| | 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: |
| | |
| | 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() |
| |
|
