Showing
2 changed files
with
121 additions
and
27 deletions
| 1 | ################################################## | 1 | ################################################## |
| 2 | -#1. webcam에서 얼굴을 인식합니다. # | 2 | +#1. webcam에서 얼굴을 인식합니다. |
| 3 | -#2. 얼굴일 확률이 95% 이상인 이미지를 이미지 서버로 전송합니다. # | 3 | +#2. 얼굴일 확률이 97% 이상이고 영역이 15000 이상인 이미지를 서버에 전송 |
| 4 | -#3. 전처리 된 데이터를 verification 서버에 전송합니다. # | ||
| 5 | ################################################## | 4 | ################################################## |
| 6 | import torch | 5 | import torch |
| 7 | import numpy as np | 6 | import numpy as np |
| ... | @@ -27,11 +26,10 @@ mtcnn = MTCNN(keep_all=True, device=device) | ... | @@ -27,11 +26,10 @@ mtcnn = MTCNN(keep_all=True, device=device) |
| 27 | uri = 'ws://localhost:8765' | 26 | uri = 'ws://localhost:8765' |
| 28 | 27 | ||
| 29 | async def send_face(face_list, image_list): | 28 | async def send_face(face_list, image_list): |
| 30 | - global uri | ||
| 31 | async with websockets.connect(uri) as websocket: | 29 | async with websockets.connect(uri) as websocket: |
| 32 | for face, image in zip(face_list, image_list): | 30 | for face, image in zip(face_list, image_list): |
| 33 | #type: np.float32 | 31 | #type: np.float32 |
| 34 | - send = json.dumps({"action": "verify", "MTCNN": face.tolist()}) | 32 | + send = json.dumps({'action': 'verify', 'MTCNN': face.tolist()}) |
| 35 | await websocket.send(send) | 33 | await websocket.send(send) |
| 36 | recv = await websocket.recv() | 34 | recv = await websocket.recv() |
| 37 | data = json.loads(recv) | 35 | data = json.loads(recv) |
| ... | @@ -39,53 +37,58 @@ async def send_face(face_list, image_list): | ... | @@ -39,53 +37,58 @@ async def send_face(face_list, image_list): |
| 39 | # 성공 | 37 | # 성공 |
| 40 | print(data['student_id'], 'is attend') | 38 | print(data['student_id'], 'is attend') |
| 41 | else: | 39 | else: |
| 42 | - print('verification failed') | 40 | + print('verification failed:', data['status']) |
| 41 | + if data['status'] == 'failed': | ||
| 43 | send = json.dumps({'action': 'save_image', 'image': image.tolist()}) | 42 | send = json.dumps({'action': 'save_image', 'image': image.tolist()}) |
| 44 | - await websocket.send(send) | ||
| 45 | 43 | ||
| 46 | def detect_face(frame): | 44 | def detect_face(frame): |
| 47 | - # If required, create a face detection pipeline using MTCNN: | ||
| 48 | - global mtcnn | ||
| 49 | results = mtcnn.detect(frame) | 45 | results = mtcnn.detect(frame) |
| 46 | + faces = mtcnn(frame, return_prob = False) | ||
| 50 | image_list = [] | 47 | image_list = [] |
| 48 | + face_list = [] | ||
| 51 | if results[1][0] == None: | 49 | if results[1][0] == None: |
| 52 | - return [] | 50 | + return [], [] |
| 53 | - for box, prob in zip(results[0], results[1]): | 51 | + for box, face, prob in zip(results[0], faces, results[1]): |
| 54 | - if prob < 0.95: | 52 | + if prob < 0.97: |
| 55 | continue | 53 | continue |
| 56 | print('face detected. prob:', prob) | 54 | print('face detected. prob:', prob) |
| 57 | x1, y1, x2, y2 = box | 55 | x1, y1, x2, y2 = box |
| 58 | - image = frame[int(y1-10):int(y2+10), int(x1-10):int(x2+10)] | 56 | + if (x2-x1) * (y2-y1) < 15000: |
| 57 | + # 얼굴 해상도가 너무 낮으면 무시 | ||
| 58 | + continue | ||
| 59 | + # 얼굴 주변 ±3 영역 저장 | ||
| 60 | + image = frame[int(y1-3):int(y2+3), int(x1-3):int(x2+3)] | ||
| 59 | image_list.append(image) | 61 | image_list.append(image) |
| 60 | - return image_list | 62 | + # MTCNN 데이터 저장 |
| 63 | + face_list.append(face.numpy()) | ||
| 64 | + return image_list, face_list | ||
| 61 | 65 | ||
| 62 | def make_face_list(frame): | 66 | def make_face_list(frame): |
| 63 | - global mtcnn | ||
| 64 | results, prob = mtcnn(frame, return_prob = True) | 67 | results, prob = mtcnn(frame, return_prob = True) |
| 65 | face_list = [] | 68 | face_list = [] |
| 66 | if prob[0] == None: | 69 | if prob[0] == None: |
| 67 | return [] | 70 | return [] |
| 68 | for result, prob in zip(results, prob): | 71 | for result, prob in zip(results, prob): |
| 69 | - if prob < 0.95: | 72 | + if prob < 0.97: |
| 70 | continue | 73 | continue |
| 71 | #np.float32 | 74 | #np.float32 |
| 72 | face_list.append(result.numpy()) | 75 | face_list.append(result.numpy()) |
| 73 | return face_list | 76 | return face_list |
| 74 | 77 | ||
| 75 | -cap = cv2.VideoCapture(0) | 78 | +if __name__ == '__main__': |
| 76 | -cap.set(3, 720) | 79 | + cap = cv2.VideoCapture(0, cv2.CAP_DSHOW) |
| 77 | -cap.set(4, 480) | 80 | + cap.set(3, 720) |
| 78 | -while True: | 81 | + cap.set(4, 480) |
| 82 | + cv2.namedWindow("img", cv2.WINDOW_NORMAL) | ||
| 83 | + while True: | ||
| 79 | try: | 84 | try: |
| 80 | - #start = timeit.default_timer() | ||
| 81 | ret, frame = cap.read() | 85 | ret, frame = cap.read() |
| 86 | + cv2.imshow('img', frame) | ||
| 87 | + cv2.waitKey(10) | ||
| 82 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | 88 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
| 83 | - face_list = make_face_list(frame) | 89 | + image_list, face_list = detect_face(frame) |
| 84 | - image_list = detect_face(frame) | 90 | + if not face_list: |
| 85 | - ##embedding server로 전송## | 91 | + continue; |
| 86 | - if face_list: | ||
| 87 | asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list)) | 92 | asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list)) |
| 88 | - #end = timeit.default_timer() | ||
| 89 | - #print('delta time: ', end - start) | ||
| 90 | except Exception as ex: | 93 | except Exception as ex: |
| 91 | print(ex) | 94 | print(ex) | ... | ... |
client/legacy/clinet(window)0605.py
0 → 100644
| 1 | +################################################## | ||
| 2 | +#1. webcam에서 얼굴을 인식합니다. # | ||
| 3 | +#2. 얼굴일 확률이 95% 이상인 이미지를 이미지 서버로 전송합니다. # | ||
| 4 | +#3. 전처리 된 데이터를 verification 서버에 전송합니다. # | ||
| 5 | +################################################## | ||
| 6 | +import torch | ||
| 7 | +import numpy as np | ||
| 8 | +import cv2 | ||
| 9 | +import asyncio | ||
| 10 | +import websockets | ||
| 11 | +import json | ||
| 12 | +import os | ||
| 13 | +import timeit | ||
| 14 | +import base64 | ||
| 15 | + | ||
| 16 | +from PIL import Image | ||
| 17 | +from io import BytesIO | ||
| 18 | +import requests | ||
| 19 | + | ||
| 20 | +from models.mtcnn import MTCNN | ||
| 21 | + | ||
| 22 | +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | ||
| 23 | +print('Running on device: {}'.format(device)) | ||
| 24 | + | ||
| 25 | +mtcnn = MTCNN(keep_all=True, device=device) | ||
| 26 | + | ||
| 27 | +uri = 'ws://localhost:8765' | ||
| 28 | + | ||
| 29 | +async def send_face(face_list, image_list): | ||
| 30 | + global uri | ||
| 31 | + async with websockets.connect(uri) as websocket: | ||
| 32 | + for face, image in zip(face_list, image_list): | ||
| 33 | + #type: np.float32 | ||
| 34 | + send = json.dumps({"action": "verify", "MTCNN": face.tolist()}) | ||
| 35 | + await websocket.send(send) | ||
| 36 | + recv = await websocket.recv() | ||
| 37 | + data = json.loads(recv) | ||
| 38 | + if data['status'] == 'success': | ||
| 39 | + # 성공 | ||
| 40 | + print(data['student_id'], 'is attend') | ||
| 41 | + else: | ||
| 42 | + print('verification failed') | ||
| 43 | + send = json.dumps({'action': 'save_image', 'image': image.tolist()}) | ||
| 44 | + await websocket.send(send) | ||
| 45 | + | ||
| 46 | +def detect_face(frame): | ||
| 47 | + # If required, create a face detection pipeline using MTCNN: | ||
| 48 | + global mtcnn | ||
| 49 | + results = mtcnn.detect(frame) | ||
| 50 | + image_list = [] | ||
| 51 | + if results[1][0] == None: | ||
| 52 | + return [] | ||
| 53 | + for box, prob in zip(results[0], results[1]): | ||
| 54 | + if prob < 0.95: | ||
| 55 | + continue | ||
| 56 | + print('face detected. prob:', prob) | ||
| 57 | + x1, y1, x2, y2 = box | ||
| 58 | + image = frame[int(y1-10):int(y2+10), int(x1-10):int(x2+10)] | ||
| 59 | + image_list.append(image) | ||
| 60 | + return image_list | ||
| 61 | + | ||
| 62 | +def make_face_list(frame): | ||
| 63 | + global mtcnn | ||
| 64 | + results, prob = mtcnn(frame, return_prob = True) | ||
| 65 | + face_list = [] | ||
| 66 | + if prob[0] == None: | ||
| 67 | + return [] | ||
| 68 | + for result, prob in zip(results, prob): | ||
| 69 | + if prob < 0.95: | ||
| 70 | + continue | ||
| 71 | + #np.float32 | ||
| 72 | + face_list.append(result.numpy()) | ||
| 73 | + return face_list | ||
| 74 | + | ||
| 75 | +cap = cv2.VideoCapture(0) | ||
| 76 | +cap.set(3, 720) | ||
| 77 | +cap.set(4, 480) | ||
| 78 | +while True: | ||
| 79 | + try: | ||
| 80 | + #start = timeit.default_timer() | ||
| 81 | + ret, frame = cap.read() | ||
| 82 | + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | ||
| 83 | + face_list = make_face_list(frame) | ||
| 84 | + image_list = detect_face(frame) | ||
| 85 | + ##embedding server로 전송## | ||
| 86 | + if face_list: | ||
| 87 | + asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list)) | ||
| 88 | + #end = timeit.default_timer() | ||
| 89 | + #print('delta time: ', end - start) | ||
| 90 | + except Exception as ex: | ||
| 91 | + print(ex) |
-
Please register or login to post a comment