Update app.py

app.py

@@ -1,77 +1,40 @@
-# import streamlit as st
-# import cv2
-# import mediapipe as mp
-# import numpy as np
-# import math
-# from tensorflow.keras.models import Model
-# from tensorflow.keras.layers import (LSTM, Dense, Dropout, Input, Flatten, 
-#                                      Bidirectional, Permute, multiply)
+import os
+import streamlit as st
+import cv2
+import mediapipe as mp
+import numpy as np
+import math
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import (LSTM, Dense, Dropout, Input, Flatten, 
+                                     Bidirectional, Permute, multiply)
 
-# # Load the pose estimation model from Mediapipe
-# mp_pose = mp.solutions.pose 
-# mp_drawing = mp.solutions.drawing_utils 
-# pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) 
+# Load the pose estimation model from Mediapipe
+mp_pose = mp.solutions.pose 
+mp_drawing = mp.solutions.drawing_utils 
+pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) 
 
-# # Define the attention block for the LSTM model
-# def attention_block(inputs, time_steps):
-#     a = Permute((2, 1))(inputs)
-#     a = Dense(time_steps, activation='softmax')(a)
-#     a_probs = Permute((2, 1), name='attention_vec')(a)
-#     output_attention_mul = multiply([inputs, a_probs], name='attention_mul') 
-#     return output_attention_mul
+# Define the attention block for the LSTM model
+def attention_block(inputs, time_steps):
+    a = Permute((2, 1))(inputs)
+    a = Dense(time_steps, activation='softmax')(a)
+    a_probs = Permute((2, 1), name='attention_vec')(a)
+    output_attention_mul = multiply([inputs, a_probs], name='attention_mul') 
+    return output_attention_mul
 
-# # Build and load the LSTM model
-# @st.cache(allow_output_mutation=True)
-# def build_model(HIDDEN_UNITS=256, sequence_length=30, num_input_values=33*4, num_classes=3):
-#     inputs = Input(shape=(sequence_length, num_input_values))
-#     lstm_out = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True))(inputs)
-#     attention_mul = attention_block(lstm_out, sequence_length)
-#     attention_mul = Flatten()(attention_mul)
-#     x = Dense(2*HIDDEN_UNITS, activation='relu')(attention_mul)
-#     x = Dropout(0.5)(x)
-#     x = Dense(num_classes, activation='softmax')(x)
-#     model = Model(inputs=[inputs], outputs=x)
-#     load_dir = "./models/LSTM_Attention.h5"  
-#     model.load_weights(load_dir)
-#     return model
-
-# # Define the VideoProcessor class for real-time video processing
-# class VideoProcessor:
-#     def __init__(self):
-#         self.actions = np.array(['curl', 'press', 'squat'])
-#         self.sequence_length = 30
-#         self.colors = [(245,117,16), (117,245,16), (16,117,245)]
-#         self.pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
-#         self.model = build_model()
-    
-#     def process_video(self, video_file):
-#         # Get the filename from the file object
-#         filename = video_file.name
-#         # Create a temporary file to write the contents of the uploaded video file
-#         temp_file = open(filename, 'wb')
-#         temp_file.write(video_file.read())
-#         temp_file.close()
-#         # Now we can open the video file using cv2.VideoCapture()
-#         cap = cv2.VideoCapture(filename)
-#         out_frames = []
-#         while cap.isOpened():
-#             ret, frame = cap.read()
-#             if not ret:
-#                 break
-#             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#             results = self.pose.process(frame_rgb)
-#             frame = self.draw_landmarks(frame, results)
-#             out_frames.append(frame)
-#         cap.release()
-#         # Remove the temporary file
-#         os.remove(filename)
-#         return out_frames
-    
-#     def draw_landmarks(self, image, results):
-#         mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
-#                                   mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), 
-#                                   mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2))
-#         return image
+# Build and load the LSTM model
+@st.cache(allow_output_mutation=True)
+def build_model(HIDDEN_UNITS=256, sequence_length=30, num_input_values=33*4, num_classes=3):
+    inputs = Input(shape=(sequence_length, num_input_values))
+    lstm_out = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True))(inputs)
+    attention_mul = attention_block(lstm_out, sequence_length)
+    attention_mul = Flatten()(attention_mul)
+    x = Dense(2*HIDDEN_UNITS, activation='relu')(attention_mul)
+    x = Dropout(0.5)(x)
+    x = Dense(num_classes, activation='softmax')(x)
+    model = Model(inputs=[inputs], outputs=x)
+    load_dir = "./models/LSTM_Attention.h5"  
+    model.load_weights(load_dir)
+    return model
 
 class VideoProcessor:
     def __init__(self):
@@ -240,6 +203,8 @@ class VideoProcessor:
         # Remove the temporary file
         os.remove(filename)
         return out_frames
+
+
 # Define Streamlit app
 def main():
     st.title("Real-time Exercise Detection")