| 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() |
|
|
