app.py

from pathlib import Path
import json
from typing import Dict, Optional, List, Tuple
from collections import defaultdict
import streamlit as st
from streamlit.runtime.uploaded_file_manager import UploadedFile
import numpy as np
from pose_format import Pose
from pose_format.utils.generic import pose_hide_legs, reduce_holistic
from pose_format.pose_visualizer import PoseVisualizer
from pyzstd import decompress
from PIL import Image
import mediapipe as mp

mp_holistic = mp.solutions.holistic
FACEMESH_CONTOURS_POINTS = [
    str(p)
    for p in sorted(
        set([p for p_tup in list(mp_holistic.FACEMESH_CONTOURS) for p in p_tup])
    )
]
COMPONENT_SELECTION_METHODS = ["manual", "signclip", "youtube-asl", "reduce_holistic"]

def download_json(data):
    json_data = json.dumps(data)
    json_bytes = json_data.encode('utf-8')
    return json_bytes

def get_points_dict_and_components_with_index_list(
    pose: Pose, landmark_indices: List[int], components_to_include: Optional[List[str]]
) -> Tuple[List[str], Dict[str, List[str]]]:
    """Used to get components/points if you only have a list of indices, 
    e.g. listed in a research paper like YouTube-ASL.
    If you want to also explicitly specify component names, you can.
    So for example, to get the two hands and the nose you could do the following:
    c_names, points_dict = get_points_dict_and_components_with_index_list(pose,
        landmark_indices=[0] # which is "NOSE" within POSE_LANDMARKS components
        components_to_include=["LEFT_HAND_LANDMARKS", "RIGHT_HAND_LANDMARKS]
        )

    then you can just use get_components
    filtered_pose = pose.get_components(c_names, points_dict)

    """
    components_to_get = []
    points_dict = defaultdict(list)

    for c in pose.header.components:
        for point_name in c.points:
            point_index = pose.header.get_point_index(c.name, point_name)
            if point_index in landmark_indices:
                components_to_get.append(c.name)
                points_dict[c.name].append(point_name)
                # print(f"Point with index {point_index} has name {c.name}:{point_name}")

    if components_to_include:
        components_to_get.extend(components_to_include)
    components_to_get = list(set(components_to_get))
    # print("*********************")
    # print(components_to_get)
    # print(points_dict)
    return components_to_get, points_dict


# @st.cache_data(hash_funcs={UploadedFile: lambda p: str(p.name)})
def load_pose(uploaded_file: UploadedFile) -> Pose:

    # with input_path.open("rb") as f_in:
    if uploaded_file.name.endswith(".zst"):
        return Pose.read(decompress(uploaded_file.read()))
    else:
        return Pose.read(uploaded_file.read())


@st.cache_data(hash_funcs={Pose: lambda p: np.asarray(p.body.data.data)})
def get_pose_frames(pose: Pose, transparency: bool = False):
    v = PoseVisualizer(pose)
    frames = [frame_data for frame_data in v.draw()]

    if transparency:
        cv_code = v.cv2.COLOR_BGR2RGBA
    else:
        cv_code = v.cv2.COLOR_BGR2RGB
    images = [Image.fromarray(v.cv2.cvtColor(frame, cv_code)) for frame in frames]
    return frames, images


def get_pose_gif(
    pose: Pose,
    step: int = 1,
    start_frame: Optional[int] = None,
    end_frame: Optional[int] = None,
    fps: Optional[float] = None,
):
    if fps is not None:
        pose.body.fps = fps
    v = PoseVisualizer(pose)
    frames = [frame_data for frame_data in v.draw()]
    frames = frames[start_frame:end_frame:step]
    return v.save_gif(None, frames=frames)


st.write("# Pose-format explorer")
st.write(
    "`pose-format` is a toolkit/library for 'handling, manipulation, and visualization of poses'. See [The documentation](https://pose-format.readthedocs.io/en/latest/)"
)
st.write(
    "I made this app to help me visualize and understand the format, including different 'components' and 'points', and what they are named."
)
st.write(
    "If you need a .pose file, here's a few:"
)
st.write("* One of [me doing a self-introduction](https://drive.google.com/file/d/1_L5sYVhONDBABuTmQUvjsl94LbFqzEyP/view?usp=sharing)")
st.write("* One of [me signing ASL 'HOUSE'](https://drive.google.com/file/d/1uggYqLyTA4XdDWaWsS9w5hKaPwW86IF_/view?usp=sharing)")
st.write(
    "* ... or [the same file, but with the 10 extra landmarks](https://drive.google.com/file/d/1XHkfn24PIas1a3XUUXYXTX2DvYeUDuCI/view?usp=drive_link) from mediapipe holistic's [`refine_face_landmarks` option](https://github.com/sign-language-processing/pose/?tab=readme-ov-file#2-estimating-pose-from-video)"
)
uploaded_file = st.file_uploader("Upload a .pose file", type=[".pose", ".pose.zst"])


if uploaded_file is not None:
    with st.spinner(f"Loading {uploaded_file.name}"):
        pose = load_pose(uploaded_file)
        # st.write(pose.body.data.shape)
        frames, images = get_pose_frames(pose=pose)
    st.success("Done loading!")

    st.write("### File Info")
    with st.expander(f"Show full Pose-format header from {uploaded_file.name}"):
        st.write(pose.header)

    st.write(f"### Selection")
    component_selection = st.radio(
        "How to select components?", options=COMPONENT_SELECTION_METHODS
    )

    component_names = [c.name for c in pose.header.components]
    chosen_component_names = []
    points_dict = {}
    HIDE_LEGS = False

    if component_selection == "manual":

        chosen_component_names = st.pills(
            "Select components to visualize",
            options=component_names,
            default=component_names,
            selection_mode="multi",
        )

        for component in pose.header.components:
            if component.name in chosen_component_names:
                with st.expander(f"Points for {component.name}"):
                    selected_points = st.multiselect(
                        f"Select points for component {component.name}:",
                        options=component.points,
                        default=component.points,
                    )
                    if (
                        selected_points != component.points
                    ):  # Only add entry if not all points are selected
                        points_dict[component.name] = selected_points

    elif component_selection == "signclip":
        st.write("Selected landmarks used for SignCLIP.")
        chosen_component_names = [
            "POSE_LANDMARKS",
            "FACE_LANDMARKS",
            "LEFT_HAND_LANDMARKS",
            "RIGHT_HAND_LANDMARKS",
        ]
        points_dict = {"FACE_LANDMARKS": FACEMESH_CONTOURS_POINTS}

    elif component_selection == "youtube-asl":
        st.write("Selected landmarks used for SignCLIP.")
        # https://arxiv.org/pdf/2306.15162
        # For each hand, we use all 21 landmark points.
        # Colin: So that's
        # For the pose, we use 6 landmark points, for the shoulders, elbows and hips
        # These are indices 11, 12, 13, 14, 23, 24
        # For the face, we use 37 landmark points, from the eyes, eyebrows, lips, and face outline.
        # These are indices 0, 4, 13, 14, 17, 33, 37, 39, 46, 52, 55, 61, 64, 81, 82, 93, 133, 151, 152, 159, 172, 178,
        # 181, 263, 269, 276, 282, 285, 291, 294, 311, 323, 362, 386, 397, 468, 473.
        # Colin: note that these are with refine_face_landmarks on, and are relative to the component itself. Working it all out the result is:
        components=['POSE_LANDMARKS', 'FACE_LANDMARKS', 'LEFT_HAND_LANDMARKS', 'RIGHT_HAND_LANDMARKS']
        points_dict={
            "POSE_LANDMARKS": [
                "LEFT_SHOULDER",
                "RIGHT_SHOULDER",
                "LEFT_HIP",
                "RIGHT_HIP",
                "LEFT_ELBOW",
                "RIGHT_ELBOW"
            ],
            "FACE_LANDMARKS": [
                "0",
                "4",
                "13",
                "14",
                "17",
                "33",
                "37",
                "39",
                "46",
                "52",
                "55",
                "61",
                "64",
                "81",
                "82",
                "93",
                "133",
                "151",
                "152",
                "159",
                "172",
                "178",
                "181",
                "263",
                "269",
                "276",
                "282",
                "285",
                "291",
                "294",
                "311",
                "323",
                "362",
                "386",
                "397",
                "468", # 468 only exists with the refine_face_landmarks option on MediaPipe
                "473", # 473 only exists with the refine_face_landmarks option on MediaPipe
            ]
        }

    # Filter button logic
    # Filter section
    st.write("### Filter .pose File")
    filtered = st.button("Apply Filter!")
    if filtered:
        st.write(f"Filtering strategy: {component_selection}")

        if component_selection == "reduce_holistic":
            # st.write(f"reduce_holistic:")
            pose = reduce_holistic(pose)
            st.write("Used pose_format.reduce_holistic")
        else:
            pose = pose.get_components(components=chosen_component_names, points=points_dict if points_dict else None
            )
            with st.expander("Show component list and points dict used for get_components"):
                st.write("##### Component names")
                st.write(chosen_component_names)
                st.write("##### Points dict")
                st.write(points_dict)

            with st.expander("How to replicate in pose-format"):
                st.write("##### Usage:")
                st.write("How to achieve the same result with pose-format library")
                # points_dict_str = json.dumps(points_dict, indent=4)
                usage_string = f"components={chosen_component_names}\npoints_dict={points_dict}\npose = pose.get_components(components=components, points=points_dict)"
                st.code(usage_string)

        if HIDE_LEGS:
            pose = pose_hide_legs(pose, remove=True)
        st.session_state.filtered_pose = pose

    filtered_pose = st.session_state.get("filtered_pose", pose)
    if filtered_pose:
        filtered_pose = st.session_state.get("filtered_pose", pose)
        st.write("#### Filtered .pose file")
        st.write(f"Pose data shape: {filtered_pose.body.data.shape}")
        with st.expander("Show header"):
            st.write(filtered_pose.header)
        with st.expander("Show body"):
            st.write(filtered_pose.body)
        # with st.expander("Show data:"):
        #     for frame in filtered_pose.body.data:
        #         st.write(f"Frame:{frame}")
        #         for person in frame:
        #             st.write(person)

        pose_file_out = Path(uploaded_file.name).with_suffix(".pose")
        with pose_file_out.open("wb") as f:
            pose.write(f)

        with pose_file_out.open("rb") as f:
            st.download_button(
                "Download Filtered Pose", f, file_name=pose_file_out.name
            )

    st.write("### Visualization")
    step = st.select_slider(
        "Step value to select every nth image", list(range(1, len(frames))), value=1
    )
    fps = st.slider(
        "FPS for visualization",
        min_value=1.0,
        max_value=filtered_pose.body.fps,
        value=filtered_pose.body.fps,
    )
    start_frame, end_frame = st.slider(
        "Select Frame Range",
        0,
        len(frames),
        (0, len(frames)),  # Default range
    )
    # Visualization button logic
    if st.button("Visualize"):
        # Load filtered pose if it exists; otherwise, use the unfiltered pose

        pose_bytes = get_pose_gif(
            pose=filtered_pose,
            step=step,
            start_frame=start_frame,
            end_frame=end_frame,
            fps=fps,
        )
        if pose_bytes is not None:
            st.image(pose_bytes)