v2 working

working
2025-12-08 20:25:20 +01:00 · 2025-11-28 15:36:57 +00:00 · 2025-11-28 08:31:35 +00:00 · 2025-11-27 21:28:10 +01:00 · 2025-09-10 19:59:07 +02:00 · 2025-09-10 19:56:21 +02:00
45 changed files with 2331 additions and 1098 deletions
--- a/.gitattributes
+++ b/.gitattributes
@ -0,0 +1 @@
+*.pth filter=lfs diff=lfs merge=lfs -text
--- a/.gitignore
+++ b/.gitignore
@ -1,6 +1,9 @@
 /mmdetection/
-/mmpose/
 /.ipynb_checkpoints/
 /.gpu/
 /.gpu-3d/
-/.venv/
+/.venv/
+/venv/
+*.mp4
+
+yolo11*
--- a/.idea/JustTwerk.iml
+++ b/.idea/JustTwerk.iml
@ -2,13 +2,20 @@
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 </project>
--- a/.idea/vcs.xml
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@ -2,7 +2,5 @@
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-    <SUITE FILE_PATH="coverage/JustTwerk$is_torch.coverage" NAME="is_torch Coverage Results" MODIFIED="1755943611769" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
-    <SUITE FILE_PATH="coverage/JustTwerk$test.coverage" NAME="test Coverage Results" MODIFIED="1755962675907" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$3ddisplay_replay_smoothed.coverage" NAME="3ddisplay_replay_smoothed Coverage Results" MODIFIED="1764878813417" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$record_video_pose.coverage" NAME="record_video_pose Coverage Results" MODIFIED="1765215859591" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$receive_images.coverage" NAME="receive_images Coverage Results" MODIFIED="1755966230858" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$is_torch.coverage" NAME="is_torch Coverage Results" MODIFIED="1764256054151" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$rotate.coverage" NAME="rotate Coverage Results" MODIFIED="1764831290832" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$mac.coverage" NAME="mac Coverage Results" MODIFIED="1764919104935" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$3cams_3D_v2.coverage" NAME="3cams_3D_v2 Coverage Results" MODIFIED="1765111338220" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/JustTwerk$test.coverage" NAME="test Coverage Results" MODIFIED="1756025632346" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
  </component>
 </project>
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@ -0,0 +1,3 @@
+{
+    "liveServer.settings.port": 5501
+}
--- a/02_whole_body_from_image.py
+++ b/02_whole_body_from_image.py
@ -1,70 +0,0 @@
-# From Python
-# It requires OpenCV installed for Python
-import sys
-import cv2
-import os
-from sys import platform
-import argparse
-
-try:
-    # Import Openpose (Windows/Ubuntu/OSX)
-    dir_path = r"C:\Users\Kajetan\Documents\openpose/python"
-    try:
-        # Change these variables to point to the correct folder (Release/x64 etc.)
-        sys.path.append(dir_path + '/../bin/python/openpose/Release');
-        os.environ['PATH']  = os.environ['PATH'] + ';' + dir_path + '/../x64/Release;' +  dir_path + '/../bin;'
-
-        print(os.environ["PATH"])
-        import pyopenpose as op
-    except ImportError as e:
-        print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
-        raise e
-
-    # Flags
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--image_path", default="../examples/media/COCO_val2014_000000000241.jpg", help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
-    args = parser.parse_known_args()
-
-    # Custom Params (refer to include/openpose/flags.hpp for more parameters)
-    params = dict()
-    params["model_folder"] = "../models/"
-    params["face"] = True
-    params["hand"] = True
-
-    # Add others in path?
-    for i in range(0, len(args[1])):
-        curr_item = args[1][i]
-        if i != len(args[1])-1: next_item = args[1][i+1]
-        else: next_item = "1"
-        if "--" in curr_item and "--" in next_item:
-            key = curr_item.replace('-','')
-            if key not in params:  params[key] = "1"
-        elif "--" in curr_item and "--" not in next_item:
-            key = curr_item.replace('-','')
-            if key not in params: params[key] = next_item
-
-    # Construct it from system arguments
-    # op.init_argv(args[1])
-    # oppython = op.OpenposePython()
-
-    # Starting OpenPose
-    opWrapper = op.WrapperPython()
-    opWrapper.configure(params)
-    opWrapper.start()
-
-    # Process Image
-    datum = op.Datum()
-    imageToProcess = cv2.imread(args[0].image_path)
-    datum.cvInputData = imageToProcess
-    opWrapper.emplaceAndPop(op.VectorDatum([datum]))
-
-    # Display Image
-    print("Body keypoints: \n" + str(datum.poseKeypoints))
-    print("Face keypoints: \n" + str(datum.faceKeypoints))
-    print("Left hand keypoints: \n" + str(datum.handKeypoints[0]))
-    print("Right hand keypoints: \n" + str(datum.handKeypoints[1]))
-    cv2.imshow("OpenPose 1.7.0 - Tutorial Python API", datum.cvOutputData)
-    cv2.waitKey(0)
-except Exception as e:
-    print(e)
-    sys.exit(-1)
--- a/3cams_3D_v2.py
+++ b/3cams_3D_v2.py
@ -0,0 +1,85 @@
+import glob
+import numpy as np
+import cv2
+import tqdm
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+
+# --- Wczytanie kalibracji ---
+data = np.load("calib_relative_to_A_3cams.npz")
+
+K_A, D_A = data["K_A"], data["D_A"]
+K_B, D_B = data["K_B"], data["D_B"]
+K_C, D_C = data["K_C"], data["D_C"]
+
+R_AA, T_AA = data["R_AA"], data["T_AA"]
+R_BA, T_BA = data["R_BA"], data["T_BA"]
+R_CA, T_CA = data["R_CA"], data["T_CA"]
+
+# reshape translacji
+T_AA = T_AA.reshape(3,1)
+T_BA = T_BA.reshape(3,1)
+T_CA = T_CA.reshape(3,1)
+
+# Kamera A = układ odniesienia
+P_A = K_A @ np.hstack((np.eye(3), np.zeros((3,1))))
+P_B = K_B @ np.hstack((R_BA, T_BA))
+P_C = K_C @ np.hstack((R_CA, T_CA))
+
+
+def triangulate_three_views(pA, pB, pC):
+    pA = pA.reshape(2,1)
+    pB = pB.reshape(2,1)
+    pC = pC.reshape(2,1)
+
+    XAB_h = cv2.triangulatePoints(P_A, P_B, pA, pB)
+    XAB = (XAB_h / XAB_h[3])[:3].reshape(3)
+
+    XAC_h = cv2.triangulatePoints(P_A, P_C, pA, pC)
+    XAC = (XAC_h / XAC_h[3])[:3].reshape(3)
+
+    return (XAB + XAC)/2
+
+
+# --- YOLO Pose ---
+model = YOLO("yolo11x-pose.pt")
+
+skeleton = [
+    [0,1],[0,2],[1,3],[2,4],[0,5],[0,6],
+    [5,7],[7,9],[6,8],[8,10],[5,6],
+    [11,12],[12,14],[14,16],[11,13],[13,15]
+]
+
+points3DList = {}
+
+frames = sorted(glob.glob("video/camA/*.jpg"), key=lambda f: int(__import__("re").search(r"\d+", f).group()))
+
+for frame in tqdm.tqdm(frames):
+    name = frame.replace('video/camA\\',"")
+    imgA = cv2.imread(f"video/camA/{name}")
+    imgB = cv2.imread(f"video/camB/{name}")
+    imgC = cv2.imread(f"video/camC/{name}")
+
+    rA = model(imgA, verbose=False)[0]
+    rB = model(imgB, verbose=False)[0]
+    rC = model(imgC, verbose=False)[0]
+
+    if len(rA.keypoints.xy)==0: continue
+    if len(rB.keypoints.xy)==0: continue
+    if len(rC.keypoints.xy)==0: continue
+
+    kpA = rA.keypoints.xy[0].cpu().numpy()
+    kpB = rB.keypoints.xy[0].cpu().numpy()
+    kpC = rC.keypoints.xy[0].cpu().numpy()
+
+    pts = []
+    for i in range(kpA.shape[0]):
+        X = triangulate_three_views(kpA[i], kpB[i], kpC[i])
+        pts.append(X)
+
+    pts = np.array(pts)
+    points3DList[name] = pts
+
+import pickle
+with open("replay_tpose.pkl", "wb") as f:
+    pickle.dump(points3DList, f)
--- a/3cams_3d.py
+++ b/3cams_3d.py
@ -0,0 +1,112 @@
+import glob
+
+import numpy as np
+import cv2
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+
+# --- Wczytanie kalibracji ---
+data = np.load("calibration_3cams_1.npz")
+
+K1, D1 = data["K1"], data["D1"]
+K2, D2 = data["K2"], data["D2"]
+K3, D3 = data["K3"], data["D3"]
+R12, T12 = data["R12"], data["T12"]
+R13, T13 = data["R13"], data["T13"]
+
+# Naprawa wymiarów translacji
+T12 = T12.reshape(3,1)
+T13 = T13.reshape(3,1)
+
+# Kamera 1 = układ odniesienia
+P1 = K1 @ np.hstack((np.eye(3), np.zeros((3,1))))
+P2 = K2 @ np.hstack((R12, T12))
+P3 = K3 @ np.hstack((R13, T13))
+
+# --- Funkcja triangulacji 3D z trzech kamer ---
+def triangulate_three_views(p1, p2, p3):
+    """
+    Triangulacja 3D z trzech kamer metodą OpenCV + średnia dla stabilności
+    p1, p2, p3: punkty w pikselach (2,)
+    """
+    # Zamiana na odpowiedni kształt (2,1)
+    p1 = p1.reshape(2,1)
+    p2 = p2.reshape(2,1)
+    p3 = p3.reshape(2,1)
+
+    # Triangulacja pary kamer 1-2
+    X12_h = cv2.triangulatePoints(P1, P2, p1, p2)
+    X12 = (X12_h / X12_h[3])[:3].reshape(3)
+
+    # Triangulacja pary kamer 1-3
+    X13_h = cv2.triangulatePoints(P1, P3, p1, p3)
+    X13 = (X13_h / X13_h[3])[:3].reshape(3)
+
+    # Średnia dla większej stabilności
+    X_avg = (X12 + X13) / 2
+    return X_avg
+
+# --- Wczytanie YOLOv11 Pose ---
+model = YOLO("yolo11x-pose.pt")
+
+skeleton = [
+    [0,1],[0,2],[1,3],[2,4],[0,5],[0,6],
+    [5,7],[7,9],[6,8],[8,10],[5,6],
+    [11,12],[12,14],[14,16],[11,13],[13,15]
+]
+
+plt.ion()  # włączenie trybu interaktywnego
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+# Tworzymy początkowy wykres punktów
+points_plot = ax.scatter([], [], [], c='r', marker='o', s=50)
+lines_plot = [ax.plot([0,0],[0,0],[0,0], c='b')[0] for _ in skeleton]
+
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.view_init(elev=20, azim=-60)
+
+points3DList = {}
+
+for i in sorted(glob.glob("video/camA/*.jpg"), key=lambda f: int(__import__("re").search(r"\d+", f).group())):
+    # Zakładamy, że mamy 3 obrazy z 3 kamer
+    data = i.replace(f'video/camA\\', "")
+    img1 = cv2.imread(f"video/camA/{data}")
+    img2 = cv2.imread(f"video/camB/{data}")
+    img3 = cv2.imread(f"video/camC/{data}")
+
+    # Predykcja keypoints
+    results1 = model(img1, verbose=False)[0]
+    results2 = model(img2, verbose=False)[0]
+    results3 = model(img3, verbose=False)[0]
+
+    # Zakładamy jedną osobę na scenie
+    if len(results1.keypoints.xy) == 0: continue
+    if len(results2.keypoints.xy) == 0: continue
+    if len(results3.keypoints.xy) == 0: continue
+
+    yolo_cam1 = results1.keypoints.xy[0].cpu().numpy()  # shape (17,2)
+    yolo_cam2 = results2.keypoints.xy[0].cpu().numpy()
+    yolo_cam3 = results3.keypoints.xy[0].cpu().numpy()
+
+    # --- Triangulacja wszystkich punktów ---
+    points3D = []
+
+    for i in range(yolo_cam1.shape[0]):  # 17 punktów COCO
+        p1 = yolo_cam1[i]
+        p2 = yolo_cam2[i]
+        p3 = yolo_cam3[i]
+
+        X = triangulate_three_views(p1, p2, p3)
+        points3D.append(X)
+
+    points3D = np.array(points3D)
+    print(points3D)
+    points3DList[data] = points3D
+
+import pickle
+
+with open("replay_tpose.pkl", "wb") as f:
+    pickle.dump(points3DList, f)
--- a/3ddisplay_replay.py
+++ b/3ddisplay_replay.py
@ -0,0 +1,57 @@
+import pickle
+
+from matplotlib import pyplot as plt
+
+with open("replay_xyz.pkl", "rb") as f:
+    points3DList = pickle.load(f)
+
+skeleton = [
+    [0, 1], [0, 2],       # nose -> eyes
+    [1, 3], [2, 4],       # eyes -> ears
+    # [0, 5], [0, 6],       # nose -> shoulders
+    [5, 7], [7, 9],       # left arm
+    [6, 8], [8, 10],      # right arm
+    [5, 6],                # shoulders
+    [5, 11], [6, 12],      # shoulders -> hips
+    [11, 12],              # hips
+    [11, 13], [13, 15],    # left leg
+    [12, 14], [14, 16]     # right leg
+]
+
+plt.ion()  # włączenie trybu interaktywnego
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+# Tworzymy początkowy wykres punktów
+points_plot = ax.scatter([], [], [], c='r', marker='o', s=50)
+lines_plot = [ax.plot([0,0],[0,0],[0,0], c='b')[0] for _ in skeleton]
+
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.set_xlim(-0.6, 0.4)
+ax.set_ylim(1.2, 2.2)
+ax.set_zlim(-0.5, 1.1)
+ax.view_init(elev=20, azim=-60)
+
+i = 0
+
+for points3Dkey in points3DList:
+    points3D = points3DList[points3Dkey]
+    print("3D points:\n", points3D)
+
+    # --- Wizualizacja 3D ---d
+    X = points3D[:,0] - 0.25
+    Z = -points3D[:,1] + 0.5
+    Y = points3D[:,2]
+
+    points_plot._offsets3d = (X, Y, Z)
+
+    # Aktualizacja linii (szkielet)
+    for idx, (i, j) in enumerate(skeleton):
+        lines_plot[idx].set_data([X[i], X[j]], [Y[i], Y[j]])
+        lines_plot[idx].set_3d_properties([Z[i], Z[j]])
+
+    fig.canvas.draw()
+    fig.canvas.flush_events()
+    plt.pause(0.01)
--- a/3ddisplay_replay_smoothed.py
+++ b/3ddisplay_replay_smoothed.py
@ -0,0 +1,58 @@
+from scipy.signal import savgol_filter
+import numpy as np
+import pickle
+from matplotlib import pyplot as plt
+
+with open("replay_xyz.pkl", "rb") as f:
+    points3DList = pickle.load(f)
+
+skeleton = [
+    [0, 1], [0, 2],
+    [1, 3], [2, 4],
+    [5, 7], [7, 9],
+    [6, 8], [8, 10],
+    [5, 6],
+    [5, 11], [6, 12],
+    [11, 12],
+    [11, 13], [13, 15],
+    [12, 14], [14, 16]
+]
+
+keys_sorted = sorted(points3DList.keys())
+points_sequence = np.array([points3DList[k] for k in keys_sorted])  # (frames, points, 3)
+
+# --- Filtr Savitzky-Golaya ---
+window_length = 7  # musi być nieparzyste
+polyorder = 2
+smoothed_sequence = savgol_filter(points_sequence, window_length=window_length,
+                                  polyorder=polyorder, axis=0, mode='nearest')
+
+plt.ion()
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+points_plot = ax.scatter([], [], [], c='r', marker='o', s=50)
+lines_plot = [ax.plot([0,0],[0,0],[0,0], c='b')[0] for _ in skeleton]
+
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.set_xlim(-0.6, 0.4)
+ax.set_ylim(1.2, 2.2)
+ax.set_zlim(-0.5, 1.1)
+ax.view_init(elev=20, azim=-60)
+
+for frame_points in smoothed_sequence:
+    X = frame_points[:,0] - 0.25
+    Z = -frame_points[:,1] + 0.5
+    Y = frame_points[:,2]
+
+    points_plot._offsets3d = (X, Y, Z)
+
+    for idx, (i, j) in enumerate(skeleton):
+        lines_plot[idx].set_data([X[i], X[j]], [Y[i], Y[j]])
+        lines_plot[idx].set_3d_properties([Z[i], Z[j]])
+
+    fig.canvas.draw()
+    fig.canvas.flush_events()
+    plt.pause(0.001)
--- a/pycache/draw.cpython-312.pyc
+++ b/pycache/draw.cpython-312.pyc
--- a/pycache/filter.cpython-312.pyc
+++ b/pycache/filter.cpython-312.pyc
--- a/pycache/utils.cpython-312.pyc
+++ b/pycache/utils.cpython-312.pyc
--- a/body3d.py
+++ b/body3d.py
@ -1,555 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-import mimetypes
-import os
-import time
-from argparse import ArgumentParser
-from functools import partial
-
-import cv2
-import json_tricks as json
-import mmcv
-import mmengine
-import numpy as np
-from mmengine.logging import print_log
-
-from mmpose.apis import (_track_by_iou, _track_by_oks,
-                         convert_keypoint_definition, extract_pose_sequence,
-                         inference_pose_lifter_model, inference_topdown,
-                         init_model)
-from mmpose.models.pose_estimators import PoseLifter
-from mmpose.models.pose_estimators.topdown import TopdownPoseEstimator
-from mmpose.registry import VISUALIZERS
-from mmpose.structures import (PoseDataSample, merge_data_samples,
-                               split_instances)
-from mmpose.utils import adapt_mmdet_pipeline
-
-try:
-    from mmdet.apis import inference_detector, init_detector
-    has_mmdet = True
-except (ImportError, ModuleNotFoundError):
-    has_mmdet = False
-
-
-def parse_args():
-    parser = ArgumentParser()
-    parser.add_argument('--det_config', default="mmpose/demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py", help='Config file for detection')
-    parser.add_argument('--det_checkpoint', default="rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth", help='Checkpoint file for detection')
-    parser.add_argument(
-        '--pose_estimator_config',
-        type=str,
-        default="mmpose/configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py",
-        help='Config file for the 1st stage 2D pose estimator')
-    parser.add_argument(
-        '--pose_estimator_checkpoint',
-        type=str,
-        default="rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth",
-        help='Checkpoint file for the 1st stage 2D pose estimator')
-    parser.add_argument(
-        '--pose_lifter_config',
-        default="mmpose/configs/body_3d_keypoint/video_pose_lift/h36m/video-pose-lift_tcn-243frm-supv-cpn-ft_8xb128-200e_h36m.py",
-        help='Config file for the 2nd stage pose lifter model')
-    parser.add_argument(
-        '--pose_lifter_checkpoint',
-        default="videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth",
-        help='Checkpoint file for the 2nd stage pose lifter model')
-    parser.add_argument('--input', type=str, default='webcam', help='Video path')
-    parser.add_argument(
-        '--show',
-        action='store_true',
-        default=True,
-        help='Whether to show visualizations')
-    parser.add_argument(
-        '--disable-rebase-keypoint',
-        action='store_true',
-        default=False,
-        help='Whether to disable rebasing the predicted 3D pose so its '
-        'lowest keypoint has a height of 0 (landing on the ground). Rebase '
-        'is useful for visualization when the model do not predict the '
-        'global position of the 3D pose.')
-    parser.add_argument(
-        '--disable-norm-pose-2d',
-        action='store_true',
-        default=False,
-        help='Whether to scale the bbox (along with the 2D pose) to the '
-        'average bbox scale of the dataset, and move the bbox (along with the '
-        '2D pose) to the average bbox center of the dataset. This is useful '
-        'when bbox is small, especially in multi-person scenarios.')
-    parser.add_argument(
-        '--num-instances',
-        type=int,
-        default=1,
-        help='The number of 3D poses to be visualized in every frame. If '
-        'less than 0, it will be set to the number of pose results in the '
-        'first frame.')
-    parser.add_argument(
-        '--output-root',
-        type=str,
-        default='',
-        help='Root of the output video file. '
-        'Default not saving the visualization video.')
-    parser.add_argument(
-        '--save-predictions',
-        action='store_true',
-        default=False,
-        help='Whether to save predicted results')
-    parser.add_argument(
-        '--device', default='cuda:0', help='Device used for inference')
-    parser.add_argument(
-        '--det-cat-id',
-        type=int,
-        default=0,
-        help='Category id for bounding box detection model')
-    parser.add_argument(
-        '--bbox-thr',
-        type=float,
-        default=0.3,
-        help='Bounding box score threshold')
-    parser.add_argument('--kpt-thr', type=float, default=0.3)
-    parser.add_argument(
-        '--use-oks-tracking', action='store_true', help='Using OKS tracking')
-    parser.add_argument(
-        '--tracking-thr', type=float, default=0.3, help='Tracking threshold')
-    parser.add_argument(
-        '--show-interval', type=int, default=0, help='Sleep seconds per frame')
-    parser.add_argument(
-        '--thickness',
-        type=int,
-        default=1,
-        help='Link thickness for visualization')
-    parser.add_argument(
-        '--radius',
-        type=int,
-        default=3,
-        help='Keypoint radius for visualization')
-    parser.add_argument(
-        '--online',
-        action='store_true',
-        default=False,
-        help='Inference mode. If set to True, can not use future frame'
-        'information when using multi frames for inference in the 2D pose'
-        'detection stage. Default: False.')
-
-    args = parser.parse_args()
-    return args
-
-
-def process_one_image(args, detector, frame, frame_idx, pose_estimator,
-                      pose_est_results_last, pose_est_results_list, next_id,
-                      pose_lifter, visualize_frame, visualizer):
-    """Visualize detected and predicted keypoints of one image.
-
-    Pipeline of this function:
-
-                              frame
-                                |
-                                V
-                        +-----------------+
-                        |     detector    |
-                        +-----------------+
-                                |  det_result
-                                V
-                        +-----------------+
-                        |  pose_estimator |
-                        +-----------------+
-                                |  pose_est_results
-                                V
-            +--------------------------------------------+
-            |  convert 2d kpts into pose-lifting format  |
-            +--------------------------------------------+
-                                |  pose_est_results_list
-                                V
-                    +-----------------------+
-                    | extract_pose_sequence |
-                    +-----------------------+
-                                |  pose_seq_2d
-                                V
-                         +-------------+
-                         | pose_lifter |
-                         +-------------+
-                                |  pose_lift_results
-                                V
-                       +-----------------+
-                       | post-processing |
-                       +-----------------+
-                                |  pred_3d_data_samples
-                                V
-                         +------------+
-                         | visualizer |
-                         +------------+
-
-    Args:
-        args (Argument): Custom command-line arguments.
-        detector (mmdet.BaseDetector): The mmdet detector.
-        frame (np.ndarray): The image frame read from input image or video.
-        frame_idx (int): The index of current frame.
-        pose_estimator (TopdownPoseEstimator): The pose estimator for 2d pose.
-        pose_est_results_last (list(PoseDataSample)): The results of pose
-            estimation from the last frame for tracking instances.
-        pose_est_results_list (list(list(PoseDataSample))): The list of all
-            pose estimation results converted by
-            ``convert_keypoint_definition`` from previous frames. In
-            pose-lifting stage it is used to obtain the 2d estimation sequence.
-        next_id (int): The next track id to be used.
-        pose_lifter (PoseLifter): The pose-lifter for estimating 3d pose.
-        visualize_frame (np.ndarray): The image for drawing the results on.
-        visualizer (Visualizer): The visualizer for visualizing the 2d and 3d
-            pose estimation results.
-
-    Returns:
-        pose_est_results (list(PoseDataSample)): The pose estimation result of
-            the current frame.
-        pose_est_results_list (list(list(PoseDataSample))): The list of all
-            converted pose estimation results until the current frame.
-        pred_3d_instances (InstanceData): The result of pose-lifting.
-            Specifically, the predicted keypoints and scores are saved at
-            ``pred_3d_instances.keypoints`` and
-            ``pred_3d_instances.keypoint_scores``.
-        next_id (int): The next track id to be used.
-    """
-    pose_lift_dataset = pose_lifter.cfg.test_dataloader.dataset
-    pose_lift_dataset_name = pose_lifter.dataset_meta['dataset_name']
-
-    # First stage: conduct 2D pose detection in a Topdown manner
-    # use detector to obtain person bounding boxes
-    det_result = inference_detector(detector, frame)
-    pred_instance = det_result.pred_instances.cpu().numpy()
-
-    # filter out the person instances with category and bbox threshold
-    # e.g. 0 for person in COCO
-    bboxes = pred_instance.bboxes
-    bboxes = bboxes[np.logical_and(pred_instance.labels == args.det_cat_id,
-                                   pred_instance.scores > args.bbox_thr)]
-
-    # estimate pose results for current image
-    pose_est_results = inference_topdown(pose_estimator, frame, bboxes)
-
-    if args.use_oks_tracking:
-        _track = partial(_track_by_oks)
-    else:
-        _track = _track_by_iou
-
-    pose_det_dataset_name = pose_estimator.dataset_meta['dataset_name']
-    pose_est_results_converted = []
-
-    # convert 2d pose estimation results into the format for pose-lifting
-    # such as changing the keypoint order, flipping the keypoint, etc.
-    for i, data_sample in enumerate(pose_est_results):
-        pred_instances = data_sample.pred_instances.cpu().numpy()
-        keypoints = pred_instances.keypoints
-        # calculate area and bbox
-        if 'bboxes' in pred_instances:
-            areas = np.array([(bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
-                              for bbox in pred_instances.bboxes])
-            pose_est_results[i].pred_instances.set_field(areas, 'areas')
-        else:
-            areas, bboxes = [], []
-            for keypoint in keypoints:
-                xmin = np.min(keypoint[:, 0][keypoint[:, 0] > 0], initial=1e10)
-                xmax = np.max(keypoint[:, 0])
-                ymin = np.min(keypoint[:, 1][keypoint[:, 1] > 0], initial=1e10)
-                ymax = np.max(keypoint[:, 1])
-                areas.append((xmax - xmin) * (ymax - ymin))
-                bboxes.append([xmin, ymin, xmax, ymax])
-            pose_est_results[i].pred_instances.areas = np.array(areas)
-            pose_est_results[i].pred_instances.bboxes = np.array(bboxes)
-
-        # track id
-        track_id, pose_est_results_last, _ = _track(data_sample,
-                                                    pose_est_results_last,
-                                                    args.tracking_thr)
-        if track_id == -1:
-            if np.count_nonzero(keypoints[:, :, 1]) >= 3:
-                track_id = next_id
-                next_id += 1
-            else:
-                # If the number of keypoints detected is small,
-                # delete that person instance.
-                keypoints[:, :, 1] = -10
-                pose_est_results[i].pred_instances.set_field(
-                    keypoints, 'keypoints')
-                pose_est_results[i].pred_instances.set_field(
-                    pred_instances.bboxes * 0, 'bboxes')
-                pose_est_results[i].set_field(pred_instances, 'pred_instances')
-                track_id = -1
-        pose_est_results[i].set_field(track_id, 'track_id')
-
-        # convert keypoints for pose-lifting
-        pose_est_result_converted = PoseDataSample()
-        pose_est_result_converted.set_field(
-            pose_est_results[i].pred_instances.clone(), 'pred_instances')
-        pose_est_result_converted.set_field(
-            pose_est_results[i].gt_instances.clone(), 'gt_instances')
-        keypoints = convert_keypoint_definition(keypoints,
-                                                pose_det_dataset_name,
-                                                pose_lift_dataset_name)
-        pose_est_result_converted.pred_instances.set_field(
-            keypoints, 'keypoints')
-        pose_est_result_converted.set_field(pose_est_results[i].track_id,
-                                            'track_id')
-        pose_est_results_converted.append(pose_est_result_converted)
-
-    pose_est_results_list.append(pose_est_results_converted.copy())
-
-    # Second stage: Pose lifting
-    # extract and pad input pose2d sequence
-    pose_seq_2d = extract_pose_sequence(
-        pose_est_results_list,
-        frame_idx=frame_idx,
-        causal=pose_lift_dataset.get('causal', False),
-        seq_len=pose_lift_dataset.get('seq_len', 1),
-        step=pose_lift_dataset.get('seq_step', 1))
-
-    # conduct 2D-to-3D pose lifting
-    norm_pose_2d = not args.disable_norm_pose_2d
-    pose_lift_results = inference_pose_lifter_model(
-        pose_lifter,
-        pose_seq_2d,
-        image_size=visualize_frame.shape[:2],
-        norm_pose_2d=norm_pose_2d)
-
-    # post-processing
-    for idx, pose_lift_result in enumerate(pose_lift_results):
-        pose_lift_result.track_id = pose_est_results[idx].get('track_id', 1e4)
-
-        pred_instances = pose_lift_result.pred_instances
-        keypoints = pred_instances.keypoints
-        keypoint_scores = pred_instances.keypoint_scores
-        if keypoint_scores.ndim == 3:
-            keypoint_scores = np.squeeze(keypoint_scores, axis=1)
-            pose_lift_results[
-                idx].pred_instances.keypoint_scores = keypoint_scores
-        if keypoints.ndim == 4:
-            keypoints = np.squeeze(keypoints, axis=1)
-
-        keypoints = keypoints[..., [0, 2, 1]]
-        keypoints[..., 0] = -keypoints[..., 0]
-        keypoints[..., 2] = -keypoints[..., 2]
-
-        # rebase height (z-axis)
-        if not args.disable_rebase_keypoint:
-            keypoints[..., 2] -= np.min(
-                keypoints[..., 2], axis=-1, keepdims=True)
-
-        pose_lift_results[idx].pred_instances.keypoints = keypoints
-
-    pose_lift_results = sorted(
-        pose_lift_results, key=lambda x: x.get('track_id', 1e4))
-
-    pred_3d_data_samples = merge_data_samples(pose_lift_results)
-    det_data_sample = merge_data_samples(pose_est_results)
-    pred_3d_instances = pred_3d_data_samples.get('pred_instances', None)
-
-    if args.num_instances < 0:
-        args.num_instances = len(pose_lift_results)
-
-    # Visualization
-    if visualizer is not None:
-        visualizer.add_datasample(
-            'result',
-            visualize_frame,
-            data_sample=pred_3d_data_samples,
-            det_data_sample=det_data_sample,
-            draw_gt=False,
-            dataset_2d=pose_det_dataset_name,
-            dataset_3d=pose_lift_dataset_name,
-            show=args.show,
-            draw_bbox=True,
-            kpt_thr=args.kpt_thr,
-            num_instances=args.num_instances,
-            wait_time=args.show_interval)
-
-    return pose_est_results, pose_est_results_list, pred_3d_instances, next_id
-
-
-def main():
-    assert has_mmdet, 'Please install mmdet to run the demo.'
-
-    args = parse_args()
-
-    assert args.show or (args.output_root != '')
-    assert args.input != ''
-    assert args.det_config is not None
-    assert args.det_checkpoint is not None
-
-    detector = init_detector(
-        args.det_config, args.det_checkpoint, device=args.device.lower())
-    detector.cfg = adapt_mmdet_pipeline(detector.cfg)
-
-    pose_estimator = init_model(
-        args.pose_estimator_config,
-        args.pose_estimator_checkpoint,
-        device=args.device.lower())
-
-    assert isinstance(pose_estimator, TopdownPoseEstimator), 'Only "TopDown"' \
-        'model is supported for the 1st stage (2D pose detection)'
-
-    det_kpt_color = pose_estimator.dataset_meta.get('keypoint_colors', None)
-    det_dataset_skeleton = pose_estimator.dataset_meta.get(
-        'skeleton_links', None)
-    det_dataset_link_color = pose_estimator.dataset_meta.get(
-        'skeleton_link_colors', None)
-
-    pose_lifter = init_model(
-        args.pose_lifter_config,
-        args.pose_lifter_checkpoint,
-        device=args.device.lower())
-
-    assert isinstance(pose_lifter, PoseLifter), \
-        'Only "PoseLifter" model is supported for the 2nd stage ' \
-        '(2D-to-3D lifting)'
-
-    pose_lifter.cfg.visualizer.radius = args.radius
-    pose_lifter.cfg.visualizer.line_width = args.thickness
-    pose_lifter.cfg.visualizer.det_kpt_color = det_kpt_color
-    pose_lifter.cfg.visualizer.det_dataset_skeleton = det_dataset_skeleton
-    pose_lifter.cfg.visualizer.det_dataset_link_color = det_dataset_link_color
-    visualizer = VISUALIZERS.build(pose_lifter.cfg.visualizer)
-
-    # the dataset_meta is loaded from the checkpoint
-    visualizer.set_dataset_meta(pose_lifter.dataset_meta)
-
-    if args.input == 'webcam':
-        input_type = 'webcam'
-    else:
-        input_type = mimetypes.guess_type(args.input)[0].split('/')[0]
-
-    if args.output_root == '':
-        save_output = False
-    else:
-        mmengine.mkdir_or_exist(args.output_root)
-        output_file = os.path.join(args.output_root,
-                                   os.path.basename(args.input))
-        if args.input == 'webcam':
-            output_file += '.mp4'
-        save_output = True
-
-    if args.save_predictions:
-        assert args.output_root != ''
-        args.pred_save_path = f'{args.output_root}/results_' \
-            f'{os.path.splitext(os.path.basename(args.input))[0]}.json'
-
-    if save_output:
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-
-    pose_est_results_list = []
-    pred_instances_list = []
-    if input_type == 'image':
-        frame = mmcv.imread(args.input, channel_order='rgb')
-        _, _, pred_3d_instances, _ = process_one_image(
-            args=args,
-            detector=detector,
-            frame=frame,
-            frame_idx=0,
-            pose_estimator=pose_estimator,
-            pose_est_results_last=[],
-            pose_est_results_list=pose_est_results_list,
-            next_id=0,
-            pose_lifter=pose_lifter,
-            visualize_frame=frame,
-            visualizer=visualizer)
-
-        if args.save_predictions:
-            # save prediction results
-            pred_instances_list = split_instances(pred_3d_instances)
-
-        if save_output:
-            frame_vis = visualizer.get_image()
-            mmcv.imwrite(mmcv.rgb2bgr(frame_vis), output_file)
-
-    elif input_type in ['webcam', 'video']:
-        next_id = 0
-        pose_est_results = []
-
-        if args.input == 'webcam':
-            video = cv2.VideoCapture(0)
-        else:
-            video = cv2.VideoCapture(args.input)
-
-        (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
-        if int(major_ver) < 3:
-            fps = video.get(cv2.cv.CV_CAP_PROP_FPS)
-        else:
-            fps = video.get(cv2.CAP_PROP_FPS)
-
-        video_writer = None
-        frame_idx = 0
-
-        while video.isOpened():
-            success, frame = video.read()
-            frame_idx += 1
-
-            if not success:
-                break
-
-            pose_est_results_last = pose_est_results
-
-            # First stage: 2D pose detection
-            # make person results for current image
-            (pose_est_results, pose_est_results_list, pred_3d_instances,
-             next_id) = process_one_image(
-                 args=args,
-                 detector=detector,
-                 frame=frame,
-                 frame_idx=frame_idx,
-                 pose_estimator=pose_estimator,
-                 pose_est_results_last=pose_est_results_last,
-                 pose_est_results_list=pose_est_results_list,
-                 next_id=next_id,
-                 pose_lifter=pose_lifter,
-                 visualize_frame=mmcv.bgr2rgb(frame),
-                 visualizer=visualizer)
-
-            if args.save_predictions:
-                # save prediction results
-                pred_instances_list.append(
-                    dict(
-                        frame_id=frame_idx,
-                        instances=split_instances(pred_3d_instances)))
-
-            if save_output:
-                frame_vis = visualizer.get_image()
-                if video_writer is None:
-                    # the size of the image with visualization may vary
-                    # depending on the presence of heatmaps
-                    video_writer = cv2.VideoWriter(output_file, fourcc, fps,
-                                                   (frame_vis.shape[1],
-                                                    frame_vis.shape[0]))
-
-                video_writer.write(mmcv.rgb2bgr(frame_vis))
-
-            if args.show:
-                # press ESC to exit
-                if cv2.waitKey(5) & 0xFF == 27:
-                    break
-                time.sleep(args.show_interval)
-
-        video.release()
-
-        if video_writer:
-            video_writer.release()
-    else:
-        args.save_predictions = False
-        raise ValueError(
-            f'file {os.path.basename(args.input)} has invalid format.')
-
-    if args.save_predictions:
-        with open(args.pred_save_path, 'w') as f:
-            json.dump(
-                dict(
-                    meta_info=pose_lifter.dataset_meta,
-                    instance_info=pred_instances_list),
-                f,
-                indent='\t')
-        print(f'predictions have been saved at {args.pred_save_path}')
-
-    if save_output:
-        input_type = input_type.replace('webcam', 'video')
-        print_log(
-            f'the output {input_type} has been saved at {output_file}',
-            logger='current',
-            level=logging.INFO)
-
-
-if __name__ == '__main__':
-    main()
--- a/calculate.py
+++ b/calculate.py
@ -0,0 +1,135 @@
+import numpy as np
+
+
+import numpy as np
+
+def angle_between(pkt1, pkt2, pkt3):
+    """
+    Oblicza kąt między trzema punktami w stopniach z zachowaniem znaku.
+    pkt2 jest wierzchołkiem kąta.
+
+    Parameters:
+        pkt1, pkt2, pkt3 : array-like (x, y) lub (x, y, z)
+
+    Returns:
+        Kąt w stopniach (ujemny lub dodatni)
+    """
+
+    pkt1 = np.array(pkt1[:2].cpu().numpy())
+    pkt2 = np.array(pkt2[:2].cpu().numpy())
+    pkt3 = np.array(pkt3[:2].cpu().numpy())
+
+    # wektory względem pkt2
+    a = pkt1 - pkt2
+    b = pkt3 - pkt2
+
+    # iloczyn skalarny i cosinus kąta
+    dot = np.dot(a, b)
+    norm = np.linalg.norm(a) * np.linalg.norm(b)
+    cos_theta = dot / norm
+    cos_theta = np.clip(cos_theta, -1.0, 1.0)
+
+    # kąt bez znaku
+    angle = np.degrees(np.arccos(cos_theta))
+
+    # znak z iloczynu wektorowego (w 2D to skalar = z-component)
+    cross = a[0]*b[1] - a[1]*b[0]
+
+    if cross < 0:
+        angle = -angle
+
+    return angle
+
+def compare_poses(f1, f2):
+    # Odległość euklidesowa
+    l2_dist = np.linalg.norm(f1 - f2)
+
+    # Cosine similarity
+    cos_sim = np.dot(f1, f2) / (np.linalg.norm(f1) * np.linalg.norm(f2) + 1e-6)
+
+    return l2_dist, cos_sim
+
+def compare_poses_boolean(f1, f2):
+    l2, cos_sim = compare_poses(f1, f2)
+
+    return l2 < 1.2 and cos_sim > 0.85
+
+def center(keypoints):
+    mid_hip = (keypoints[11] + keypoints[12]) / 2  # left_hip=11, right_hip=12
+    keypoints = keypoints - mid_hip
+
+    return keypoints
+
+def normalize_pose(keypoints):
+    """
+    keypoints: np.array shape (17, 2) [x,y] dla COCO
+    Zwraca wektor cech odporny na skalę i przesunięcie
+    """
+
+    # 1. translacja -> środek bioder jako początek układu
+    mid_hip = (keypoints[11] + keypoints[12]) / 2  # left_hip=11, right_hip=12
+    keypoints = keypoints - mid_hip
+
+    # 2. normalizacja skali -> odległość między barkami
+    shoulder_dist = np.linalg.norm(keypoints[5] - keypoints[6])  # left_shoulder=5, right_shoulder=6
+    if shoulder_dist > 0:
+        keypoints = keypoints / shoulder_dist
+
+    # 3. definicja segmentów (przykład: łokieć-ramię, nadgarstek-łokieć)
+    limbs = [
+        (5, 7),  # ramię L
+        (7, 9),  # przedramię L
+        (6, 8),  # ramię P
+        (8, 10),  # przedramię P
+        (11, 13),  # udo L
+        (13, 15),  # goleń L
+        (12, 14),  # udo P
+        (14, 16),  # goleń P
+    ]
+
+    # 4. oblicz kąty
+    angles = []
+    for (a, b), (c, d) in zip(limbs[::2], limbs[1::2]):  # np. (ramię, przedramię)
+        v1 = keypoints[b] - keypoints[a]
+        v2 = keypoints[d] - keypoints[c]
+        cos_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + 1e-6)
+        angle = np.arccos(np.clip(cos_angle, -1, 1))
+        angles.append(angle)
+
+    # 5. opcjonalnie: dodać wektory kończyn (znormalizowane)
+    vectors = []
+    for (a, b) in limbs:
+        v = keypoints[b] - keypoints[a]
+        v_norm = v / (np.linalg.norm(v) + 1e-6)
+        vectors.extend(v_norm)
+
+    # finalny wektor cech = kąty + wektory
+    feature_vector = np.concatenate([angles, vectors])
+
+    return feature_vector
+
+
+def denormalize_pose(feature_vector):
+    """
+    feature_vector: wynik normalize_pose
+    Zwraca przybliżone współrzędne keypoints (w układzie znormalizowanym)
+    """
+    # 1. oddziel kąty i wektory
+    angles = feature_vector[:4]
+    vectors_flat = feature_vector[4:]
+    vectors = vectors_flat.reshape(-1, 2)
+
+    # 2. inicjalizacja keypoints
+    keypoints = np.zeros((17, 2))
+
+    # 3. przybliżona rekonstrukcja kończyn
+    limbs = [
+        (5, 7), (7, 9), (6, 8), (8, 10),
+        (11, 13), (13, 15), (12, 14), (14, 16)
+    ]
+
+    for (a, b), v in zip(limbs, vectors):
+        keypoints[b] = keypoints[a] + v  # przybliżona rekonstrukcja
+
+    # 4. punkt startowy (biodra) = (0,0), skalowanie w oryginale trzeba by przywrócić osobno
+    return keypoints
--- a/checkpoint/pretrained_h36m_detectron_coco.bin
+++ b/checkpoint/pretrained_h36m_detectron_coco.bin
--- a/draw.py
+++ b/draw.py
--- a/filter.py
+++ b/filter.py
@ -0,0 +1,93 @@
+from collections import deque
+
+import numpy as np
+
+def filter_moves(moves):
+    newMoves = []
+    lastTime = 0
+
+    ema = EMAFilter(0.2)
+
+    for i, move in enumerate(moves):
+        s = move[0] / 1000
+
+        if i != len(moves) - 1:
+            origS = s
+            s = s - lastTime
+            lastTime = origS
+
+        newMoves.append((s, ema.update(move[1])))
+
+    return newMoves
+
+class MedianFilter:
+    def __init__(self, n_channels=8, window_size=3):
+        self.n = n_channels
+        self.buffers = [deque(maxlen=window_size) for _ in range(n_channels)]
+
+    def update(self, angles_deg):
+        smoothed = []
+        for i, ang in enumerate(angles_deg):
+            self.buffers[i].append(ang)
+            smoothed_ang = np.median(self.buffers[i])
+            smoothed.append(smoothed_ang)
+        return smoothed
+
+class HybridFilter:
+    def __init__(self, alpha=0.7, n_channels=8, median_window=3):
+        self.alpha = alpha
+        self.n = n_channels
+        self.median_window = median_window
+
+        # Bufory do mediany dla każdego kanału
+        self.buffers = [deque(maxlen=median_window) for _ in range(n_channels)]
+
+        # Stan EMA (cos/sin)
+        self.cos_state = [None] * n_channels
+        self.sin_state = [None] * n_channels
+
+    def update(self, angles_deg):
+        smoothed = []
+        for i, ang in enumerate(angles_deg):
+            # wrzucamy do bufora mediany
+            self.buffers[i].append(ang)
+            med = np.median(self.buffers[i])  # filtr medianowy
+
+            ang_rad = np.deg2rad(med)
+            c, s = np.cos(ang_rad), np.sin(ang_rad)
+
+            if self.cos_state[i] is None:
+                self.cos_state[i] = c
+                self.sin_state[i] = s
+            else:
+                self.cos_state[i] = self.alpha * c + (1 - self.alpha) * self.cos_state[i]
+                self.sin_state[i] = self.alpha * s + (1 - self.alpha) * self.sin_state[i]
+
+            smoothed_ang = np.rad2deg(np.arctan2(self.sin_state[i], self.cos_state[i]))
+            smoothed.append(smoothed_ang)
+        return smoothed
+
+class EMAFilter:
+    def __init__(self, alpha=0.2, n_channels=8):
+        self.alpha = alpha
+        self.cos_state = [None] * n_channels
+        self.sin_state = [None] * n_channels
+        self.n = n_channels
+
+    def update(self, angles_deg):
+        smoothed = []
+        for i, ang in enumerate(angles_deg):
+            ang_rad = np.deg2rad(ang)
+
+            c, s = np.cos(ang_rad), np.sin(ang_rad)
+
+            if self.cos_state[i] is None:
+                self.cos_state[i] = c
+                self.sin_state[i] = s
+            else:
+                self.cos_state[i] = self.alpha * c + (1 - self.alpha) * self.cos_state[i]
+                self.sin_state[i] = self.alpha * s + (1 - self.alpha) * self.sin_state[i]
+
+            smoothed_ang = np.rad2deg(np.arctan2(self.sin_state[i], self.cos_state[i]))
+            smoothed.append(smoothed_ang)
+        return smoothed
--- a/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth
+++ b/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth
--- a/humanPoseDetection.ipynb
+++ b/humanPoseDetection.ipynb
--- a/mac.py
+++ b/mac.py
@ -0,0 +1,36 @@
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_pose = mp.solutions.pose
+
+cap = cv2.VideoCapture(0)
+with mp_pose.Pose(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as pose:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      continue
+
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(image)
+
+    # Draw the pose annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+
+cap.release()
--- a/main.py
+++ b/main.py
@ -0,0 +1,219 @@
+import json
+import os
+import pickle
+import socket
+import sys
+
+import torch
+from ultralytics import YOLO
+import cv2
+import time
+
+import poses
+import utils
+from calculate import normalize_pose, compare_poses_boolean
+from draw import draw_new
+from utils import find_closest
+from video_methods import initialize_method
+
+model = YOLO("yolo11s-pose.pt")
+model.to(torch.device('cuda:0'))
+
+if len(sys.argv) == 2:
+    method_type = sys.argv[1]
+else:
+    print("Podaj argument 'cam', albo 'net'.")
+    exit(1)
+
+method = initialize_method(method_type)
+
+do_pose_shot = False
+
+def click_event(event, x, y, flags, param):
+    global do_pose_shot
+
+    if event == cv2.EVENT_LBUTTONDOWN:  # lewy przycisk myszy
+        do_pose_shot = not do_pose_shot
+
+def main():
+    last_time = time.time()
+
+    currTimeIndex = 0
+    currIndex = None
+    currMove = None
+    currStatus = "Zacznij tanczyc"
+
+    mehCount = 0
+    goodCount = 0
+    failCount = 0
+    failRate = 10
+
+    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    server_socket.bind(("0.0.0.0", 13425))
+    server_socket.listen()
+
+    print("czekam na clienta...")
+
+    client_socket, _ = server_socket.accept()
+    print("mam clienta!")
+
+    data = client_socket.recv(1024).decode()
+    if not data.startswith("id_"):
+        client_socket.close()
+        server_socket.close()
+        return
+
+    map = data.replace("id_", "").replace("\n", "").strip()
+    if not os.path.isfile(f'moves_{map}.pkl'):
+        print(map)
+        print("moves_" + map + ".pkl")
+        client_socket.sendall("not_exists".encode())
+        client_socket.close()
+        server_socket.close()
+        return
+
+    moves = []
+
+    with open(f'moves_{map}.pkl', 'rb') as f:  # 'rb' = read binary
+        moves = pickle.load(f)
+
+    startValue = moves[0][0]
+    totalCount = len(moves)
+
+    for i, move in enumerate(moves):
+        moves[i] = ((move[0] - startValue) / 1000, move[1], move[2])
+
+    currIndex = 1
+    currTimeIndex = time.time()
+    deltaTime = time.time()
+    currStatus = f"Zaczoles tanczyc {currIndex}"
+    currMove = moves[0]
+
+
+    doStreak = False
+    streak = 0
+    doing = 0
+    actuallyDoing = False
+
+    while True:
+        doing += 1
+        frame = method.receive_frame()
+        frame = cv2.flip(frame, 1)
+        results = model(frame, verbose=False)
+
+        if not actuallyDoing:
+            client_socket.sendall("start".encode())
+            actuallyDoing = True
+
+        current_time = time.time()
+        delta = current_time - last_time
+        last_time = current_time
+
+        if doing % 30 == 0:
+            if doStreak:
+                streak += 5
+                client_socket.sendall(f"streak_{streak}".encode())
+            else:
+                streak = 0
+                client_socket.sendall(f"streak_0".encode())
+
+        fps = 1 / delta if delta > 0 else float('inf')
+        # print(f"\rDelta: {delta:.4f}s, FPS: {fps:.2f}", end="")
+
+        if len(results) != 0:
+
+            result = results[0]
+            kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
+
+            if kpts is None:
+                continue
+
+            img = frame
+
+            normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
+
+            draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
+            cv2.imshow('you', draw_new(draw * 100 + 100))
+
+            if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
+                mehCount = abs(totalCount - (failCount + goodCount))
+
+                stats = {
+                    "failCount": failCount,
+                    "goodCount": goodCount,
+                    "mehCount": mehCount,
+                    "percentage": (goodCount + (0.85 * mehCount)) / totalCount * 100
+                }
+
+                client_socket.sendall(f"finish_{json.dumps(stats)}".encode())
+                print(
+                    f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.85 * mehCount)) / totalCount * 100}%")
+                cv2.destroyAllWindows()
+                break
+                    # thread = Thread(target=print_animation, args=(moves, False))
+                    # thread.start()
+            else:
+                changed = False
+
+                closest = find_closest(moves, time.time() - currTimeIndex)
+                cv2.imshow('Dots', draw_new(utils.normalize(closest[2]) * 250 + 250))
+
+                if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
+                    currStatus = f"FAIL!"
+                    failCount += 1
+                    doStreak = False
+
+                if compare_poses_boolean(closest[1], normalized):
+                    # delays += (time.time() - deltaTime - moves[0][0]) * 1000
+                    # delaysCount += 1
+
+                    # currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
+                    deltaTime = time.time()
+
+                    currIndex = moves.index(closest) + 1
+                    goodCount += 1
+                    changed = True
+                    doStreak = True
+
+                if not changed and compare_poses_boolean(moves[currIndex][1], normalized):
+                    # delays += (time.time() - deltaTime - moves[0][0]) * 1000
+                    # delaysCount += 1
+
+                    # currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
+                    deltaTime = time.time()
+
+                    changed = True
+
+                    currIndex += 1
+                    goodCount += 1
+                    doStreak = True
+
+                # if do_pose_shot:
+                #     moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))
+                # elif len(moves) != 0:
+                #     with open('moves.pkl', 'wb') as f:  # 'wb' = write binary
+                #         pickle.dump
+                #         (moves, f)
+                #
+                #     exit(1)
+
+                cv2.putText(
+                    img,  # obraz
+                    currStatus,  # tekst
+                    (50, 100),  # pozycja (x, y) lewego dolnego rogu tekstu
+                    cv2.FONT_HERSHEY_SIMPLEX,  # czcionka
+                    1,  # rozmiar (skalowanie)
+                    (0, 0, 255),  # kolor (BGR) - tutaj czerwony
+                    2,  # grubość linii
+                    cv2.LINE_AA  # typ antyaliasingu
+                )
+
+            cv2.imshow('Klatka z kamerki', img)
+            cv2.setMouseCallback('Klatka z kamerki', click_event)
+            cv2.waitKey(1)  # Czekaj na naciśnięcie klawisza
+
+try:
+    while True:
+        main()
+except KeyboardInterrupt:
+    pass
--- a/moves.pkl
+++ b/moves.pkl
--- a/moves_3d.py
+++ b/moves_3d.py
@ -0,0 +1,43 @@
+import cv2
+import mediapipe as mp
+import cv2
+import mediapipe as mp
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_pose = mp.solutions.pose
+
+cap = cv2.VideoCapture(0)
+with mp_pose.Pose(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as pose:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(image)
+
+    print(f"\r{results.pose_world_landmarks[0]}", end="")
+
+    # Draw the pose annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+
+    landmarks = results.pose_world_landmarks.landmark
+
+    print(landmark)
+cap.release()
--- a/moves_3d_mp4.py
+++ b/moves_3d_mp4.py
@ -0,0 +1,60 @@
+import pickle
+import time
+
+import cv2
+from ultralytics import YOLO
+
+from calculate import normalize_pose
+from utils import normalize
+
+# Wczytanie wideo
+cap = cv2.VideoCapture("input.mp4")
+fps = cap.get(cv2.CAP_PROP_FPS)
+width = 1920
+height = 1080
+
+# Ustawienia zapisu wideo
+fourcc = cv2.VideoWriter_fourcc(*"avc1")
+out = cv2.VideoWriter("output.mp4", fourcc, fps, (width, height))
+
+# Wczytanie modelu YOLOv8 Pose
+model = YOLO("yolo11x-pose.pt", verbose=False)  # Twój model pose
+
+moves = []
+started = False
+frame_id = 0
+
+while True:
+    ok, frame = cap.read()
+    if not ok:
+        break
+
+    # Skalowanie do 640x640
+    frame_resized = cv2.resize(frame, (width, height))
+
+    # Wykrywanie poz
+    results = model.predict(frame_resized, verbose=False)
+
+    # Rysowanie punktów 2D
+    for result in results:
+        if result.keypoints is not None and len(result.keypoints.xy) > 0:
+            for keypoint in result.keypoints.xy[0]:  # keypoints[0] bo dla jednej osoby
+                if not started:
+                    frame_id = 0
+                    started = True
+                x, y = keypoint  # współrzędne + confidence
+                x = int(x)
+                y = int(y)
+                cv2.circle(frame_resized, (x, y), 5, (0, 255, 0), -1)  # zielone kropki
+
+            moves.append((frame_id * 1 / fps, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), normalize(result.keypoints.xy.cpu()[0]) * 160 + 250))
+
+    out.write(frame_resized)
+    frame_id += 1
+
+with open('moves2.pkl', 'wb') as f:  # 'wb' = write binary
+    pickle.dump(moves, f)
+
+cap.release()
+out.release()
+print("Zapisano: output.mp4")
--- a/moves_dump.py
+++ b/moves_dump.py
@ -0,0 +1,62 @@
+import json
+import pickle
+import time
+
+import cv2
+import numpy as np
+
+import utils
+from draw import draw_new
+
+# moves = {}
+better_moves = {}
+
+with open('replay_tpose.pkl', 'rb') as f:  # 'rb' = read binary
+    moves = pickle.load(f)
+    movesCopy = {}
+
+    for move in moves:
+        # listx = moves[move].tolist()
+        # print(type(listx))
+
+        # if type(listx) != list:
+        #     listx = listx.tolist()
+
+        movesCopy[move.replace(".jpg", "")] = moves[move].tolist()
+
+    with open("plikv10.json", "w", encoding="utf-8") as f:
+        json.dump(movesCopy, f)
+
+exit(1)
+
+startValue = moves[0][0]
+totalCount = len(moves)
+
+for i, move in enumerate(moves):
+    moves[i] = (move[0] - startValue, move[1], move[2])
+
+    # left_hip = move[2][11]   # Left Hip
+    # right_hip = move[2][12]  # Right Hip
+    # center = (left_hip + right_hip) / 2
+    #
+    # # Normalizacja względem środka ciała
+    # normalized_keypoints = move[2] - center
+    #
+    # better_moves[round((move[0] - startValue) * 1000)] = normalized_keypoints.tolist()
+    #
+    # # scale = utils.distance(move[2][11], move[2][12])
+    # # print(scale)
+    # draw = normalized_keypoints + 200
+
+
+    # Do rysowania (np. przesunięcie na ekran)
+    draw = utils.normalize(move[2])
+    better_moves[round((move[0] - startValue) * 1000)] = draw.tolist()
+
+    # cv2.imshow('you', draw_new(draw))
+    # cv2.waitKey(1)
+    # time.sleep(0.1)
+
+
+with open("plik-234.json", "w", encoding="utf-8") as f:
+    json.dump(better_moves, f)
--- a/moves_dump_2.py
+++ b/moves_dump_2.py
@ -0,0 +1,47 @@
+import json
+import pickle
+import time
+
+import cv2
+import numpy as np
+
+import utils
+from draw import draw_new
+
+moves = []
+better_moves = {}
+
+with open('moves.pkl', 'rb') as f:  # 'rb' = read binary
+    moves = pickle.load(f)
+
+startValue = moves[0][0]
+totalCount = len(moves)
+
+for i, move in enumerate(moves):
+    moves[i] = (move[0] - startValue, move[1], move[2])
+
+    # left_hip = move[2][11]   # Left Hip
+    # right_hip = move[2][12]  # Right Hip
+    # center = (left_hip + right_hip) / 2
+    #
+    # # Normalizacja względem środka ciała
+    # normalized_keypoints = move[2] - center
+    #
+    # better_moves[round((move[0] - startValue) * 1000)] = normalized_keypoints.tolist()
+    #
+    # # scale = utils.distance(move[2][11], move[2][12])
+    # # print(scale)
+    # draw = normalized_keypoints + 200
+
+
+    # Do rysowania (np. przesunięcie na ekran)
+    draw = utils.normalize(move[2])
+    better_moves[round((move[0] - startValue) * 1000)] = draw.tolist()
+
+    # cv2.imshow('you', draw_new(draw))
+    # cv2.waitKey(1)
+    # time.sleep(0.1)
+
+
+with open("plik.json", "w", encoding="utf-8") as f:
+    json.dump(better_moves, f)
--- a/moves_videopose3d.py
+++ b/moves_videopose3d.py
@ -0,0 +1,116 @@
+import cv2
+import torch
+import numpy as np
+from common.model import TemporalModel
+from common.camera import *
+# from common.utils import evaluate
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D  # not strictly needed in newer matplotlib
+import time
+
+# --- 1. Inicjalizacja modelu 3D VideoPose3D ---
+model_3d = TemporalModel(
+    num_joints_in=17,
+    in_features=2,
+    num_joints_out=17,
+    filter_widths=[3,3,3,3],
+    causal=False
+)
+
+chk = torch.load("checkpoint/pretrained_h36m_detectron_coco.bin", map_location='cpu')
+model_3d.load_state_dict(chk, strict=False)
+model_3d.eval()
+
+# --- 2. Inicjalizacja modelu YOLO (pose keypoints) ---
+yolo = YOLO('yolo11s-pose.pt')  # używamy najmniejszej wersji dla szybkości
+
+# --- 3. Wczytanie wideo ---
+cap = cv2.VideoCapture("input.mp4")
+frame_buffer = []
+BUFFER_SIZE = 243  # VideoPose3D potrzebuje sekwencji
+
+fig = plt.figure(figsize=(5, 5))
+ax = fig.add_subplot(111, projection='3d')
+
+# inicjalizacja scatter i linii szkieletu
+scatter = ax.scatter([], [], [], c='r')
+
+skeleton = [ (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6), (0, 7), (7, 8), (8, 9), (7, 12), (12, 13), (13, 14), (7, 10), (10, 11), (11, 12) ]
+
+skeleton_lines = []
+for _ in skeleton:
+    line, = ax.plot([], [], [], c='b')
+    skeleton_lines.append(line)
+
+ax.set_xlim3d(-1, 1)
+ax.set_ylim3d(-1, 1)
+ax.set_zlim3d(0, 2)
+ax.view_init(elev=20, azim=-70)
+plt.ion()
+plt.show()
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # --- 4. Detekcja keypointów z YOLO ---
+    results = yolo(frame)
+    if len(results) == 0 or len(results[0].keypoints.xy) == 0:
+        continue
+
+    # Zakładamy 1 osobę na klatkę (dla uproszczenia)
+    keypoints = results[0].keypoints.xy[0]  # shape [17, 2]
+    keypoints = np.array(keypoints)
+
+    # Normalizacja do [0,1] (opcjonalnie zależnie od VideoPose3D)
+    keypoints[:, 0] /= frame.shape[1]
+    keypoints[:, 1] /= frame.shape[0]
+
+    frame_buffer.append(keypoints)
+
+    # --- 5. Jeśli mamy pełną sekwencję, predykcja 3D ---
+    skeleton = [
+        (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6),
+        (0, 7), (7, 8), (8, 9), (7, 12), (12, 13), (13, 14),
+        (7, 10), (10, 11), (11, 12)
+    ]
+
+    # --- after getting pred_3d ---
+    if len(frame_buffer) == BUFFER_SIZE:
+        seq_2d = torch.tensor(np.array(frame_buffer)).unsqueeze(0).float()
+        with torch.no_grad():
+            pred_3d = model_3d(seq_2d)
+
+        pose_3d = pred_3d[0, -1].numpy()  # [17,3]
+
+        # --- 2D overlay ---
+        # for i, kp in enumerate(frame_buffer[-1]):
+        #     x, y = int(kp[0] * frame.shape[1]), int(kp[1] * frame.shape[0])
+        #     cv2.circle(frame, (x, y), 5, (0, 255, 0), -1)
+        # cv2.imshow("2D Pose", frame)
+        # cv2.waitKey(1)
+
+        pose_3d = pose_3d[:, [0, 2, 1]]  # X, Z, Y
+        pose_3d[:, 2] *= -1
+
+        # --- 3D update ---
+        xs, ys, zs = pose_3d[:, 0], pose_3d[:, 1], pose_3d[:, 2]
+
+        # update scatter
+        scatter._offsets3d = (xs, ys, zs)
+
+        # update skeleton lines
+        for idx, (a, b) in enumerate(skeleton):
+            skeleton_lines[idx].set_data([xs[a], xs[b]], [ys[a], ys[b]])
+            skeleton_lines[idx].set_3d_properties([zs[a], zs[b]])
+
+        plt.draw()
+        plt.pause(0.001)
+        print(pose_3d.tolist())
+
+        frame_buffer.pop(0)
+
+cap.release()
+cv2.destroyAllWindows()
--- a/ploting.py
+++ b/ploting.py
@ -0,0 +1,26 @@
+import matplotlib.pyplot as plt
+import queue
+
+data_queue = queue.Queue()
+
+x_data, y_data = [], []
+
+fig, ax = plt.subplots()
+line, = ax.plot([], [], 'r-')
+
+def init():
+    ax.set_xlim(0, 100)
+    ax.set_ylim(0, 10)
+    return line,
+
+def update(frame):
+    # sprawdzamy, czy są nowe dane w kolejce
+    while not data_queue.empty():
+        value = data_queue.get()
+        x_data.append(len(x_data))
+        y_data.append(value)
+        if len(x_data) > 100:
+            x_data.pop(0)
+            y_data.pop(0)
+    line.set_data(x_data, y_data)
+    return line,
--- a/poses.py
+++ b/poses.py
@ -0,0 +1 @@
+t_pose = [   0.079178   ,   0.1963  ,  0.098774 ,   0.033032 ,    0.99646 ,    0.08401 ,    0.99999,   0.0049546,    -0.99134 ,    0.13132,    -0.99791,   -0.064541,    0.063086,     0.99801,    -0.03562,     0.99936,   -0.012939,     0.99992,     0.02004,      0.9998]
--- a/receive_images.py
+++ b/receive_images.py
@ -0,0 +1,48 @@
+import socket
+import cv2
+import numpy as np
+import struct
+
+HOST = "0.0.0.0"   # nasłuchuj na wszystkich interfejsach
+PORT = 9999
+
+server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+server_socket.bind((HOST, PORT))
+server_socket.listen(1)
+
+print("Serwer nasłuchuje na port", PORT)
+conn, addr = server_socket.accept()
+print("Połączono z:", addr)
+
+data = b""
+payload_size = struct.calcsize("Q")  # 8 bajtów na długość ramki
+
+while True:
+    while len(data) < payload_size:
+        packet = conn.recv(4096)
+        if not packet:
+            break
+        data += packet
+    if not data:
+        break
+
+    packed_msg_size = data[:payload_size]
+    data = data[payload_size:]
+    msg_size = struct.unpack("Q", packed_msg_size)[0]
+
+    while len(data) < msg_size:
+        data += conn.recv(4096)
+
+    frame_data = data[:msg_size]
+    data = data[msg_size:]
+
+    frame = np.frombuffer(frame_data, dtype=np.uint8)
+    frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)
+
+    cv2.imshow("Odebrany obraz", frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+conn.close()
+server_socket.close()
+cv2.destroyAllWindows()
--- a/receiver.py
+++ b/receiver.py
@ -0,0 +1,62 @@
+import socket
+import struct
+import numpy as np
+import cv2
+import time
+
+HOST = '0.0.0.0'
+PORT = 9999
+
+sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+sock.bind((HOST, PORT))
+sock.listen(1)
+conn, addr = sock.accept()
+print(f"Connected by {addr}")
+
+total_bytes_received = 0
+start_time = time.time()
+
+
+def recvall(sock, n):
+    data = b''
+    while len(data) < n:
+        packet = sock.recv(n - len(data))
+        if not packet:
+            return None
+        data += packet
+    return data
+
+
+try:
+    while True:
+        # Odbiór długości
+        packed_len = recvall(conn, 4)
+        if not packed_len:
+            break
+        length = struct.unpack('!I', packed_len)[0]
+
+        # Odbiór danych
+        data = recvall(conn, length)
+        if not data:
+            break
+
+        total_bytes_received += length
+
+        # Dekodowanie JPEG
+        img_array = np.frombuffer(data, dtype=np.uint8)
+        frame = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+
+        cv2.imshow("Stream", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+        elapsed = time.time() - start_time
+        if elapsed >= 1.0:
+            print(f"Download speed: {total_bytes_received * 8 / 1e6:.2f} Mbps")
+            total_bytes_received = 0
+            start_time = time.time()
+
+finally:
+    conn.close()
+    sock.close()
+    cv2.destroyAllWindows()
--- a/record.py
+++ b/record.py
@ -0,0 +1,68 @@
+import pickle
+import sys
+
+import torch
+from ultralytics import YOLO
+import cv2
+import time
+
+import utils
+from calculate import normalize_pose, compare_poses_boolean
+from draw import draw_new
+from utils import find_closest
+from video_methods import initialize_method
+
+model = YOLO("yolo11x-pose.pt")
+model.to(torch.device('cuda:0'))
+
+if len(sys.argv) == 2:
+    method_type = sys.argv[1]
+else:
+    print("Podaj argument 'cam', albo 'net'.")
+    exit(1)
+
+method = initialize_method(method_type)
+
+do_pose_shot = False
+startTime = 0
+
+def click_event(event, x, y, flags, param):
+    global do_pose_shot, startTime
+
+    if event == cv2.EVENT_LBUTTONDOWN:  # lewy przycisk myszy
+        do_pose_shot = not do_pose_shot
+        if do_pose_shot:
+            startTime = time.time()
+
+def main():
+    moves = []
+
+    while True:
+        frame = method.receive_frame()
+
+        frame = cv2.flip(frame, 1)
+        results = model(frame, verbose=False)
+
+        if len(results) != 0:
+
+            result = results[0]
+            kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
+
+            if kpts is None:
+                continue
+
+            img = frame
+
+            if do_pose_shot:
+                moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))
+            elif len(moves) != 0:
+                with open('moves.pkl', 'wb') as f:  # 'wb' = write binary
+                    pickle.dump(moves, f)
+                exit(1)
+
+
+            cv2.imshow('Klatka z kamerki', img)
+            cv2.setMouseCallback('Klatka z kamerki', click_event)
+            cv2.waitKey(1)  # Czekaj na naciśnięcie klawisza
+
+main()
--- a/record_one_pose.py
+++ b/record_one_pose.py
@ -0,0 +1,58 @@
+import pickle
+import sys
+
+import torch
+from ultralytics import YOLO
+import cv2
+import time
+
+import utils
+from calculate import normalize_pose, compare_poses_boolean
+from draw import draw_new
+from utils import find_closest
+from video_methods import initialize_method
+
+model = YOLO("yolo11x-pose.pt")
+model.to(torch.device('cuda:0'))
+
+method = initialize_method("cam")
+
+do_pose_shot = False
+startTime = 0
+
+def click_event(event, x, y, flags, param):
+    global do_pose_shot, startTime
+
+    if event == cv2.EVENT_LBUTTONDOWN:  # lewy przycisk myszy
+        do_pose_shot = not do_pose_shot
+        if do_pose_shot:
+            startTime = time.time()
+
+def main():
+    moves = []
+
+    while True:
+        frame = method.receive_frame()
+
+        frame = cv2.flip(frame, 1)
+        results = model(frame, verbose=False)
+
+        if len(results) != 0:
+
+            result = results[0]
+            kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
+
+            if kpts is None:
+                continue
+
+            img = frame
+
+            if do_pose_shot:
+                print(normalize_pose(result.keypoints.xy.cpu().numpy()[0]))
+                exit(0)
+
+            cv2.imshow('Klatka z kamerki', img)
+            cv2.setMouseCallback('Klatka z kamerki', click_event)
+            cv2.waitKey(1)  # Czekaj na naciśnięcie klawisza
+
+main()
--- a/record_video_pose.py
+++ b/record_video_pose.py
@ -0,0 +1,40 @@
+import glob
+import pickle
+from tqdm import tqdm
+
+import torch
+from ultralytics import YOLO
+import cv2
+import time
+
+import utils
+from calculate import normalize_pose, compare_poses_boolean
+from draw import draw_new
+from utils import find_closest
+from video_methods import initialize_method
+
+model = YOLO("yolo11x-pose.pt")
+model.to(torch.device('cuda:0'))
+
+startTime = 0
+def main():
+    moves = []
+
+    for i in tqdm(sorted(glob.glob("video/camA/*.jpg"), key=lambda f: int(__import__("re").search(r"\d+", f).group()))):
+        data = i.replace(f'video/camA\\', "")
+        frame = cv2.imread(f"video/camA/{data}")
+        results = model(frame, verbose=False)
+
+        if len(results) != 0:
+            result = results[0]
+            kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
+
+            if kpts is None:
+                continue
+
+            moves.append((int(data.replace(".jpg", "")), normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))
+
+    with open('moves_makarena_best.pkl', 'wb') as f:  # 'wb' = write binary
+        pickle.dump(moves, f)
+
+main()
--- a/rotate.py
+++ b/rotate.py
@ -0,0 +1,19 @@
+import cv2
+import os
+
+folder = r"video/camC"  # ← podaj swoją ścieżkę
+
+# rozszerzenia jakie chcesz obracać
+ext = (".jpg", ".jpeg", ".png")
+
+for filename in os.listdir(folder):
+    if filename.lower().endswith(ext):
+        path = os.path.join(folder, filename)
+
+        img = cv2.imread(path)
+        rotated = cv2.rotate(img, cv2.ROTATE_180)
+
+        cv2.imwrite(path, rotated)  # nadpisanie pliku
+        print(f"Obrócono: {filename}")
+
+print("Gotowe ✔️")
--- a/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth
+++ b/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth
--- a/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth
+++ b/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth
--- a/sender.py
+++ b/sender.py
@ -0,0 +1,51 @@
+import cv2
+import socket
+import zstandard as zstd
+import struct
+import time
+
+from utils import resize_with_padding
+
+SERVER_IP = '127.0.0.1'
+SERVER_PORT = 9999
+
+# Socket
+sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+sock.connect((SERVER_IP, SERVER_PORT))
+
+# Kompresor Zstd
+compressor = zstd.ZstdCompressor(level=10)
+
+cap = cv2.VideoCapture(0)  # kamerka
+
+total_bytes_sent = 0
+start_time = time.time()
+
+JPEG_QUALITY = 25  # 0-100, im mniejsza, tym większa kompresja
+
+try:
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = resize_with_padding(frame)
+
+        # Konwersja do JPEG
+        _, buffer = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, JPEG_QUALITY])
+        data = buffer.tobytes()
+
+        # Wysyłanie długości + danych
+        sock.sendall(struct.pack('!I', len(data)))
+        sock.sendall(data)
+
+        total_bytes_sent += len(data)
+        elapsed = time.time() - start_time
+        if elapsed >= 1.0:
+            print(f"Upload speed: {total_bytes_sent * 8 / 1e6:.2f} Mbps")  # w megabitach
+            total_bytes_sent = 0
+            start_time = time.time()
+
+finally:
+    cap.release()
+    sock.close()
--- a/test.py
+++ b/test.py
--- a/utils.py
+++ b/utils.py
@ -0,0 +1,64 @@
+import math
+
+import cv2
+import numpy as np
+
+def recvall(sock, n):
+    data = b''
+    while len(data) < n:
+        packet = sock.recv(n - len(data))
+        if not packet:
+            return None
+        data += packet
+    return data
+
+def distance(p1, p2):
+    return math.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)
+
+import numpy as np
+
+def normalize(move):
+    left_hip = move[11]   # Left Hip
+    right_hip = move[12]  # Right Hip
+    nose = move[0]        # Nose (głowa)
+
+    # Środek bioder
+    center = (left_hip + right_hip) / 2
+
+    # Przesunięcie względem środka
+    normalized_keypoints = move - center
+
+    # Zamiast max_dist używamy stałej miary "rozmiaru ciała"
+    body_height = np.linalg.norm(nose[:2] - center[:2])  # np. odległość biodra-głowa
+
+    if body_height > 0:
+        normalized_keypoints[:, :2] /= body_height
+
+    draw = normalized_keypoints[:, :2]
+    return draw
+
+def find_closest(moves, target):
+    return min(moves, key=lambda t: abs(t[0] - target))
+
+def resize_with_padding(image, target_size=(640, 640)):
+    h, w = image.shape[:2]
+    target_w, target_h = target_size
+
+    # Oblicz współczynnik skalowania, zachowując proporcje
+    scale = min(target_w / w, target_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+
+    # Resize obrazu
+    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
+
+    # Stwórz tło (czarne) o wymiarach docelowych
+    output_image = np.zeros((target_h, target_w, 3), dtype=np.uint8)
+
+    # Oblicz offsety do wyśrodkowania obrazu
+    x_offset = (target_w - new_w) // 2
+    y_offset = (target_h - new_h) // 2
+
+    # Wklej resized image na tło
+    output_image[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_image
+
+    return output_image
--- a/video_methods.py
+++ b/video_methods.py
@ -0,0 +1,65 @@
+import socket
+import struct
+import time
+
+import cv2
+import numpy as np
+
+from utils import recvall
+
+methods = ["cam", "net"]
+
+HOST = '0.0.0.0'
+PORT = 9999
+
+class Method:
+    def __init__(self, method_type):
+        self.method_type = method_type
+
+        if method_type == "cam":
+            self.cap = cv2.VideoCapture(0)
+
+            if not self.cap.isOpened():
+                print("Nie można otworzyć kamerki")
+                exit(1)
+        else:
+            self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+            self.sock.bind((HOST, PORT))
+            self.sock.listen(1)
+            print(f"Oczekuje podłączenia na: {HOST}:{PORT}")
+            self.conn, addr = self.sock.accept()
+            print(f"Podłączono przez {addr}")
+
+            self.total_bytes_received = 0
+            self.start_time = time.time()
+
+    def receive_frame(self):
+        if self.method_type == "cam":
+            _, frame = self.cap.read()
+
+            if not _:
+                exit(1)
+        else:
+            packed_len = recvall(self.conn, 4)
+            if not packed_len:
+                exit(1)
+
+            length = struct.unpack('!I', packed_len)[0]
+
+            data = recvall(self.conn, length)
+            if not data:
+                exit(1)
+
+            self.total_bytes_received += length
+
+            img_array = np.frombuffer(data, dtype=np.uint8)
+            frame = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+
+        return frame
+
+
+def initialize_method(method_type):
+    if not method_type in methods:
+        return None
+
+    return Method(method_type)
--- a/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth
+++ b/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth
Author	SHA1	Message	Date
Tulis12	b84c43a898	v2 working	2025-12-08 20:25:20 +01:00
Tulis12	4943a20c11	working	2025-11-28 15:36:57 +00:00
Tulis12	40dc5b3b59	working	2025-11-28 08:31:35 +00:00
Tulis12	310997a8dd	working	2025-11-27 21:28:10 +01:00
Tulis	d3063a6048	working	2025-09-10 19:59:07 +02:00
Tulis	35e0f79b35	working	2025-09-10 19:56:21 +02:00
Tulis	5ab222aee3	working	2025-09-10 19:51:05 +02:00
Tulis	5560f6b2e6	working	2025-08-25 19:37:48 +02:00
				`@ -0,0 +1 @@`
				`t_pose = [ 0.079178 , 0.1963 , 0.098774 , 0.033032 , 0.99646 , 0.08401 , 0.99999, 0.0049546, -0.99134 , 0.13132, -0.99791, -0.064541, 0.063086, 0.99801, -0.03562, 0.99936, -0.012939, 0.99992, 0.02004, 0.9998]`