Upload app.py

app.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.layers import LSTM
+import streamlit as st
+
+labels = np.array(['FALL', 'LYING', 'SIT', 'STAND', 'MOVE'])
+
+n_time_steps = 25
+mpPose = mp.solutions.pose
+pose = mpPose.Pose()
+mpDraw = mp.solutions.drawing_utils
+
+def custom_lstm(*args, **kwargs):
+    kwargs.pop('time_major', None)
+    return LSTM(*args, **kwargs)
+
+model = tf.keras.models.load_model('bro.h5', custom_objects={'LSTM': custom_lstm})
+
+def make_landmark_timestep(results):
+    c_lm = []
+    for id, lm in enumerate(results.pose_landmarks.landmark):
+        c_lm.append(lm.x)
+        c_lm.append(lm.y)
+        c_lm.append(lm.z)
+        c_lm.append(lm.visibility)
+    return c_lm
+
+def draw_landmark_on_image(mpDraw, results, img, label):
+    mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS)
+    for id, lm in enumerate(results.pose_landmarks.landmark):
+        h, w, c = img.shape
+        cx, cy = int(lm.x * w), int(lm.y * h)
+        if label != "FALL":
+            cv2.circle(img, (cx, cy), 5, (0, 255, 0), cv2.FILLED)
+        else:
+            cv2.circle(img, (cx, cy), 5, (0, 0, 255), cv2.FILLED)
+    return img
+
+def draw_class_on_image(label, img):
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    bottomLeftCornerOfText = (10, 30)
+    fontScale = 1
+    fontColor = (0, 255, 0)
+    thickness = 2
+    lineType = 2
+    cv2.putText(img, label,
+                bottomLeftCornerOfText,
+                font,
+                fontScale,
+                fontColor,
+                thickness,
+                lineType)
+    return img
+
+def detect(model, lm_list):
+    lm_list = np.array(lm_list)
+    lm_list = np.expand_dims(lm_list, axis=0)
+    results = model.predict(lm_list)
+    if results[0][0] >= 0.5: 
+        label = labels[0]
+    elif results[0][1] >= 0.5:
+        label = labels[1]  
+    elif results[0][2] >= 0.5:
+        label = labels[2]
+    elif results[0][3] >= 0.5:
+        label = labels[3]
+    elif results[0][4] >= 0.5:
+        label = labels[4]
+    else:
+        label = "NONE DETECTION"
+    return label
+
+def main():
+    st.title("Pose Detection and Classification")
+    
+    run_type = st.sidebar.selectbox("Select input type", ("Camera", "Video File"))
+    
+    if run_type == "Camera":
+        cap = cv2.VideoCapture(0)
+    else:
+        video_file = st.sidebar.file_uploader("Upload a video", type=["mp4", "mov", "avi"])
+        if video_file is not None:
+            # Temporarily save the uploaded video to disk to pass to cv2.VideoCapture
+            with open("temp_video.mp4", "wb") as f:
+                f.write(video_file.read())
+            cap = cv2.VideoCapture("temp_video.mp4")
+        else:
+            st.write("Please upload a video file.")
+            return
+    
+    stframe = st.empty()
+    label = 'Starting...'
+    lm_list = []
+
+    while cap.isOpened():
+        success, img = cap.read()
+        if not success:
+            break
+        
+        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = pose.process(imgRGB)
+        
+        if results.pose_landmarks:
+            c_lm = make_landmark_timestep(results)
+            img = draw_landmark_on_image(mpDraw, results, img, label)
+            img = draw_class_on_image(label, img)
+            lm_list.append(c_lm)
+            if len(lm_list) == n_time_steps:
+                label = detect(model, lm_list)
+                lm_list = []
+        
+        stframe.image(img, channels="BGR")
+        
+        if cv2.waitKey(1) == ord('q'):
+            break
+    
+    cap.release()
+
+if __name__ == '__main__':
+    main()