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