working

.gitignore

@@ -4,5 +4,6 @@
 /.gpu-3d/
 /.venv/
 /venv/
+*.mp4
 
 yolo11*

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "liveServer.settings.port": 5501
+}

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()

main.py

@@ -11,7 +11,7 @@ from draw import draw_new
 from utils import find_closest
 from video_methods import initialize_method
 
-model = YOLO("yolo11x-pose.pt")
+model = YOLO("yolo11s-pose.pt")
 
 if len(sys.argv) == 2:
     method_type = sys.argv[1]
@@ -69,68 +69,71 @@ def main():
         fps = 1 / delta if delta > 0 else float('inf')
         # print(f"\rDelta: {delta:.4f}s, FPS: {fps:.2f}", end="")
 
-        for result in results:
-            kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
+        if len(results) == 0:
+            continue
 
-            if kpts is None:
-                continue
+        result = results[0]
+        kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
 
-            img = frame
+        if kpts is None:
+            continue
 
-            normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
+        img = frame
 
-            draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
-            cv2.imshow('you', draw_new(draw * 100 + 100))
+        normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
 
-            if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
-                mehCount = totalCount - failCount - goodCount
+        draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
+        cv2.imshow('you', draw_new(draw * 100 + 100))
 
-                print(
-                    f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.95 * mehCount)) / totalCount * 100}%")
-                exit(1)
+        if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
+            mehCount = totalCount - failCount - goodCount
 
-            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]
+            print(
+                f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.95 * mehCount)) / totalCount * 100}%")
+            exit(1)
 
-                    # thread = Thread(target=print_animation, args=(moves, False))
-                    # thread.start()
-            else:
-                changed = False
+        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]
 
-                closest = find_closest(moves, time.time() - currTimeIndex)
-                cv2.imshow('Dots', draw_new(closest[2]))
+                # thread = Thread(target=print_animation, args=(moves, False))
+                # thread.start()
+        else:
+            changed = False
 
-                if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
-                    currStatus = f"FAIL!"
-                    failCount += 1
+            closest = find_closest(moves, time.time() - currTimeIndex)
+            cv2.imshow('Dots', draw_new(closest[2]))
 
-                if compare_poses_boolean(closest[1], normalized):
-                    # delays += (time.time() - deltaTime - moves[0][0]) * 1000
-                    # delaysCount += 1
+            if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
+                currStatus = f"FAIL!"
+                failCount += 1
 
-                    currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
-                    deltaTime = time.time()
+            if compare_poses_boolean(closest[1], normalized):
+                # delays += (time.time() - deltaTime - moves[0][0]) * 1000
+                # delaysCount += 1
 
-                    currIndex = moves.index(closest) + 1
-                    goodCount += 1
-                    changed = True
+                currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
+                deltaTime = time.time()
 
-                if not changed and compare_poses_boolean(moves[currIndex][1], normalized):
-                    # delays += (time.time() - deltaTime - moves[0][0]) * 1000
-                    # delaysCount += 1
+                currIndex = moves.index(closest) + 1
+                goodCount += 1
+                changed = True
 
-                    currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
-                    deltaTime = time.time()
+            if not changed and compare_poses_boolean(moves[currIndex][1], normalized):
+                # delays += (time.time() - deltaTime - moves[0][0]) * 1000
+                # delaysCount += 1
 
-                    changed = True
+                currStatus = f"SUPER! {currIndex} Zostalo {len(moves)} Delay {(time.time() - currTimeIndex - closest[0]) / 1000}ms"
+                deltaTime = time.time()
 
-                    currIndex += 1
-                    goodCount += 1
+                changed = True
+
+                currIndex += 1
+                goodCount += 1
 
             # if do_pose_shot:
             #     moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))

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()

moves_3d_mp4.py

@@ -0,0 +1,92 @@
+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
+
+# ---------------------
+# Wideo wejściowe
+# ---------------------
+cap = cv2.VideoCapture("input.mp4")
+fps = cap.get(cv2.CAP_PROP_FPS)
+width = 640
+height = 640
+
+# ---------------------
+# Wideo wyjściowe
+# ---------------------
+fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+
+
+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)
+
+frame_id = 0
+
+while True:
+    ok, frame = cap.read()
+    if not ok:
+        break
+
+    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results = pose.process(rgb)
+
+    # -----------------------------------------
+    # 3D landmarki: pose_world_landmarks
+    # -----------------------------------------
+    if results.pose_world_landmarks:
+        lm = results.pose_world_landmarks.landmark
+
+        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 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)
+
+    frame_id += 1
+
+cap.release()
+out.release()
+print("Zapisano: output.mp4")

moves_dump.py

@@ -35,7 +35,7 @@ for i, move in enumerate(moves):
 
 
     # Do rysowania (np. przesunięcie na ekran)
-    draw = utils.normalize(move[2])
+    draw = utils.normalize(move[2]) * 200 + 250
 
     cv2.imshow('you', draw_new(draw))
     cv2.waitKey(1)

utils.py

@@ -15,21 +15,26 @@ def recvall(sock, n):
 def distance(p1, p2):
     return math.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)
 
-def normalize(move):
-    left_hip = move[11]  # Left Hip
-    right_hip = move[12]  # Right Hip
+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
-    distances = np.linalg.norm(normalized_keypoints[:, :2], axis=1)
-    max_dist = np.max(distances)
 
-    if max_dist > 0:
-        normalized_keypoints[:, :2] /= max_dist
+    # 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):