():
-  od_graph_def = tf.compat.v1.GraphDef()
-  with tf.compat.v2.io.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
-    serialized_graph = fid.read()
-    od_graph_def.ParseFromString(serialized_graph)
-    tf.import_graph_def(od_graph_def, name='')
-
-
-#map labels
-category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
-
-def run_inference_for_single_image(image, graph):
-    if 'detection_masks' in tensor_dict:
-        # The following processing is only for single image
-        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
-        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
-        
-        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
-        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
-        
-        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
-        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(detection_masks, detection_boxes, image.shape[0], image.shape[1])
-        detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)
-        
-        # Follow the convention by adding back the batch dimension
-        tensor_dict['detection_masks'] = tf.expand_dims(
-            detection_masks_reframed, 0)
-    image_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:0')
-
-    # Run inference
-    output_dict = sess.run(tensor_dict,
-                            feed_dict={image_tensor: np.expand_dims(image, 0)})
-
-    # outputs are float32 numpy arrays
-    output_dict['num_detections'] = int(output_dict['num_detections'][0])
-    output_dict['detection_classes'] = output_dict[
-        'detection_classes'][0].astype(np.uint8)
-    output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
-    output_dict['detection_scores'] = output_dict['detection_scores'][0]
-    if 'detection_masks' in output_dict:
-        output_dict['detection_masks'] = output_dict['detection_masks'][0]
-    return output_dict
-
-
-#------------------------test section do not touch
-#method
-def runInferenceForMultipleImage(imageSet, graph):
-    with detection_graph.as_default():
-        with tf.Session() as session:
-            output_dicts= []
-
-            for index,image in enumerate(imageSet):
-                run_inference_for_single_image(image,graph)
-                output_dicts.append(image)
-    return output_dict
-
-#Initialize Camera
-camera = cv2.VideoCapture(0)
-try:
-    with detection_graph.as_default():
-        with tf.compat.v1.Session() as sess:
-                # Get handles to input and output tensors
-                ops = tf.compat.v1.get_default_graph().get_operations()
-                all_tensor_names = {output.name for op in ops for output in op.outputs}
-                tensor_dict = {}
-                key_detections =  [
-                  'num_detections', 'detection_boxes', 'detection_scores',
-                  'detection_classes', 'detection_masks'
-                ]
-                
-                for key in key_detections:
-                    tensor_name = key + ':0'
-                    if tensor_name in all_tensor_names:
-                        tensor_dict[key] = tf.compat.v1.get_default_graph().get_tensor_by_name(
-                      tensor_name)
-
-                while True:
-                    ret, image_np = camera.read()
-                    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
-                    image_np_expanded = np.expand_dims(image_np, axis=0)
-                    # Actual detection.
-                    output_dict = runInferenceForMultipleImage(image_np, detection_graph)
-                    # Visualization of the results of a detection.
-                    vis_util.visualize_boxes_and_labels_on_image_array(
-                        image_np,
-                        output_dict['detection_boxes'],
-                        output_dict['detection_classes'],
-                        output_dict['detection_scores'],
-                        category_index,
-                        instance_masks=output_dict.get('detection_masks'),
-                        use_normalized_coordinates=True,
-                        line_thickness=8)
-                    
-                    score = round(100*output_dict['detection_scores'][0])
-                    #send the request to bavan here
-                    ###
-                    # I will be sending a POST request to u. a hand picture   
-                    if score > 80:
-                        print(image_np_expanded)
-                        #waiting for the API on that component to be built
-                    # end send request
-                    cv2.imshow('Hand Detector. Press Q to close', cv2.resize(image_np, (800, 600)))
-                    
-                    if cv2.waitKey(25) & 0xFF == ord('q'):
-                        camera.release()
-                        cv2.destroyAllWindows()
-                        break
-except Exception as e:
-    print(e)
-    camera.release()
-
-
-# --------------------------end test section
\ No newline at end of file

dataq/testMakeGIF.py

-import firebase_admin
-from firebase_admin import credentials
-from firebase_admin import storage
-import datetime
-import os
-import requests , PIL.Image as pigm, io
-# Fetch the service account key JSON file contents
-PATH = os.path.dirname (os.path.abspath(__file__))
-JSON = os.path.join(PATH,'creadentials.json')
-cred = credentials.Certificate(JSON)
-
-# Initialize the app with a service account, granting admin privileges
-app = firebase_admin.initialize_app(cred, {
-    'storageBucket': 'storage-9eed9.appspot.com',
-}, name='storage')
-
-bucket = storage.bucket(app=app)
-blob = bucket.blob("afternoon01.jpg")
-
-url = blob.generate_signed_url(datetime.timedelta(seconds=300), method='GET')
-
-response = requests.get(url)
-image_bytes = io.BytesIO(response.content)
-
-img =pigm.open(image_bytes)
-img.show()
\ No newline at end of file

dataq/test_bavan.py

-import cv2
-

dataq/xml_to_csv.py

@@ -26,11 +26,11 @@ def xml_to_csv(path):
 
 
 def converter():
-    # for directory in ['train','test']:
-    #     image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory))
-    #     xml_df = xml_to_csv(image_path)
-    #     xml_df.to_csv('images/{}_labels.csv'.format(directory), index=None)
-    #     print('Successfully converted xml to csv.')
+    for directory in ['train','test']:
+        image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory))
+        xml_df = xml_to_csv(image_path)
+        xml_df.to_csv('images/{}_labels.csv'.format(directory), index=None)
+        print('Successfully converted xml to csv.')
     print("xmltocsv") 
 
-#converter()
+converter()