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Tulis12
2025-11-27 21:28:10 +01:00
parent d3063a6048
commit 310997a8dd
7 changed files with 164 additions and 49 deletions
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5
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15
main.py
View File

@ -5,8 +5,10 @@ from ultralytics import YOLO
import cv2 import cv2
import time import time
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
from utils import find_closest
from video_methods import initialize_method from video_methods import initialize_method
model = YOLO("yolo11x-pose.pt") model = YOLO("yolo11x-pose.pt")
@ -51,6 +53,8 @@ def main():
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, move[1], move[2])
print(moves)
while True: while True:
frame = method.receive_frame() frame = method.receive_frame()
@ -74,9 +78,11 @@ def main():
img = frame img = frame
normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0]) normalized = normalize_pose(result.keypoints.xy.cpu().numpy()[0])
cv2.imshow('you', draw_new(result.keypoints.xy.cpu()[0]))
if currTimeIndex != 0 and moves.index(find_closest(time.time() - currTimeIndex)) == len(moves) - 1: draw = utils.normalize(result.keypoints.xy.cpu().numpy()[0])
cv2.imshow('you', draw_new(draw * 100 + 100))
if currTimeIndex != 0 and moves.index(find_closest(moves, time.time() - currTimeIndex)) == len(moves) - 1:
mehCount = totalCount - failCount - goodCount mehCount = totalCount - failCount - goodCount
print( print(
@ -96,7 +102,7 @@ def main():
else: else:
changed = False changed = False
closest = find_closest(time.time() - currTimeIndex) closest = find_closest(moves, time.time() - currTimeIndex)
cv2.imshow('Dots', draw_new(closest[2])) cv2.imshow('Dots', draw_new(closest[2]))
if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate: if abs((time.time() - currTimeIndex) - moves[currIndex][0]) > failRate:
@ -130,7 +136,8 @@ def main():
# moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0])) # moves.append((time.time() - startTime, normalize_pose(result.keypoints.xy.cpu().numpy()[0]), result.keypoints.xy.cpu()[0]))
# elif len(moves) != 0: # elif len(moves) != 0:
# with open('moves.pkl', 'wb') as f: # 'wb' = write binary # with open('moves.pkl', 'wb') as f: # 'wb' = write binary
# pickle.dump(moves, f) # pickle.dump
# (moves, f)
# #
# exit(1) # exit(1)

42
moves_dump.py Normal file
View File

@ -0,0 +1,42 @@
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])
cv2.imshow('you', draw_new(draw))
cv2.waitKey(1)
time.sleep(0.1)

25
utils.py
View File

@ -1,3 +1,5 @@
import math
import cv2 import cv2
import numpy as np import numpy as np
@ -10,8 +12,27 @@ def recvall(sock, n):
data += packet data += packet
return data return data
def find_closest(target): def distance(p1, p2):
global moves 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
center = (left_hip + right_hip) / 2
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
draw = normalized_keypoints[:, :2]
return draw
def find_closest(moves, target):
return min(moves, key=lambda t: abs(t[0] - target)) return min(moves, key=lambda t: abs(t[0] - target))
def resize_with_padding(image, target_size=(640, 640)): def resize_with_padding(image, target_size=(640, 640)):

2
video_methods.py
View File

@ -34,7 +34,7 @@ class Method:
self.start_time = time.time() self.start_time = time.time()
def receive_frame(self): def receive_frame(self):
if self.method_type is "cam": if self.method_type == "cam":
_, frame = self.cap.read() _, frame = self.cap.read()
if not _: if not _: