Merge branch 'master' into 'amashi_dev'

# Conflicts:
#   reveng/processInput.py

api.py

@@ -35,6 +35,8 @@ def home():
 @app.route('/cam')
 def openCam():
     os.system('python dataq\detect.py')
+    
+    return render_template('homePage.html')
 # END DATAQ
 
 

dataq/detect.py

@@ -107,48 +107,51 @@ try:
                       tensor_name)
 
                 while True:
-                    ret, image_np = cap.read()
-                    temp_image = image_np.copy()
-                    # 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 = run_inference_for_single_image(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 translation component here
-                    ###
-                    # I will be sending a POST request to u. a hand picture  
-                    print(category_index) 
-                    if score > 80:
-                        #print(image_np_expanded.shape)
-                        img = Image.fromarray(temp_image)
-                        #print(image_np.shape)
-                        img.save(os.getcwd() + "\\" + "capture.jpg")
-                        # array_for_translation = resize(image_np,(224,224,3))
-                        # print(array_for_translation.shape)
-                        translation.sign_predict(os.getcwd() + "\\" + "capture.jpg")
+                    # session for detect.py
+                    with sess.as_default():
+
+                        ret, image_np = cap.read()
+                        temp_image = image_np.copy()
+                        # 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 = run_inference_for_single_image(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)
                         
-                       # sign_predict(image_np) <-- still under development
-                        #waiting for the API on that component to be built
-                    # end send request
-
-                    ## Press Q to close the camera
-                    cv2.imshow('Hand Detector. Press Q to close', cv2.resize(image_np, (800, 600)))
-                    
-                    if cv2.waitKey(25) & 0xFF == ord('q'):
-                        cap.release()
-                        cv2.destroyAllWindows()
-                        break
+                        score = round(100*output_dict['detection_scores'][0])
+                        #send the request to translation component here
+                        ###
+                        # I will be sending a POST request to u. a hand picture  
+                        print(category_index) 
+                        if score > 80:
+                            #print(image_np_expanded.shape)
+                            img = Image.fromarray(temp_image)
+                            #print(image_np.shape)
+                            img.save(os.getcwd() + "\\" + "capture.jpg")
+                            # array_for_translation = resize(image_np,(224,224,3))
+                            # print(array_for_translation.shape)
+                            translation.sign_predict(os.getcwd() + "\\" + "capture.jpg")
+                            
+                        # sign_predict(image_np) <-- still under development
+                            #waiting for the API on that component to be built
+                        # end send request
+
+                        ## Press Q to close the camera
+                        cv2.imshow('Hand Detector. Press Q to close', cv2.resize(image_np, (800, 600)))
+                        
+                        if cv2.waitKey(25) & 0xFF == ord('q'):
+                            cap.release()
+                            cv2.destroyAllWindows()
+                            break
 except Exception as e:
     print(e)
     cap.release()
\ No newline at end of file

templates/signToText.html

@@ -39,15 +39,7 @@
   <!--Favicon-->
   <link rel="shortcut icon" href="static/images/EasyTalkFavicon_v1.png" type="image/x-icon">
   <link rel="icon" href="static/images/EasyTalkFavicon_v1.png" type="image/x-icon">
-  <script>
-    function goPython(){
-        $.ajax({
-          url: ""
-        }).done(function() {
-         alert('finished python script');;
-        });
-    }
-</script>
+
 </head>
 
 <body>
@@ -115,7 +107,9 @@
       <div class="col-12">
         <form id="cameraForm" class="justify-content-center">
             <div class="text-center">
-              <button type="submit" class="btn btn-primary mb-2" id="userButtonInput" style="height: 75%;" onclick="goPython()">Open My Webcamera</button>
+              <a href="http://localhost:3000/cam">
+                open cam
+                </a>
             </div>
         </form>
       </div>

textvoice/__init__.py

+from textvoice.textVoiceAs import TextVoiceAs
\ No newline at end of file

textvoice/textVoiceAs.py

+# -*- coding: utf-8 -*-
+
+import functools
+import sys
+
+from .OneGramDist import OneGramDist
+from . import spell
+#import spell
+from . import temp
+from . import speech
+
+import os
+
+class TextVoiceAs:
+
+    def genarate(self,letters):
+        sentence = ''.join(sys.argv[1:]).lower()
+        if not sentence:
+            sentence = letters
+        # onegrams = OneGramDist(filename='count_10M_gb.txt')
+        #onegrams = OneGramDist(filename='count_1M_gb.txt.gz')
+        onegrams = OneGramDist(filename= os.path.join(os.path.dirname(os.path.abspath(__file__)),'count_1w.txt'))
+        onegram_fitness = functools.partial(temp.onegram_log, onegrams)
+        #print(sentence)
+        words = temp.segment(sentence, word_seq_fitness=onegram_fitness)
+        # print(words)
+        
+        output = [spell.correction(words[index]) for index in range(0,len(words))]
+        speech.inputs(' '.join(output))
+        print(' '.join(output))
+        return 

translation/converted_keras/labels.txt

-0 one
-1 two
-2 five3
-3 face4
-4 three5
+0 A
+1 B
+2 C
+3 D
+4 E
+5 F
+6 G
+7 H
+8 I
+9 J
+10 K
+11 L
+12 M
+13 N
+14 O
+15 P
+16 Q
+17 R
+18 S
+19 T
+20 U
+21 V
+22 W
+23 X
+24 Y
+25 Z

translation/trans.py

@@ -5,25 +5,38 @@
 # * @since   2020-08-15
 
 # imports
+import tensorflow as tf
 import tensorflow.keras
 from PIL import Image, ImageOps
 import numpy as np
 import os
+import sys
+sys.path.append('.')
+from textvoice.textVoiceAs import TextVoiceAs
 import string
 
 tensorflow.compat.v1.disable_eager_execution()
 
 class Translation:
 
-    PATH = os.path.dirname(__file__)
-
+    LOCATION = os.path.dirname(__file__)
+    Letters_ = ""
+    Fletter_ = ""
+    Sletter_ = ""
+    Tex = TextVoiceAs()
 
     # def sign_predict():
     # Disable scientific notation for clarity
     np.set_printoptions(suppress=True)
 
     # Load the model
-    model = tensorflow.keras.models.load_model(PATH + '/converted_keras/keras_model.h5')
+    g2=tf.Graph()
+    # with g2.as_default():
+    sess2 = tf.compat.v1.Session()
+    with sess2.as_default():
+        model_p = tensorflow.keras.models.load_model(LOCATION + '/converted_keras/keras_model.h5')
+        model_p._make_predict_function()
+    # print(model_p.summary())
     #model = tensorflow.keras.models.load_model('C:/Users/user/Documents/A to Y 1000 dataset/keras_model.h5')
 
     # Create the array of the right shape to feed into the keras model
@@ -34,48 +47,55 @@ class Translation:
     #data = np.ndarray(shape=(1, 480, 640, 3), dtype=np.float32)
 
     def sign_predict(self, loc):
-
-        # Replace this with the path to your image
-        # 'C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png'
-        image = Image.open(loc)
-
-        # resize the image to a 224x224 with the same strategy as in TM2:
-        # resizing the image to be at least 224x224 and then cropping from the center
-
-        size = (224, 224)
-        #size = (640, 480)
-        image = ImageOps.fit(image, size, Image.ANTIALIAS)
-
-        # turn the image into a numpy array
-        image_array = np.asarray(image)
-        #print(image_array)
-
-        # display the resized image
-        #image.show()
-
-        # Normalize the image
-        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-
-        # Load the image into the array
-        self.data[0] = normalized_image_array
-
-        # run the inference
-        print("Before model*****************")
-        prediction = self.model.predict(self.data)
-        print("After model*****************")
-        #print(prediction)
-        #print(prediction.shape)
-        #print(type(prediction))
-        #print(prediction[0, 2])
-        for alpha in range(26):
-            if prediction[0, alpha] >= 0.8:
-                print(string.ascii_uppercase[alpha])
-                #return string.ascii_uppercase[alpha]
-                break
-        
-        print("After Classification*****************")
-        # if os.path.exists(loc):
-        #     os.remove(loc)
-        # else:
-        #     print("The file does not exist")
+        #with self.g2.as_default():
+        with self.sess2.as_default():
+
+            # Replace this with the path to your image
+            # 'C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png'
+            image = Image.open(loc)
+
+            # resize the image to a 224x224 with the same strategy as in TM2:
+            # resizing the image to be at least 224x224 and then cropping from the center
+
+            size = (224, 224)
+            #size = (640, 480)
+            image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+            # turn the image into a numpy array
+            image_array = np.asarray(image)
+            #print(image_array)
+
+            # display the resized image
+            #image.show()
+
+            # Normalize the image
+            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+            # Load the image into the array
+            self.data[0] = normalized_image_array
+            # run the inference
+            #sess2.run(tf.global_variables_initializer())
+            prediction = self.model_p.predict(self.data)
+            #print(prediction.shape)
+            #print(type(prediction))
+            #print(prediction[0, 2])
+            for alpha in range(26):  
+                if prediction[0, alpha] >= 0.8:
+                    self.Sletter_ = string.ascii_lowercase[alpha]
+                    if alpha == 25:
+                        self.Tex.genarate(self.Letters_)
+                        print(self.Letters_)
+                        self.Letters_ = ""
+                        self.Sletter_ = ""
+                        self.Fletter_ = ""
+                    if self.Sletter_ != self.Fletter_:
+                        print(self.Sletter_) 
+                        self.Letters_ = self.Letters_ + self.Sletter_                                     
+                        #break
+                    
+            self.Fletter_ = self.Sletter_
+            # if os.path.exists(loc):
+            #     os.remove(loc)
+            # else:
+            #     print("The file does not exist")
         return

translation/transMain.py

 from trans import Translation
-import TextVoice
+import sys
+sys.path.append('.')
+#from textvoice.textVoiceAs import TextVoiceAs
 
-if __name__ == '__main__':
+def transMain():
     t1 = Translation()
-    Tex = TextVoice()
+    #Tex = TextVoiceAs()
 
-    location1 = 'C:/Users/user/Documents/GitHub/2020_077/test1.jpg'
-    location2 = 'C:/Users/user/Documents/GitHub/2020_077/test2.jpg'
-    location3 = 'C:/Users/user/Documents/GitHub/2020_077/test3.jpg'
-    location4 = 'C:/Users/user/Documents/GitHub/2020_077/test4.jpg'
-    location5 = 'C:/Users/user/Documents/GitHub/2020_077/test55.jpg'
+    location1 = 'C:/Users/user/Documents/GitHub/2020_077/a.jpg'
+    location2 = 'C:/Users/user/Documents/GitHub/2020_077/c.jpg'
+    location3 = 'C:/Users/user/Documents/GitHub/2020_077/d.jpg'
+    location4 = 'C:/Users/user/Documents/GitHub/2020_077/e.jpg'
+    location5 = 'C:/Users/user/Documents/GitHub/2020_077/g.jpg'
+    location6 = 'C:/Users/user/Documents/GitHub/2020_077/i.jpg'
+    location7 = 'C:/Users/user/Documents/GitHub/2020_077/n.jpg'
+    location8 = 'C:/Users/user/Documents/GitHub/2020_077/o.jpg'
+    location9 = 'C:/Users/user/Documents/GitHub/2020_077/y.jpg'
+    location10 = 'C:/Users/user/Documents/GitHub/2020_077/z.jpg'
 
-    Tex.getInput()
-    #getInput('t1.sign_predict(location1)')
-    # t1.sign_predict(location2)
+    #Tex.genarate('cab')
+    
+    t1.sign_predict(location7)
+    t1.sign_predict(location6)
+    t1.sign_predict(location2)
+    t1.sign_predict(location4)
+    t1.sign_predict(location3)
+    t1.sign_predict(location1)
+    t1.sign_predict(location9)
+    t1.sign_predict(location10)
     # t1.sign_predict(location3)
-    # t1.sign_predict(location4)
-    # t1.sign_predict(location5)
-    # t1.sign_predict(location2)
-    # t1.sign_predict(location3)
-    # t1.sign_predict(location4)
-    # t1.sign_predict(location5)
-    # t1.sign_predict(location1)
-    # t1.sign_predict(location4)
\ No newline at end of file
+    #Tex.genarate(t1.sign_predict(location5))
+    t1.sign_predict(location5)
+    t1.sign_predict(location8)
+    t1.sign_predict(location8)
+    t1.sign_predict(location3)
+    t1.sign_predict(location1)
+    t1.sign_predict(location9)
+    t1.sign_predict(location10)
+
+transMain() 
\ No newline at end of file

translation/transtest.py

+# * The trans.py file is created to predict signs with images
+# *
+# * @author  K.Bavanraj | IT17032766
+# * @version 1.0
+# * @since   2020-08-15
+
+# imports
+import tensorflow.keras
+from PIL import Image, ImageOps
+import numpy as np
+import os
+import string
+
+tensorflow.compat.v1.disable_eager_execution()
+
+
+def sign_predict(loc):
+
+    PATH = os.path.dirname(__file__)
+
+    # def sign_predict():
+    # Disable scientific notation for clarity
+    np.set_printoptions(suppress=True)
+
+    # Load the model
+    model = tensorflow.keras.models.load_model(PATH + '/converted_keras/keras_model.h5')
+    #model = tensorflow.keras.models.load_model('C:/Users/user/Documents/A to Y 1000 dataset/keras_model.h5')
+
+    # Create the array of the right shape to feed into the keras model
+    # The 'length' or number of images you can put into the array is
+    # determined by the first position in the shape tuple, in this case 1.
+
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    #data = np.ndarray(shape=(1, 480, 640, 3), dtype=np.float32)
+
+    # Replace this with the path to your image
+    # 'C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png'
+    image = Image.open(loc)
+
+    # resize the image to a 224x224 with the same strategy as in TM2:
+    # resizing the image to be at least 224x224 and then cropping from the center
+
+    size = (224, 224)
+    #size = (640, 480)
+    image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+    # turn the image into a numpy array
+    image_array = np.asarray(image)
+    # print(image_array)
+
+    # display the resized image
+    # image.show()
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+    # Load the image into the array
+    data[0] = normalized_image_array
+
+    #self.model.run_eagerly = False
+    # run the inference
+    print("Before model*****************")
+    prediction = model.predict(data)
+    print("After model*****************")
+       # print(prediction)
+       # print(prediction.shape)
+       # print(type(prediction))
+       #print(prediction[0, 2])
+    for alpha in range(26):
+        if prediction[0, alpha] >= 0.8:
+            print(string.ascii_uppercase[alpha])
+            # return string.ascii_uppercase[alpha]
+            break
+
+    print("After Classification*****************")
+        # if os.path.exists(loc):
+        #     os.remove(loc)
+        # else:
+        #     print("The file does not exist")
+        # return