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# import streamlit as st
# import cv2
# import mediapipe as mp
# import math
# from PIL import Image
# import numpy as np
# ## Build and Load Model
# def attention_block(inputs, time_steps):
# """
# Attention layer for deep neural network
# """
# # Attention weights
# a = Permute((2, 1))(inputs)
# a = Dense(time_steps, activation='softmax')(a)
# # Attention vector
# a_probs = Permute((2, 1), name='attention_vec')(a)
# # Luong's multiplicative score
# output_attention_mul = multiply([inputs, a_probs], name='attention_mul')
# return output_attention_mul
# @st.cache(allow_output_mutation=True)
# def build_model(HIDDEN_UNITS=256, sequence_length=30, num_input_values=33*4, num_classes=3):
# # Input
# inputs = Input(shape=(sequence_length, num_input_values))
# # Bi-LSTM
# lstm_out = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True))(inputs)
# # Attention
# attention_mul = attention_block(lstm_out, sequence_length)
# attention_mul = Flatten()(attention_mul)
# # Fully Connected Layer
# x = Dense(2*HIDDEN_UNITS, activation='relu')(attention_mul)
# x = Dropout(0.5)(x)
# # Output
# x = Dense(num_classes, activation='softmax')(x)
# # Bring it all together
# model = Model(inputs=[inputs], outputs=x)
# ## Load Model Weights
# load_dir = "./models/LSTM_Attention.h5"
# model.load_weights(load_dir)
# return model
# threshold1 = st.slider("Minimum Keypoint Detection Confidence", 0.00, 1.00, 0.50)
# threshold2 = st.slider("Minimum Tracking Confidence", 0.00, 1.00, 0.50)
# threshold3 = st.slider("Minimum Activity Classification Confidence", 0.00, 1.00, 0.50)
# ## Real Time Machine Learning and Computer Vision Processes
# class VideoProcessor:
# def __init__(self):
# # Parameters
# self.actions = np.array(['curl', 'press', 'squat'])
# self.sequence_length = 30
# self.colors = [(245,117,16), (117,245,16), (16,117,245)]
# self.threshold = 0.50 # Default threshold for activity classification confidence
# # Detection variables
# self.sequence = []
# self.current_action = ''
# # Initialize pose model
# self.mp_pose = mp.solutions.pose
# self.mp_drawing = mp.solutions.drawing_utils
# self.pose = self.mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
# @st.cache()
# def draw_landmarks(self, image, results):
# """
# This function draws keypoints and landmarks detected by the human pose estimation model
# """
# self.mp_drawing.draw_landmarks(image, results.pose_landmarks, self.mp_pose.POSE_CONNECTIONS,
# self.mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
# self.mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
# )
# return image
# @st.cache()
# def extract_keypoints(self, results):
# """
# Processes and organizes the keypoints detected from the pose estimation model
# to be used as inputs for the exercise decoder models
# """
# pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
# return pose
# @st.cache()
# def calculate_angle(self, a, b, c):
# """
# Computes 3D joint angle inferred by 3 keypoints and their relative positions to one another
# """
# a = np.array(a) # First
# b = np.array(b) # Mid
# c = np.array(c) # End
# radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
# angle = np.abs(radians*180.0/np.pi)
# if angle > 180.0:
# angle = 360-angle
# return angle
# @st.cache()
# def get_coordinates(self, landmarks, side, joint):
# """
# Retrieves x and y coordinates of a particular keypoint from the pose estimation model
# Args:
# landmarks: processed keypoints from the pose estimation model
# side: 'left' or 'right'. Denotes the side of the body of the landmark of interest.
# joint: 'shoulder', 'elbow', 'wrist', 'hip', 'knee', or 'ankle'. Denotes which body joint is associated with the landmark of interest.
# """
# coord = getattr(self.mp_pose.PoseLandmark, side.upper() + "_" + joint.upper())
# x_coord_val = landmarks[coord.value].x
# y_coord_val = landmarks[coord.value].y
# return [x_coord_val, y_coord_val]
# @st.cache()
# def viz_joint_angle(self, image, angle, joint):
# """
# Displays the joint angle value near the joint within the image frame
# """
# cv2.putText(image, str(int(angle)),
# tuple(np.multiply(joint, [640, 480]).astype(int)),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA
# )
# return
# @st.cache()
# def process_video_input(self, threshold1, threshold2, threshold3):
# """
# Processes the video input and performs real-time action recognition and rep counting.
# """
# video_file = st.file_uploader("Upload Video", type=["mp4", "avi"])
# if video_file is None:
# st.warning("Please upload a video file.")
# return
# cap = cv2.VideoCapture(video_file)
# if not cap.isOpened():
# st.error("Error opening video stream or file.")
# return
# while cap.isOpened():
# ret, frame = cap.read()
# if not ret:
# break
# # Convert frame to RGB (Mediapipe requires RGB input)
# frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# # Pose estimation
# results = self.pose.process(frame_rgb)
# # Draw landmarks
# self.draw_landmarks(frame, results)
# # Extract keypoints
# keypoints = self.extract_keypoints(results)
# # Visualize probabilities
# if len(self.sequence) == self.sequence_length:
# sequence = np.array([self.sequence])
# res = model.predict(sequence)
# frame = self.prob_viz(res[0], frame)
# # Append frame to output frames
# out_frames.append(frame)
# # Release video capture
# cap.release()
# # # Create an instance of VideoProcessor
# # video_processor = VideoProcessor()
# # # Call the process_video_input method
# # video_processor.process_video_input(threshold1, threshold2, threshold3)
# # Define Streamlit app
# def main():
# st.title("Real-time Exercise Detection")
# video_file = st.file_uploader("Upload a video file", type=["mp4", "avi"])
# if video_file is not None:
# st.video(video_file)
# video_processor = VideoProcessor()
# frames = video_processor.process_video(video_file)
# for frame in frames:
# st.image(frame, channels="BGR")
# if __name__ == "__main__":
# main()
import streamlit as st
import cv2
import mediapipe as mp
import numpy as np
import math
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (LSTM, Dense, Dropout, Input, Flatten,
Bidirectional, Permute, multiply)
# Load the pose estimation model from Mediapipe
mp_pose = mp.solutions.pose
mp_drawing = mp.solutions.drawing_utils
pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
# Define the attention block for the LSTM model
def attention_block(inputs, time_steps):
a = Permute((2, 1))(inputs)
a = Dense(time_steps, activation='softmax')(a)
a_probs = Permute((2, 1), name='attention_vec')(a)
output_attention_mul = multiply([inputs, a_probs], name='attention_mul')
return output_attention_mul
# Build and load the LSTM model
@st.cache(allow_output_mutation=True)
def build_model(HIDDEN_UNITS=256, sequence_length=30, num_input_values=33*4, num_classes=3):
inputs = Input(shape=(sequence_length, num_input_values))
lstm_out = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True))(inputs)
attention_mul = attention_block(lstm_out, sequence_length)
attention_mul = Flatten()(attention_mul)
x = Dense(2*HIDDEN_UNITS, activation='relu')(attention_mul)
x = Dropout(0.5)(x)
x = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=[inputs], outputs=x)
load_dir = "./models/LSTM_Attention.h5"
model.load_weights(load_dir)
return model
# Define the VideoProcessor class for real-time video processing
class VideoProcessor:
def __init__(self):
self.actions = np.array(['curl', 'press', 'squat'])
self.sequence_length = 30
self.colors = [(245,117,16), (117,245,16), (16,117,245)]
self.pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
self.model = build_model()
def process_video(self, video_file):
cap = cv2.VideoCapture(video_file)
out_frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = self.pose.process(frame_rgb)
frame = self.draw_landmarks(frame, results)
out_frames.append(frame)
cap.release()
return out_frames
def draw_landmarks(self, image, results):
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2))
return image
# Define Streamlit app
def main():
st.title("Real-time Exercise Detection")
video_file = st.file_uploader("Upload a video file", type=["mp4", "avi"])
if video_file is not None:
st.video(video_file)
video_processor = VideoProcessor()
frames = video_processor.process_video(video_file)
for frame in frames:
st.image(frame, channels="BGR")
if __name__ == "__main__":
main()
# import streamlit as st
# import cv2
# from tensorflow.keras.models import Model
# from tensorflow.keras.layers import (LSTM, Dense, Dropout, Input, Flatten,
# Bidirectional, Permute, multiply)
# import numpy as np
# import mediapipe as mp
# import math
# import streamlit as st
# import cv2
# import mediapipe as mp
# import math
# # from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
# import av
# from io import BytesIO
# import av
# from PIL import Image
# ## Build and Load Model
# def attention_block(inputs, time_steps):
# """
# Attention layer for deep neural network
# """
# # Attention weights
# a = Permute((2, 1))(inputs)
# a = Dense(time_steps, activation='softmax')(a)
# # Attention vector
# a_probs = Permute((2, 1), name='attention_vec')(a)
# # Luong's multiplicative score
# output_attention_mul = multiply([inputs, a_probs], name='attention_mul')
# return output_attention_mul
# @st.cache(allow_output_mutation=True)
# def build_model(HIDDEN_UNITS=256, sequence_length=30, num_input_values=33*4, num_classes=3):
# # Input
# inputs = Input(shape=(sequence_length, num_input_values))
# # Bi-LSTM
# lstm_out = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True))(inputs)
# # Attention
# attention_mul = attention_block(lstm_out, sequence_length)
# attention_mul = Flatten()(attention_mul)
# # Fully Connected Layer
# x = Dense(2*HIDDEN_UNITS, activation='relu')(attention_mul)
# x = Dropout(0.5)(x)
# # Output
# x = Dense(num_classes, activation='softmax')(x)
# # Bring it all together
# model = Model(inputs=[inputs], outputs=x)
# ## Load Model Weights
# load_dir = "./models/LSTM_Attention.h5"
# model.load_weights(load_dir)
# return model
# HIDDEN_UNITS = 256
# model = build_model(HIDDEN_UNITS)
# threshold1 = st.slider("Minimum Keypoint Detection Confidence", 0.00, 1.00, 0.50)
# threshold2 = st.slider("Minimum Tracking Confidence", 0.00, 1.00, 0.50)
# threshold3 = st.slider("Minimum Activity Classification Confidence", 0.00, 1.00, 0.50)
# ## Mediapipe
# mp_pose = mp.solutions.pose # Pre-trained pose estimation model from Google Mediapipe
# mp_drawing = mp.solutions.drawing_utils # Supported Mediapipe visualization tools
# pose = mp_pose.Pose(min_detection_confidence=threshold1, min_tracking_confidence=threshold2) # mediapipe pose model
# ## Real Time Machine Learning and Computer Vision Processes
# class VideoProcessor:
# def __init__(self):
# # Parameters
# self.actions = np.array(['curl', 'press', 'squat'])
# self.sequence_length = 30
# self.colors = [(245,117,16), (117,245,16), (16,117,245)]
# self.threshold = threshold3
# # Detection variables
# self.sequence = []
# self.current_action = ''
# # Rep counter logic variables
# self.curl_counter = 0
# self.press_counter = 0
# self.squat_counter = 0
# self.curl_stage = None
# self.press_stage = None
# self.squat_stage = None
# @st.cache()
# def draw_landmarks(self, image, results):
# """
# This function draws keypoints and landmarks detected by the human pose estimation model
# """
# mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
# mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
# mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
# )
# return
# @st.cache()
# def extract_keypoints(self, results):
# """
# Processes and organizes the keypoints detected from the pose estimation model
# to be used as inputs for the exercise decoder models
# """
# pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
# return pose
# @st.cache()
# def calculate_angle(self, a,b,c):
# """
# Computes 3D joint angle inferred by 3 keypoints and their relative positions to one another
# """
# a = np.array(a) # First
# b = np.array(b) # Mid
# c = np.array(c) # End
# radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
# angle = np.abs(radians*180.0/np.pi)
# if angle > 180.0:
# angle = 360-angle
# return angle
# @st.cache()
# def get_coordinates(self, landmarks, mp_pose, side, joint):
# """
# Retrieves x and y coordinates of a particular keypoint from the pose estimation model
# Args:
# landmarks: processed keypoints from the pose estimation model
# mp_pose: Mediapipe pose estimation model
# side: 'left' or 'right'. Denotes the side of the body of the landmark of interest.
# joint: 'shoulder', 'elbow', 'wrist', 'hip', 'knee', or 'ankle'. Denotes which body joint is associated with the landmark of interest.
# """
# coord = getattr(mp_pose.PoseLandmark,side.upper()+"_"+joint.upper())
# x_coord_val = landmarks[coord.value].x
# y_coord_val = landmarks[coord.value].y
# return [x_coord_val, y_coord_val]
# @st.cache()
# def viz_joint_angle(self, image, angle, joint):
# """
# Displays the joint angle value near the joint within the image frame
# """
# cv2.putText(image, str(int(angle)),
# tuple(np.multiply(joint, [640, 480]).astype(int)),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA
# )
# return
# @st.cache()
# def process_video(self, video_file):
# """
# Processes each frame of the input video, performs pose estimation,
# and counts repetitions of each exercise.
# Args:
# video_file (BytesIO): Input video file.
# Returns:
# tuple: A tuple containing the processed video frames with annotations
# and the final count of repetitions for each exercise.
# """
# cap = cv2.VideoCapture(video_file)
# out_frames = []
# # Initialize repetition counters
# self.curl_counter = 0
# self.press_counter = 0
# self.squat_counter = 0
# while cap.isOpened():
# ret, frame = cap.read()
# if not ret:
# break
# # Convert frame to RGB (Mediapipe requires RGB input)
# frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# # Pose estimation
# results = pose.process(frame_rgb)
# # Draw landmarks
# self.draw_landmarks(frame, results)
# # Extract keypoints
# keypoints = self.extract_keypoints(results)
# # Count repetitions
# self.count_reps(frame, results.pose_landmarks, mp_pose)
# # Visualize probabilities
# if len(self.sequence) == self.sequence_length:
# sequence = np.array([self.sequence])
# res = model.predict(sequence)
# frame = self.prob_viz(res[0], frame)
# # Append frame to output frames
# out_frames.append(frame)
# # Release video capture
# cap.release()
# # Return annotated frames and repetition counts
# return out_frames, {'curl': self.curl_counter, 'press': self.press_counter, 'squat': self.squat_counter}
# @st.cache()
# def count_reps(self, image, landmarks, mp_pose):
# """
# Counts repetitions of each exercise. Global count and stage (i.e., state) variables are updated within this function.
# """
# if self.current_action == 'curl':
# # Get coords
# shoulder = self.get_coordinates(landmarks, mp_pose, 'left', 'shoulder')
# elbow = self.get_coordinates(landmarks, mp_pose, 'left', 'elbow')
# wrist = self.get_coordinates(landmarks, mp_pose, 'left', 'wrist')
# # calculate elbow angle
# angle = self.calculate_angle(shoulder, elbow, wrist)
# # curl counter logic
# if angle < 30:
# self.curl_stage = "up"
# if angle > 140 and self.curl_stage =='up':
# self.curl_stage="down"
# self.curl_counter +=1
# self.press_stage = None
# self.squat_stage = None
# # Viz joint angle
# self.viz_joint_angle(image, angle, elbow)
# elif self.current_action == 'press':
# # Get coords
# shoulder = self.get_coordinates(landmarks, mp_pose, 'left', 'shoulder')
# elbow = self.get_coordinates(landmarks, mp_pose, 'left', 'elbow')
# wrist = self.get_coordinates(landmarks, mp_pose, 'left', 'wrist')
# # Calculate elbow angle
# elbow_angle = self.calculate_angle(shoulder, elbow, wrist)
# # Compute distances between joints
# shoulder2elbow_dist = abs(math.dist(shoulder,elbow))
# shoulder2wrist_dist = abs(math.dist(shoulder,wrist))
# # Press counter logic
# if (elbow_angle > 130) and (shoulder2elbow_dist < shoulder2wrist_dist):
# self.press_stage = "up"
# if (elbow_angle < 50) and (shoulder2elbow_dist > shoulder2wrist_dist) and (self.press_stage =='up'):
# self.press_stage='down'
# self.press_counter += 1
# self.curl_stage = None
# self.squat_stage = None
# # Viz joint angle
# self.viz_joint_angle(image, elbow_angle, elbow)
# elif self.current_action == 'squat':
# # Get coords
# # left side
# left_shoulder = self.get_coordinates(landmarks, mp_pose, 'left', 'shoulder')
# left_hip = self.get_coordinates(landmarks, mp_pose, 'left', 'hip')
# left_knee = self.get_coordinates(landmarks, mp_pose, 'left', 'knee')
# left_ankle = self.get_coordinates(landmarks, mp_pose, 'left', 'ankle')
# # right side
# right_shoulder = self.get_coordinates(landmarks, mp_pose, 'right', 'shoulder')
# right_hip = self.get_coordinates(landmarks, mp_pose, 'right', 'hip')
# right_knee = self.get_coordinates(landmarks, mp_pose, 'right', 'knee')
# right_ankle = self.get_coordinates(landmarks, mp_pose, 'right', 'ankle')
# # Calculate knee angles
# left_knee_angle = self.calculate_angle(left_hip, left_knee, left_ankle)
# right_knee_angle = self.calculate_angle(right_hip, right_knee, right_ankle)
# # Calculate hip angles
# left_hip_angle = self.calculate_angle(left_shoulder, left_hip, left_knee)
# right_hip_angle = self.calculate_angle(right_shoulder, right_hip, right_knee)
# # Squat counter logic
# thr = 165
# if (left_knee_angle < thr) and (right_knee_angle < thr) and (left_hip_angle < thr) and (right_hip_angle < thr):
# self.squat_stage = "down"
# if (left_knee_angle > thr) and (right_knee_angle > thr) and (left_hip_angle > thr) and (right_hip_angle > thr) and (self.squat_stage =='down'):
# self.squat_stage='up'
# self.squat_counter += 1
# self.curl_stage = None
# self.press_stage = None
# # Viz joint angles
# self.viz_joint_angle(image, left_knee_angle, left_knee)
# self.viz_joint_angle(image, left_hip_angle, left_hip)
# else:
# pass
# return
# @st.cache()
# def prob_viz(self, res, input_frame):
# """
# This function displays the model prediction probability distribution over the set of exercise classes
# as a horizontal bar graph
# """
# output_frame = input_frame.copy()
# for num, prob in enumerate(res):
# cv2.rectangle(output_frame, (0,60+num*40), (int(prob*100), 90+num*40), self.colors[num], -1)
# cv2.putText(output_frame, self.actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
# return output_frame
# # Slider widgets
# threshold1 = st.slider("Minimum Keypoint Detection Confidence", 0.00, 1.00, 0.50)
# threshold2 = st.slider("Minimum Tracking Confidence", 0.00, 1.00, 0.50)
# threshold3 = st.slider("Minimum Activity Classification Confidence", 0.00, 1.00, 0.50)
# # Sidebar
# st.sidebar.header("Settings")
# st.sidebar.write("Adjust the confidence thresholds")
# # Call process_video_input() method from VideoProcessor
# video_processor.process_video_input(threshold1, threshold2, threshold3)
# # def process_uploaded_file(self, file):
# # """
# # Function to process an uploaded image or video file and run the fitness trainer AI
# # Args:
# # file (BytesIO): uploaded image or video file
# # Returns:
# # numpy array: processed image with keypoint detection and fitness activity classification visualized
# # """
# # # Initialize an empty list to store processed frames
# # processed_frames = []
# # # Check if the uploaded file is a video
# # is_video = hasattr(file, 'name') and file.name.endswith(('.mp4', '.avi', '.mov'))
# # if is_video:
# # container = av.open(file)
# # for frame in container.decode(video=0):
# # # Convert the frame to OpenCV format
# # image = frame.to_image().convert("RGB")
# # image = np.array(image)
# # # Process the frame
# # processed_frame = self.process(image)
# # # Append the processed frame to the list
# # processed_frames.append(processed_frame)
# # # Close the video file container
# # container.close()
# # else:
# # # If the uploaded file is an image
# # # Load the image from the BytesIO object
# # image = Image.open(file)
# # image = np.array(image)
# # # Process the image
# # processed_frame = self.process(image)
# # # Append the processed frame to the list
# # processed_frames.append(processed_frame)
# # return processed_frames
# # def recv_uploaded_file(self, file):
# # """
# # Receive and process an uploaded video file
# # Args:
# # file (BytesIO): uploaded video file
# # Returns:
# # List[av.VideoFrame]: list of processed video frames
# # """
# # # Process the uploaded file
# # processed_frames = self.process_uploaded_file(file)
# # # Convert processed frames to av.VideoFrame objects
# # av_frames = []
# # for frame in processed_frames:
# # av_frame = av.VideoFrame.from_ndarray(frame, format="bgr24")
# # av_frames.append(av_frame)
# # return av_frames
# # # Options
# # RTC_CONFIGURATION = RTCConfiguration(
# # {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
# # )
# # # Streamer
# # webrtc_ctx = webrtc_streamer(
# # key="AI trainer",
# # mode=WebRtcMode.SENDRECV,
# # rtc_configuration=RTC_CONFIGURATION,
# # media_stream_constraints={"video": True, "audio": False},
# # video_processor_factory=VideoProcessor,
# # async_processing=True,
# # ) |