finished spellcheck

.gitignore

@@ -1 +1,2 @@
 .DS_Store
+.env

deps.txt

@@ -6,3 +6,4 @@ tensorflow
 numpy
 comtypes
 pandas
+pyspellchecker==0.7.2

prototype 2/AI model + Prediction/testmodel.py → prototype_2/AI_model_and_Prediction/testmodel.py

@@ -6,15 +6,28 @@
 import queue
 import platform
 import subprocess
+from spellchecker import SpellChecker
+import os
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+spell = SpellChecker()
+current_word = "";
 
-model = tf.keras.models.load_model("asl_cnn_model_rel.h5")
+model = tf.keras.models.load_model(os.path.join(BASE_DIR, "../AI_model_and_Prediction/asl_cnn_model_rel.h5"))
 
 class_names = [
     'A','B','C','D','E','F','G','H','I','J','K','L','M',
     'N','O','P','Q','R','S','T','U','V','W','X','Y','Z',
     'del','nothing','space'
 ]
 
+def auto_correct(word):
+    # words like i and a:
+    if len(word) <= 1:
+        return word
+    # correcting the letters
+    corrected = spell.correction(word)
+    return corrected if corrected else word
+
 lastspoken = None
 label_lock = threading.Lock()
 landmark_queue = queue.Queue(maxsize=5)
@@ -26,23 +39,36 @@
 single_lower_limit_index = double_letter_frames-first_letter_frames
 upper_limit_index = double_letter_frames + 1
 
-def speak(label):
+def speak(text):
     system = platform.system()
     if system == "Darwin":
-        subprocess.Popen(["say", label])
+        subprocess.Popen(["say", text])
     elif system == "Windows":
         import comtypes.client
         speaker = comtypes.client.CreateObject("SAPI.SpVoice")
-        speaker.Speak(label)
+        speaker.Speak(text)
     else:
-        subprocess.Popen(["spd-say", label])
+        subprocess.Popen(["spd-say", text])
+
+def process_label(label):
+    global current_word
+    if label == "space":
+        if current_word != "":
+            corrected = auto_correct(current_word)
+            speak(corrected)
+            print("Typed:", current_word, "Corrected:", corrected)
+            current_word = ""
+    elif label == "del":
+        current_word = current_word[:-1]
+    elif label != "nothing":
+        current_word += label.lower()
 
 BaseOptions = mp.tasks.BaseOptions
 HandLandmarker = mp.tasks.vision.HandLandmarker
 HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
 VisionRunningMode = mp.tasks.vision.RunningMode
 
-MODEL_PATH = "../hand_landmarker.task"
+MODEL_PATH = os.path.join(BASE_DIR, "../hand_landmarker.task")
 
 def hand_callback(result, output_image, timestamp_ms):
     if not result.hand_landmarks:
@@ -93,17 +119,20 @@ def hand_callback(result, output_image, timestamp_ms):
                 labelbuffer.pop(0)
             if len(labelbuffer) < upper_limit_index:
                 continue
-            elif len(set(labelbuffer[single_lower_limit_index:upper_limit_index])) == 1 and labelbuffer[single_lower_limit_index] != labelbuffer[single_lower_limit_index - 1]:
+            stablelabel = labelbuffer[double_letter_frames]
+            if len(set(labelbuffer[single_lower_limit_index:upper_limit_index])) == 1 and labelbuffer[single_lower_limit_index] != labelbuffer[single_lower_limit_index - 1]:
                 if lastspoken != labelbuffer[double_letter_frames]:
-                    speak(labelbuffer[double_letter_frames])
-                    lastspoken = labelbuffer[double_letter_frames]
+                    process_label(stablelabel)
+                    lastspoken = stablelabel
             elif len(set(labelbuffer[1:upper_limit_index])) == 1 and labelbuffer[1] != labelbuffer[0]:
                 # allow a duplicate letter here
-                speak(labelbuffer[double_letter_frames])
-                lastspoken = labelbuffer[double_letter_frames]
+                process_label(stablelabel)
+                lastspoken = stablelabel
     except queue.Empty:
         pass
-
+    cv2.putText(frame, current_word, (50,50),
+            cv2.FONT_HERSHEY_SIMPLEX, 1,
+            (0,255,0), 2)
     cv2.imshow("Hand Sign Prediction", frame)
     if cv2.waitKey(5) & 0xFF == 27:
         break

prototype 2/MegaDataset/DataSetMaker.py → prototype_2/MegaDataset/DataSetMaker.py

@@ -60,7 +60,7 @@ def UpdateLabelsAndOrientation():
     global target_label, target_orientation
     try:
         target_label = letterhash[input("Enter target label: ").upper()]
-    except KeyError:
+    except:
         print("Invalid label, might be on Tryjobs, if not on TryJobs, restart the program and give it another try.")
         quit()
     target_orientation = int(input(