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Tulis
2025-09-10 19:51:05 +02:00
parent 5560f6b2e6
commit 5ab222aee3
4 changed files with 154 additions and 208 deletions
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2
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78
main.py
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@ -1,52 +1,23 @@
import pickle import pickle
import sys import sys
import regex as re
import numpy as np
from ultralytics import YOLO from ultralytics import YOLO
import cv2 import cv2
import time import time
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 video_methods import initialize_method
model = YOLO("yolo11x-pose.pt") model = YOLO("yolo11x-pose.pt")
if len(sys.argv) == 1: if len(sys.argv) == 2:
method = sys.argv[1] method_type = sys.argv[1]
else: else:
print("Podaj argument 'cam', albo 'network'.") print("Podaj argument 'cam', albo 'net'.")
exit(1) exit(1)
cap = cv2.VideoCapture(0) method = initialize_method(method_type)
if not cap.isOpened():
print("Nie można otworzyć kamerki")
exit(1)
last_time = time.time()
startTime = time.time()
stage = 0
pose = normalize_pose(np.array([[ 353.17, 107.28],
[ 363.3, 96.435],
[ 347.1, 98.647],
[ 390.12, 99.096],
[ 346.09, 103.63],
[ 425.77, 149.95],
[ 327.92, 153.12],
[ 495.16, 169.82],
[ 260.86, 166.3],
[ 565.53, 182.68],
[ 192.34, 170.58],
[ 393.83, 316.99],
[ 337.05, 316.41],
[ 388.36, 433.37],
[ 333.88, 431.89],
[ 383.58, 479.16],
[ 330.89, 480]]))
do_pose_shot = False do_pose_shot = False
@ -56,6 +27,9 @@ def click_event(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN: # lewy przycisk myszy if event == cv2.EVENT_LBUTTONDOWN: # lewy przycisk myszy
do_pose_shot = not do_pose_shot do_pose_shot = not do_pose_shot
def main():
last_time = time.time()
currTimeIndex = 0 currTimeIndex = 0
currIndex = None currIndex = None
currMove = None currMove = None
@ -66,22 +40,6 @@ goodCount = 0
failCount = 0 failCount = 0
failRate = 2 failRate = 2
def wczytaj_dane_z_txt(sciezka):
wynik = []
with open(sciezka, "r") as f:
zawartosc = f.read()
# Znajdź wszystkie krotki w formacie (float, array([...]))
pattern = re.compile(r"\(([^,]+),\s*array\((\[.*?\]),\s*dtype=float32\)\)")
matches = pattern.findall(zawartosc)
for m in matches:
liczba = float(m[0])
tablica = np.array(eval(m[1]), dtype=np.float32)
wynik.append((liczba, tablica))
return wynik
moves = [] moves = []
with open('moves.pkl', 'rb') as f: # 'rb' = read binary with open('moves.pkl', 'rb') as f: # 'rb' = read binary
@ -93,12 +51,9 @@ 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, move[1], move[2])
def find_closest(target):
global moves
return min(moves, key=lambda t: abs(t[0] - target))
while True: while True:
ret, frame = cap.read() frame = method.receive_frame()
frame = cv2.flip(frame, 1) frame = cv2.flip(frame, 1)
results = model(frame, verbose=False) results = model(frame, verbose=False)
@ -124,7 +79,8 @@ while True:
if currTimeIndex != 0 and moves.index(find_closest(time.time() - currTimeIndex)) == len(moves) - 1: if currTimeIndex != 0 and moves.index(find_closest(time.time() - currTimeIndex)) == len(moves) - 1:
mehCount = totalCount - failCount - goodCount mehCount = totalCount - failCount - goodCount
print(f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.95 * mehCount)) / totalCount * 100}%") print(
f"PODSUMOWANIE: FAIL {failCount} MEH: {mehCount} PERFECT: {goodCount} PERCENTAGE: {(goodCount + (0.95 * mehCount)) / totalCount * 100}%")
exit(1) exit(1)
if currMove is None: if currMove is None:
@ -193,12 +149,4 @@ while True:
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()
# Access the results
for result in results:
annotated_frame = result.plot() # zwraca obraz z naniesionymi keypoints
# Wyświetlenie obrazu przy użyciu OpenCV
cv2.imshow("Pose", annotated_frame)
cv2.waitKey(0)
cv2.destroyAllWindows()

4
utils.py
View File

@ -10,6 +10,10 @@ def recvall(sock, n):
data += packet data += packet
return data return data
def find_closest(target):
global moves
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)):
h, w = image.shape[:2] h, w = image.shape[:2]
target_w, target_h = target_size target_w, target_h = target_size