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Tulis12
2025-12-08 20:25:20 +01:00
parent 4943a20c11
commit b84c43a898
20 changed files with 956 additions and 162 deletions
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3
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85
3cams_3D_v2.py Normal file
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@ -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)

112
3cams_3d.py Normal file
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@ -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)

57
3ddisplay_replay.py Normal file
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@ -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)

58
3ddisplay_replay_smoothed.py Normal file
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@ -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)

2
calculate.py
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@ -52,7 +52,7 @@ def compare_poses(f1, f2):
def compare_poses_boolean(f1, f2):
l2, cos_sim = compare_poses(f1, f2)
return l2 < 0.7 and cos_sim > 0.90
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

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checkpoint/pretrained_h36m_detectron_coco.bin Normal file
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105
main.py
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@ -1,10 +1,15 @@
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
@ -12,6 +17,7 @@ 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]
@ -40,37 +46,81 @@ def main():
mehCount = 0
goodCount = 0
failCount = 0
failRate = 2
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('moves.pkl', 'rb') as f: # 'rb' = read binary
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, move[1], move[2])
moves[i] = ((move[0] - startValue) / 1000, move[1], move[2])
print(moves)
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:
continue
if len(results) != 0:
result = results[0]
kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
@ -86,54 +136,57 @@ def main():
cv2.imshow('you', draw_new(draw * 100 + 100))
if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
mehCount = totalCount - failCount - goodCount
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 + (0.95 * mehCount)) / totalCount * 100}%")
exit(1)
if currMove is None:
if compare_poses_boolean(moves[0][1], normalized):
currIndex = 1
currTimeIndex = time.time()
deltaTime = time.time()
currStatus = f"Zaczoles tanczyc {currIndex}"
currMove = moves[0]
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(closest[2]))
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"
# 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"
# 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]))
@ -159,4 +212,8 @@ def main():
cv2.setMouseCallback('Klatka z kamerki', click_event)
cv2.waitKey(1) # Czekaj na naciśnięcie klawisza
main()
try:
while True:
main()
except KeyboardInterrupt:
pass

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moves.pkl
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106
moves_3d_mp4.py
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@ -1,33 +1,27 @@
import cv2
import mediapipe as mp
import matplotlib
matplotlib.use("Agg") # <-- ważne: wyłącza GUI
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pickle
import time
# ---------------------
# Wideo wejściowe
# ---------------------
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 = 640
height = 640
# ---------------------
# Wideo wyjściowe
# ---------------------
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
width = 1920
height = 1080
# Ustawienia zapisu wideo
fourcc = cv2.VideoWriter_fourcc(*"avc1")
out = cv2.VideoWriter("output.mp4", fourcc, fps, (width, height))
# ---------------------
# MediaPipe Pose
# ---------------------
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=False, model_complexity=1)
# Wczytanie modelu YOLOv8 Pose
model = YOLO("yolo11x-pose.pt", verbose=False) # Twój model pose
moves = []
started = False
frame_id = 0
while True:
@ -35,58 +29,32 @@ while True:
if not ok:
break
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(rgb)
# Skalowanie do 640x640
frame_resized = cv2.resize(frame, (width, height))
# -----------------------------------------
# 3D landmarki: pose_world_landmarks
# -----------------------------------------
if results.pose_world_landmarks:
lm = results.pose_world_landmarks.landmark
# Wykrywanie poz
results = model.predict(frame_resized, verbose=False)
xs = np.array([p.x for p in lm])
ys = np.array([p.y for p in lm])
zs = np.array([p.z for p in lm])
# 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
# -----------------------------
# RYSOWANIE 3D w Matplotlib
# -----------------------------
fig = plt.figure(figsize=(6.4, 6.4), dpi=100)
ax = fig.add_subplot(111, projection="3d")
ax.scatter(xs, zs, ys, s=20)
ax.set_xlim([-1, 1])
ax.set_ylim([-1, 1])
ax.set_zlim([-1, 1])
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
ax.invert_zaxis()
# -----------------------------------------
# Konwersja wykresu Matplotlib → klatka do MP4
# -----------------------------------------
fig.canvas.draw()
renderer = fig.canvas.get_renderer()
w, h = fig.canvas.get_width_height()
buf = renderer.buffer_rgba()
plot_img = np.frombuffer(buf, dtype=np.uint8).reshape((h, w, 4))[:, :, :3]
plt.close(fig)
# Dopasowanie rozmiaru do wideo
plot_img = cv2.resize(plot_img, (width, height))
plot_img = cv2.cvtColor(plot_img, cv2.COLOR_RGB2BGR)
out.write(plot_img)
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")

32
moves_dump.py
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@ -8,11 +8,26 @@ import numpy as np
import utils
from draw import draw_new
moves = []
# moves = {}
better_moves = {}
with open('moves.pkl', 'rb') as f: # 'rb' = read binary
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)
@ -35,8 +50,13 @@ for i, move in enumerate(moves):
# Do rysowania (np. przesunięcie na ekran)
draw = utils.normalize(move[2]) * 200 + 250
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)
# 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)

47
moves_dump_2.py Normal file
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@ -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)

116
moves_videopose3d.py Normal file
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@ -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()

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poses.py Normal file
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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]

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record.py Normal file
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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()

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record_one_pose.py Normal file
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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()

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record_video_pose.py Normal file
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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()

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rotate.py Normal file
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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 ✔️")