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3
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@ -6,8 +6,9 @@
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<SUITE FILE_PATH="coverage/JustTwerk$sender.coverage" NAME="sender Coverage Results" MODIFIED="1756142463914" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
<SUITE FILE_PATH="coverage/JustTwerk$sender.coverage" NAME="sender Coverage Results" MODIFIED="1756142463914" 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$openpose.coverage" NAME="openpose Coverage Results" MODIFIED="1755886110615" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/human-pose-estimation-opencv" />
|
<SUITE FILE_PATH="coverage/JustTwerk$openpose.coverage" NAME="openpose Coverage Results" MODIFIED="1755886110615" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/human-pose-estimation-opencv" />
|
||||||
<SUITE FILE_PATH="coverage/JustTwerk$draw.coverage" NAME="draw Coverage Results" MODIFIED="1756053706980" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
<SUITE FILE_PATH="coverage/JustTwerk$draw.coverage" NAME="draw Coverage Results" MODIFIED="1756053706980" 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$ultralytics_test.coverage" NAME="ultralytics-test Coverage Results" MODIFIED="1756116377896" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
<SUITE FILE_PATH="coverage/JustTwerk$ultralytics_test.coverage" NAME="ultralytics-test Coverage Results" MODIFIED="1756116377896" 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$3d.coverage" NAME="3d Coverage Results" MODIFIED="1756027604884" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
<SUITE FILE_PATH="coverage/JustTwerk$3d.coverage" NAME="3d Coverage Results" MODIFIED="1756027604884" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||||
<SUITE FILE_PATH="coverage/JustTwerk$moves_dump.coverage" NAME="moves_dump Coverage Results" MODIFIED="1764275034611" 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$moves_dump.coverage" NAME="moves_dump Coverage Results" MODIFIED="1765111609135" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||||
<SUITE FILE_PATH="coverage/JustTwerk$local_visualizer_3d.coverage" NAME="local_visualizer_3d Coverage Results" MODIFIED="1755937454029" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/.gpu/Lib/site-packages/mmpose/visualization" />
|
<SUITE FILE_PATH="coverage/JustTwerk$local_visualizer_3d.coverage" NAME="local_visualizer_3d Coverage Results" MODIFIED="1755937454029" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/.gpu/Lib/site-packages/mmpose/visualization" />
|
||||||
|
<SUITE FILE_PATH="coverage/JustTwerk$3cams_3d.coverage" NAME="3cams_3d Coverage Results" MODIFIED="1765111273047" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||||
|
<SUITE FILE_PATH="coverage/JustTwerk$main.coverage" NAME="main Coverage Results" MODIFIED="1765220020573" 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$receiver.coverage" NAME="receiver Coverage Results" MODIFIED="1756142451233" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||||
|
<SUITE FILE_PATH="coverage/JustTwerk$filter.coverage" NAME="filter Coverage Results" MODIFIED="1755972211046" 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$record.coverage" NAME="record Coverage Results" MODIFIED="1764350881825" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" />
|
||||||
|
<SUITE FILE_PATH="coverage/JustTwerk$moves_3d_mp4.coverage" NAME="moves_3d_mp4 Coverage Results" MODIFIED="1764352975374" 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$body3d.coverage" NAME="body3d Coverage Results" MODIFIED="1755944498141" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||||
|
<SUITE FILE_PATH="coverage/JustTwerk$02_whole_body_from_image.coverage" NAME="02_whole_body_from_image Coverage Results" MODIFIED="1755885569302" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$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$is_torch.coverage" NAME="is_torch Coverage Results" MODIFIED="1764256054151" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$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="$PROJECT_DIR$" />
|
<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>
|
</component>
|
||||||
</project>
|
</project>
|
||||||
85
3cams_3D_v2.py
Normal file
85
3cams_3D_v2.py
Normal file
@ -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
112
3cams_3d.py
Normal file
@ -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
57
3ddisplay_replay.py
Normal file
@ -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
58
3ddisplay_replay_smoothed.py
Normal file
@ -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)
|
||||||
@ -52,7 +52,7 @@ def compare_poses(f1, f2):
|
|||||||
def compare_poses_boolean(f1, f2):
|
def compare_poses_boolean(f1, f2):
|
||||||
l2, cos_sim = compare_poses(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):
|
def center(keypoints):
|
||||||
mid_hip = (keypoints[11] + keypoints[12]) / 2 # left_hip=11, right_hip=12
|
mid_hip = (keypoints[11] + keypoints[12]) / 2 # left_hip=11, right_hip=12
|
||||||
|
|||||||
BIN
checkpoint/pretrained_h36m_detectron_coco.bin
Normal file
BIN
checkpoint/pretrained_h36m_detectron_coco.bin
Normal file
Binary file not shown.
203
main.py
203
main.py
@ -1,10 +1,15 @@
|
|||||||
|
import json
|
||||||
|
import os
|
||||||
import pickle
|
import pickle
|
||||||
|
import socket
|
||||||
import sys
|
import sys
|
||||||
|
|
||||||
|
import torch
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
import cv2
|
import cv2
|
||||||
import time
|
import time
|
||||||
|
|
||||||
|
import poses
|
||||||
import utils
|
import utils
|
||||||
from calculate import normalize_pose, compare_poses_boolean
|
from calculate import normalize_pose, compare_poses_boolean
|
||||||
from draw import draw_new
|
from draw import draw_new
|
||||||
@ -12,6 +17,7 @@ from utils import find_closest
|
|||||||
from video_methods import initialize_method
|
from video_methods import initialize_method
|
||||||
|
|
||||||
model = YOLO("yolo11s-pose.pt")
|
model = YOLO("yolo11s-pose.pt")
|
||||||
|
model.to(torch.device('cuda:0'))
|
||||||
|
|
||||||
if len(sys.argv) == 2:
|
if len(sys.argv) == 2:
|
||||||
method_type = sys.argv[1]
|
method_type = sys.argv[1]
|
||||||
@ -40,123 +46,174 @@ def main():
|
|||||||
mehCount = 0
|
mehCount = 0
|
||||||
goodCount = 0
|
goodCount = 0
|
||||||
failCount = 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 = []
|
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)
|
moves = pickle.load(f)
|
||||||
|
|
||||||
startValue = moves[0][0]
|
startValue = moves[0][0]
|
||||||
totalCount = len(moves)
|
totalCount = len(moves)
|
||||||
|
|
||||||
for i, move in enumerate(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:
|
while True:
|
||||||
|
doing += 1
|
||||||
frame = method.receive_frame()
|
frame = method.receive_frame()
|
||||||
|
|
||||||
frame = cv2.flip(frame, 1)
|
frame = cv2.flip(frame, 1)
|
||||||
|
|
||||||
results = model(frame, verbose=False)
|
results = model(frame, verbose=False)
|
||||||
|
|
||||||
|
if not actuallyDoing:
|
||||||
|
client_socket.sendall("start".encode())
|
||||||
|
actuallyDoing = True
|
||||||
|
|
||||||
current_time = time.time()
|
current_time = time.time()
|
||||||
delta = current_time - last_time
|
delta = current_time - last_time
|
||||||
last_time = current_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')
|
fps = 1 / delta if delta > 0 else float('inf')
|
||||||
# print(f"\rDelta: {delta:.4f}s, FPS: {fps:.2f}", end="")
|
# print(f"\rDelta: {delta:.4f}s, FPS: {fps:.2f}", end="")
|
||||||
|
|
||||||
if len(results) == 0:
|
if len(results) != 0:
|
||||||
continue
|
|
||||||
|
|
||||||
result = results[0]
|
result = results[0]
|
||||||
kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
|
kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
|
||||||
|
|
||||||
if kpts is None:
|
if kpts is None:
|
||||||
continue
|
continue
|
||||||
|
|
||||||
img = frame
|
img = frame
|
||||||
|
|
||||||
normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
|
normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
|
||||||
|
|
||||||
draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
|
draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
|
||||||
cv2.imshow('you', draw_new(draw * 100 + 100))
|
cv2.imshow('you', draw_new(draw * 100 + 100))
|
||||||
|
|
||||||
if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
|
if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
|
||||||
mehCount = totalCount - failCount - goodCount
|
mehCount = abs(totalCount - (failCount + goodCount))
|
||||||
|
|
||||||
print(
|
stats = {
|
||||||
f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.95 * mehCount)) / totalCount * 100}%")
|
"failCount": failCount,
|
||||||
exit(1)
|
"goodCount": goodCount,
|
||||||
|
"mehCount": mehCount,
|
||||||
|
"percentage": (goodCount + (0.85 * mehCount)) / totalCount * 100
|
||||||
|
}
|
||||||
|
|
||||||
if currMove is None:
|
client_socket.sendall(f"finish_{json.dumps(stats)}".encode())
|
||||||
if compare_poses_boolean(moves[0][1], normalized):
|
print(
|
||||||
currIndex = 1
|
f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.85 * mehCount)) / totalCount * 100}%")
|
||||||
currTimeIndex = time.time()
|
cv2.destroyAllWindows()
|
||||||
deltaTime = time.time()
|
break
|
||||||
currStatus = f"Zaczoles tanczyc {currIndex}"
|
# thread = Thread(target=print_animation, args=(moves, False))
|
||||||
currMove = moves[0]
|
# thread.start()
|
||||||
|
else:
|
||||||
|
changed = False
|
||||||
|
|
||||||
# thread = Thread(target=print_animation, args=(moves, False))
|
closest = find_closest(moves, time.time() - currTimeIndex)
|
||||||
# thread.start()
|
cv2.imshow('Dots', draw_new(utils.normalize(closest[2]) * 250 + 250))
|
||||||
else:
|
|
||||||
changed = False
|
|
||||||
|
|
||||||
closest = find_closest(moves, time.time() - currTimeIndex)
|
if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
|
||||||
cv2.imshow('Dots', draw_new(closest[2]))
|
currStatus = f"FAIL!"
|
||||||
|
failCount += 1
|
||||||
|
doStreak = False
|
||||||
|
|
||||||
if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
|
if compare_poses_boolean(closest[1], normalized):
|
||||||
currStatus = f"FAIL!"
|
# delays += (time.time() - deltaTime - moves[0][0]) * 1000
|
||||||
failCount += 1
|
# delaysCount += 1
|
||||||
|
|
||||||
if compare_poses_boolean(closest[1], normalized):
|
# currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
|
||||||
# delays += (time.time() - deltaTime - moves[0][0]) * 1000
|
deltaTime = time.time()
|
||||||
# delaysCount += 1
|
|
||||||
|
|
||||||
currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
|
currIndex = moves.index(closest) + 1
|
||||||
deltaTime = time.time()
|
goodCount += 1
|
||||||
|
changed = True
|
||||||
|
doStreak = True
|
||||||
|
|
||||||
currIndex = moves.index(closest) + 1
|
if not changed and compare_poses_boolean(moves[currIndex][1], normalized):
|
||||||
goodCount += 1
|
# delays += (time.time() - deltaTime - moves[0][0]) * 1000
|
||||||
changed = True
|
# delaysCount += 1
|
||||||
|
|
||||||
if not changed and compare_poses_boolean(moves[currIndex][1], normalized):
|
# currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
|
||||||
# delays += (time.time() - deltaTime - moves[0][0]) * 1000
|
deltaTime = time.time()
|
||||||
# delaysCount += 1
|
|
||||||
|
|
||||||
currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
|
changed = True
|
||||||
deltaTime = time.time()
|
|
||||||
|
|
||||||
changed = True
|
currIndex += 1
|
||||||
|
goodCount += 1
|
||||||
|
doStreak = True
|
||||||
|
|
||||||
currIndex += 1
|
# if do_pose_shot:
|
||||||
goodCount += 1
|
# 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)
|
||||||
|
|
||||||
# if do_pose_shot:
|
cv2.putText(
|
||||||
# moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))
|
img, # obraz
|
||||||
# elif len(moves) != 0:
|
currStatus, # tekst
|
||||||
# with open('moves.pkl', 'wb') as f: # 'wb' = write binary
|
(50, 100), # pozycja (x, y) lewego dolnego rogu tekstu
|
||||||
# pickle.dump
|
cv2.FONT_HERSHEY_SIMPLEX, # czcionka
|
||||||
# (moves, f)
|
1, # rozmiar (skalowanie)
|
||||||
#
|
(0, 0, 255), # kolor (BGR) - tutaj czerwony
|
||||||
# exit(1)
|
2, # grubość linii
|
||||||
|
cv2.LINE_AA # typ antyaliasingu
|
||||||
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.imshow('Klatka z kamerki', img)
|
||||||
cv2.setMouseCallback('Klatka z kamerki', click_event)
|
cv2.setMouseCallback('Klatka z kamerki', click_event)
|
||||||
cv2.waitKey(1) # Czekaj na naciśnięcie klawisza
|
cv2.waitKey(1) # Czekaj na naciśnięcie klawisza
|
||||||
|
|
||||||
main()
|
try:
|
||||||
|
while True:
|
||||||
|
main()
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
pass
|
||||||
106
moves_3d_mp4.py
106
moves_3d_mp4.py
@ -1,33 +1,27 @@
|
|||||||
import cv2
|
import pickle
|
||||||
import mediapipe as mp
|
import time
|
||||||
import matplotlib
|
|
||||||
matplotlib.use("Agg") # <-- ważne: wyłącza GUI
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
from mpl_toolkits.mplot3d import Axes3D
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
# ---------------------
|
import cv2
|
||||||
# Wideo wejściowe
|
from ultralytics import YOLO
|
||||||
# ---------------------
|
|
||||||
|
from calculate import normalize_pose
|
||||||
|
from utils import normalize
|
||||||
|
|
||||||
|
# Wczytanie wideo
|
||||||
cap = cv2.VideoCapture("input.mp4")
|
cap = cv2.VideoCapture("input.mp4")
|
||||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||||
width = 640
|
width = 1920
|
||||||
height = 640
|
height = 1080
|
||||||
|
|
||||||
# ---------------------
|
|
||||||
# Wideo wyjściowe
|
|
||||||
# ---------------------
|
|
||||||
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
|
|
||||||
|
|
||||||
|
|
||||||
|
# Ustawienia zapisu wideo
|
||||||
|
fourcc = cv2.VideoWriter_fourcc(*"avc1")
|
||||||
out = cv2.VideoWriter("output.mp4", fourcc, fps, (width, height))
|
out = cv2.VideoWriter("output.mp4", fourcc, fps, (width, height))
|
||||||
|
|
||||||
# ---------------------
|
# Wczytanie modelu YOLOv8 Pose
|
||||||
# MediaPipe Pose
|
model = YOLO("yolo11x-pose.pt", verbose=False) # Twój model pose
|
||||||
# ---------------------
|
|
||||||
mp_pose = mp.solutions.pose
|
|
||||||
pose = mp_pose.Pose(static_image_mode=False, model_complexity=1)
|
|
||||||
|
|
||||||
|
moves = []
|
||||||
|
started = False
|
||||||
frame_id = 0
|
frame_id = 0
|
||||||
|
|
||||||
while True:
|
while True:
|
||||||
@ -35,58 +29,32 @@ while True:
|
|||||||
if not ok:
|
if not ok:
|
||||||
break
|
break
|
||||||
|
|
||||||
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
# Skalowanie do 640x640
|
||||||
results = pose.process(rgb)
|
frame_resized = cv2.resize(frame, (width, height))
|
||||||
|
|
||||||
# -----------------------------------------
|
# Wykrywanie poz
|
||||||
# 3D landmarki: pose_world_landmarks
|
results = model.predict(frame_resized, verbose=False)
|
||||||
# -----------------------------------------
|
|
||||||
if results.pose_world_landmarks:
|
|
||||||
lm = results.pose_world_landmarks.landmark
|
|
||||||
|
|
||||||
xs = np.array([p.x for p in lm])
|
# Rysowanie punktów 2D
|
||||||
ys = np.array([p.y for p in lm])
|
for result in results:
|
||||||
zs = np.array([p.z for p in lm])
|
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))
|
||||||
# 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)
|
|
||||||
|
|
||||||
|
out.write(frame_resized)
|
||||||
frame_id += 1
|
frame_id += 1
|
||||||
|
|
||||||
|
with open('moves2.pkl', 'wb') as f: # 'wb' = write binary
|
||||||
|
pickle.dump(moves, f)
|
||||||
|
|
||||||
cap.release()
|
cap.release()
|
||||||
out.release()
|
out.release()
|
||||||
print("Zapisano: output.mp4")
|
print("Zapisano: output.mp4")
|
||||||
|
|||||||
@ -8,11 +8,26 @@ import numpy as np
|
|||||||
import utils
|
import utils
|
||||||
from draw import draw_new
|
from draw import draw_new
|
||||||
|
|
||||||
moves = []
|
# moves = {}
|
||||||
better_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)
|
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]
|
startValue = moves[0][0]
|
||||||
totalCount = len(moves)
|
totalCount = len(moves)
|
||||||
@ -35,8 +50,13 @@ for i, move in enumerate(moves):
|
|||||||
|
|
||||||
|
|
||||||
# Do rysowania (np. przesunięcie na ekran)
|
# 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.imshow('you', draw_new(draw))
|
||||||
cv2.waitKey(1)
|
# cv2.waitKey(1)
|
||||||
time.sleep(0.1)
|
# 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
47
moves_dump_2.py
Normal file
@ -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
116
moves_videopose3d.py
Normal file
@ -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()
|
||||||
1
poses.py
Normal file
1
poses.py
Normal file
@ -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]
|
||||||
68
record.py
Normal file
68
record.py
Normal file
@ -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()
|
||||||
58
record_one_pose.py
Normal file
58
record_one_pose.py
Normal file
@ -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()
|
||||||
40
record_video_pose.py
Normal file
40
record_video_pose.py
Normal file
@ -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()
|
||||||
19
rotate.py
Normal file
19
rotate.py
Normal file
@ -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 ✔️")
|
||||||
Reference in New Issue
Block a user