feat : Get predictions with precentage & threshold value

Project/Backend/Server_Python/app.log

@@ -13,3 +13,6 @@
 2023-05-24 20:05:37,936 - INFO - Error. 
 2023-05-24 20:05:37,936 - INFO - Error. 
 2023-05-24 20:05:37,936 - INFO - Error. 
+2023-07-12 05:50:25,202 - INFO - Error. 'SignLanguagePredictionService' object has no attribute 'predict_sign_language_video2'
+2023-07-12 05:50:25,202 - INFO - Error. 'SignLanguagePredictionService' object has no attribute 'predict_sign_language_video2'
+2023-07-12 05:50:25,202 - INFO - Error. 'SignLanguagePredictionService' object has no attribute 'predict_sign_language_video2'

Project/Backend/Server_Python/controllers/translate_controler.py

@@ -53,7 +53,7 @@ def predict_using_image(image_request: UploadFile = File(...)):
 @router.post('/predict-sign-language/video', tags=["Sign Language"])
 def predict_using_video(video_request: UploadFile = File(...)):
     try:
-        return prediction_service.predict_sign_language_video2(video_request)
+        return prediction_service.predict_sign_language_video_new(video_request)
     except Exception as e:
         logger.info(f"Error. {e}")
         raise HTTPException(

Project/Backend/Server_Python/services/translate_service.py

@@ -3,7 +3,7 @@ import cv2
 import numpy as np
 
 from fastapi import HTTPException, UploadFile
-from typing import Dict
+from typing import Counter, Dict
 
 from core.logger import setup_logger
 
@@ -95,7 +95,7 @@ class SignLanguagePredictionService:
             )
 
 
-    def predict_sign_language_video(self, video_request: UploadFile) -> Dict[str, str]:
+    def predict_sign_language_video_new(self, video_request: UploadFile) -> Dict[str, str]:
         try:
             # Create a temporary file to save the video
             video_location = f"files/{video_request.filename}"
@@ -120,6 +120,8 @@ class SignLanguagePredictionService:
                 frame = extract_hand_shape(frame)
                 frame = np.array([frame], dtype=np.float32) / 255.0
 
+                
+
                 # Make prediction
                 prediction = self.model.predict(frame)
                 class_index = np.argmax(prediction)
@@ -136,7 +138,11 @@ class SignLanguagePredictionService:
             # Delete the video file
             os.remove(video_location)
 
-            return {'frame_count': frame_count, 'predictions': predictions}
+            threshold_percentage = 60
+
+            predictions = get_predicted_percentage(predictions, threshold_percentage)
+
+            return {'frame_count': frame_count, 'predictions': predictions }
         except Exception as e:
             logger.info(f"Failed to make predictions. {e}")
             raise HTTPException(
@@ -153,4 +159,18 @@ def extract_hand_shape(image):
     hand_contour = contours[0]
     hand_shape = np.zeros_like(image)
     cv2.drawContours(hand_shape, [hand_contour], 0, (255, 255, 255), thickness=cv2.FILLED)
-    return hand_shape
\ No newline at end of file
+    return hand_shape
+
+
+
+def get_predicted_percentage(array, threshold):
+    counts = Counter(array)
+    total_elements = len(array)
+    
+    percentages = {}
+    for element, count in counts.items():
+        percentage = (count / total_elements) * 100
+        percentages[element] = percentage
+    
+    elements_above_threshold = [element for element, percentage in percentages.items() if percentage > threshold]
+    return elements_above_threshold
\ No newline at end of file