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Merge pull request #16 from DEVOCEAN-YOUNG-404/seungjun
Fix: 플러그인 Footer 오류 수정
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| import cv2 | ||
| import mediapipe as mp | ||
| import numpy as np | ||
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| def calculate_angle(joint): | ||
| v1 = joint[[0, 1, 2, 3, 0, 5, 6, 7, 0, 9, 10, 11, 0, 13, 14, 15, 0, 17, 18, 19], :] | ||
| v2 = joint[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], :] | ||
| v = v2 - v1 | ||
| # Nomalize v | ||
| v = v / np.linalg.norm(v, axis=1)[:, np.newaxis] | ||
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| # Dot product의 아크코사인으로 각도를 구한다. | ||
| compareV1 = v[[0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17], :] | ||
| compareV2 = v[[1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19], :] | ||
| angle = np.arccos(np.einsum('nt,nt->n', compareV1, compareV2)) | ||
| angle = np.degrees(angle) # radian값을 degree로 변환 | ||
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| return angle | ||
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| cap = cv2.VideoCapture(0) | ||
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| mp_drawing = mp.solutions.drawing_utils | ||
| mp_hands = mp.solutions.hands | ||
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| with mp_hands.Hands( | ||
| min_detection_confidence=0.5, | ||
| min_tracking_confidence=0.5) as hands: | ||
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| while cap.isOpened(): | ||
| success, image = cap.read() | ||
| if not success: | ||
| print("Ignoring empty camera frame.") | ||
| continue | ||
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| image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) | ||
| image.flags.writeable = False | ||
| results = hands.process(image) | ||
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| image.flags.writeable = True | ||
| image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | ||
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| if results.multi_hand_landmarks: | ||
| for res in results.multi_hand_landmarks: | ||
| joint = np.zeros((21, 3)) | ||
| for j, lm in enumerate(res.landmark): | ||
| joint[j] = [lm.x, lm.y, lm.z] | ||
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| angle = calculate_angle(joint) | ||
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| mp_drawing.draw_landmarks( | ||
| image, res, mp_hands.HAND_CONNECTIONS) | ||
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| cv2.imshow('MediaPipe Hands', image) | ||
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| key = cv2.waitKey(1) | ||
| if key == ord('.'): | ||
| if results.multi_hand_landmarks: | ||
| # 라벨 입력 받기 | ||
| label = input("라벨을 입력하세요 (예: open_hand, closed_fist, peace_sign 등): ") | ||
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| # 라벨과 각도 값을 텍스트 파일에 저장 | ||
| with open('hand_data_with_labels.txt', 'a') as file: | ||
| file.write(f"{','.join(str(a) for a in angle)},{label}\n") | ||
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| elif key == 27: # ESC 키를 누르면 루프 종료 | ||
| break | ||
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| cap.release() | ||
| cv2.destroyAllWindows() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| import cv2 | ||
| import mediapipe as mp | ||
| import numpy as np | ||
| import json | ||
| import keyboard | ||
| import time | ||
|
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| max_num_hands = 1 | ||
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| gesture = { | ||
| 0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f', 6: 'g', 7: 'h', | ||
| 8: 'i', 9: 'j', 10: 'k', 11: 'l', 12: 'm', 13: 'n', 14: 'o', | ||
| 15: 'p', 16: 'q', 17: 'r', 18: 's', 19: 't', 20: 'u', 21: 'v', | ||
| 22: 'w', 23: 'x', 24: 'y', 25: 'z', 26: 'spacing', 27: 'backspace', 28:'1', | ||
| 29: '2', 30: '3', 31: '4', 32: '5', 33: '6', 34: '7', 35:'8', 36: '9', 37:'0' | ||
| } | ||
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| mp_hands = mp.solutions.hands | ||
| mp_drawing = mp.solutions.drawing_utils | ||
| hands = mp_hands.Hands( | ||
| max_num_hands=max_num_hands, | ||
| min_detection_confidence=0.5, | ||
| min_tracking_confidence=0.5 | ||
| ) | ||
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| file = np.genfromtxt('dataSet.txt', delimiter=',') | ||
| angleFile = file[:, :-1] | ||
| labelFile = file[:, -1] | ||
| angle = angleFile.astype(np.float32) | ||
| label = labelFile.astype(np.float32) | ||
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| knn = cv2.ml.KNearest_create() | ||
| knn.train(angle, cv2.ml.ROW_SAMPLE, label) | ||
| cap = cv2.VideoCapture(0) | ||
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| startTime = time.time() | ||
| prev_index = 0 | ||
| sentence = '' | ||
| recognizeDelay = 1 | ||
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| output_data = {'gestures': [], 'text': ''} | ||
|
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| while True: | ||
| ret, img = cap.read() | ||
| if not ret: | ||
| continue | ||
| img = cv2.flip(img, 1) # 이미지를 좌우 반전시켜 원래 방향으로 표시 | ||
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| result = hands.process(img) | ||
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| if result.multi_hand_landmarks is not None: | ||
| for res in result.multi_hand_landmarks: | ||
| joint = np.zeros((21, 3)) | ||
| for j, lm in enumerate(res.landmark): | ||
| joint[j] = [lm.x, lm.y, lm.z] | ||
|
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| v1 = joint[[0, 1, 2, 3, 0, 5, 6, 7, 0, 9, 10, 11, 0, 13, 14, 15, 0, 17, 18, 19], :] | ||
| v2 = joint[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], :] | ||
| v = v2 - v1 | ||
| v = v / np.linalg.norm(v, axis=1)[:, np.newaxis] | ||
|
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| compareV1 = v[[0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17], :] | ||
| compareV2 = v[[1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19], :] | ||
| angle = np.arccos(np.einsum('nt,nt->n', compareV1, compareV2)) | ||
| angle = np.degrees(angle) | ||
|
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| data = np.array([angle], dtype=np.float32) | ||
| ret, results, neighbours, dist = knn.findNearest(data, 3) | ||
| idx = int(results[0][0]) | ||
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| if idx in gesture.keys(): | ||
| if idx != prev_index: | ||
| startTime = time.time() | ||
| prev_index = idx | ||
| else: | ||
| if time.time() - startTime > recognizeDelay: | ||
| if idx == 26: | ||
| sentence += ' ' | ||
| elif idx == 27: | ||
| if len(sentence) > 0: | ||
| sentence = sentence[:-1] # 마지막 문자 제거 | ||
| else: | ||
| sentence += gesture[idx] | ||
| startTime = time.time() | ||
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| cv2.putText(img, gesture[idx].upper(), (int(res.landmark[0].x * img.shape[1] - 10), | ||
| int(res.landmark[0].y * img.shape[0] + 40)), | ||
| cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 3) | ||
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| mp_drawing.draw_landmarks(img, res, mp_hands.HAND_CONNECTIONS) | ||
| cv2.putText(img, sentence, (20, 440), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3) | ||
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| cv2.imshow('HandTracking', img) | ||
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| key = cv2.waitKey(1) | ||
| if key == ord('.'): | ||
| output_data['text'] = sentence | ||
| json_filename = 'output_data.json' | ||
| with open(json_filename, 'w') as json_file: | ||
| json.dump(output_data, json_file, indent=4) | ||
| break | ||
|
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||
| cap.release() | ||
| cv2.destroyAllWindows() |
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