Delete run.py

run.py

@@ -1,122 +0,0 @@
-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()