Spaces:
Sleeping
Sleeping
Théo Rousseaux
commited on
Commit
•
a755c90
1
Parent(s):
025e412
début pose agent
Browse files- Modules/PoseEstimation/__init__.py +0 -0
- Modules/PoseEstimation/pose_agent.py +74 -0
- Modules/PoseEstimation/pose_estimation.ipynb +0 -0
- Modules/PoseEstimation/pose_estimator.py +1 -4
- app.py +9 -2
- utils.py +12 -0
Modules/PoseEstimation/__init__.py
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Modules/PoseEstimation/pose_agent.py
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from Modules.PoseEstimation.pose_estimator import calculate_angle, joints_id_dict, model
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from langchain.tools import tool
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.messages import HumanMessage
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from langchain_mistralai.chat_models import ChatMistralAI
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# If api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.
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llm = ChatMistralAI(model='mistral-large-latest', api_key="i5jSJkCFNGKfgIztloxTMjfckiFbYBj4")
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@tool
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def compute_right_knee_angle(pose: list) -> float:
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"""
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Computes the knee angle.
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Args:
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pose (list): list of keypoints
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Returns:
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knee_angle (float): knee angle
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"""
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right_hip = pose[joints_id_dict['right_hip']]
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right_knee = pose[joints_id_dict['right_knee']]
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right_ankle = pose[joints_id_dict['right_ankle']]
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knee_angle = calculate_angle(right_hip, right_knee, right_ankle)
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print(knee_angle)
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return str(knee_angle)
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@tool
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def get_keypoints_from_path(video_path: str):
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"""
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Get keypoints from a video.
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Args:
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video_path (str): path to the video
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model (YOLO): model to use
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Returns:
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keypoints (list): list of keypoints
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"""
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keypoints = []
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results = model(video_path, save=True, show_conf=False, show_boxes=False)
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for frame in results:
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tensor = frame.keypoints.xy[0]
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keypoints.append(tensor.tolist())
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return keypoints
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tools = [compute_right_knee_angle]
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful assistant. Make sure to use the compute_right_knee_angle tool for information.",
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),
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("placeholder", "{chat_history}"),
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("human", "{input}"),
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("placeholder", "{agent_scratchpad}"),
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]
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)
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# Construct the Tools agent
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agent = create_tool_calling_agent(llm, tools, prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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Modules/PoseEstimation/pose_estimation.ipynb
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The diff for this file is too large to render.
See raw diff
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Modules/PoseEstimation/pose_estimator.py
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@@ -31,8 +31,6 @@ def get_keypoints_from_keypoints(model, video_path):
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return keypoints
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keypoints = get_keypoints_from_keypoints(model, '../../data/pose/squat.mp4')
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def calculate_angle(a, b, c):
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"""
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for i in range(len(data) - window_size + 1):
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avg.append(sum(data[i:i + window_size]) / window_size)
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return avg
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return keypoints
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def calculate_angle(a, b, c):
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"""
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for i in range(len(data) - window_size + 1):
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avg.append(sum(data[i:i + window_size]) / window_size)
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return avg
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app.py
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@@ -7,6 +7,8 @@ from dotenv import load_dotenv
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load_dotenv() # load .env api keys
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import os
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mistral_api_key = os.getenv("MISTRAL_API_KEY")
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st.set_page_config(layout="wide", initial_sidebar_state="collapsed")
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# Create two columns
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if video_uploaded is None:
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video_uploaded = ask_video.file_uploader("Choose a video file", type=["mp4", "ogg", "webm"])
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if video_uploaded:
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ask_video.empty()
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with st.spin("Processing video"):
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pass # TO DO
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_left, mid, _right = st.columns(3)
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with mid:
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st.video(video_uploaded)
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st.subheader("Graph Displayer")
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# TO DO
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load_dotenv() # load .env api keys
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import os
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mistral_api_key = os.getenv("MISTRAL_API_KEY")
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from Modules.PoseEstimation import pose_estimator
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from utils import save_uploaded_file
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st.set_page_config(layout="wide", initial_sidebar_state="collapsed")
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# Create two columns
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if video_uploaded is None:
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video_uploaded = ask_video.file_uploader("Choose a video file", type=["mp4", "ogg", "webm"])
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if video_uploaded:
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video_uploaded = save_uploaded_file(video_uploaded)
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ask_video.empty()
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_left, mid, _right = st.columns(3)
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with mid:
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st.video(video_uploaded)
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apply_pose = st.button("Apply Pose Estimation")
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if apply_pose:
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with st.spinner("Processing video"):
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keypoints = pose_estimator.get_keypoints_from_keypoints(pose_estimator.model, video_uploaded)
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st.subheader("Graph Displayer")
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# TO DO
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utils.py
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import streamlit as st
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import os
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def save_uploaded_file(uploaded_file):
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try:
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file_path = os.path.join('uploaded', uploaded_file.name)
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with open(file_path, 'wb') as f:
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f.write(uploaded_file.getvalue())
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return file_path
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except Exception as e:
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st.error(f"Error: {e}")
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return None
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