Archived
Дипломная работа
This commit is contained in:
IgorVolochay
@@ -0,0 +1,4 @@
|
||||
MONGO_HOST=127.0.0.1
|
||||
MONGO_PORT=27017
|
||||
MONGO_USER=user
|
||||
MONGO_PASS=pass
|
||||
@@ -0,0 +1,19 @@
|
||||
# Система распознавания лиц для электронных замков
|
||||
|
||||
## Запуск:
|
||||
|
||||
### Установка зависимостей Python:
|
||||
```commandline
|
||||
python3.9 -m venv venv
|
||||
source venv/bin/activate
|
||||
pip3 install -r requirements.txt
|
||||
```
|
||||
### Запуск MongoDB
|
||||
```commandline
|
||||
docker run --name mongodb -d -p 27017:27017 /
|
||||
-e MONGO_INITDB_ROOT_USERNAME=user /
|
||||
-e MONGO_INITDB_ROOT_PASSWORD=pass /
|
||||
mongodb/mongodb-community-server
|
||||
```
|
||||
|
||||
developed by FabLab for Igor's diplom rabota
|
||||
@@ -0,0 +1,166 @@
|
||||
import requests
|
||||
import time
|
||||
import threading
|
||||
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
|
||||
class DoorWorker():
|
||||
def __init__(self):
|
||||
self.door_status = False
|
||||
|
||||
def open_door(self):
|
||||
if not self.door_status:
|
||||
self.door_status = True
|
||||
print("OPEN DOOR")
|
||||
#GPIO.setmode(GPIO.BOARD)
|
||||
#GPIO.setup(16, GPIO.OUT)
|
||||
#GPIO.output(16, 1)
|
||||
time.sleep(5)
|
||||
#GPIO.output(16, 0)
|
||||
|
||||
white_list.clear()
|
||||
permit_list.clear()
|
||||
self.door_status = False
|
||||
|
||||
class HttpWorker():
|
||||
def __init__(self):
|
||||
self.last_answer = None
|
||||
self.search_filter = threading.Lock()
|
||||
|
||||
def search_face(self, position, img_data):
|
||||
return
|
||||
if not self.search_filter.locked():
|
||||
with self.search_filter:
|
||||
URL = ADDRESS + "/find_by_img"
|
||||
headers = {
|
||||
'accept': 'application/json'
|
||||
}
|
||||
files = {
|
||||
'img_data': ('photo.jpg', img_data["image"], 'image/jpeg')
|
||||
}
|
||||
answer = requests.post(URL, headers=headers, files=files).json()
|
||||
if answer["result"]:
|
||||
if answer["result"] in white_list:
|
||||
white_list[answer["result"]] = \
|
||||
{"position": position,
|
||||
"counter": white_list[answer["result"]]["counter"] + 1}
|
||||
else:
|
||||
white_list[answer["result"]] = \
|
||||
{"position": position, "counter": 1}
|
||||
|
||||
print(white_list)
|
||||
self.last_answer = answer
|
||||
time.sleep(0.1)
|
||||
|
||||
class RecognitionWorker():
|
||||
def __init__(self, cap):
|
||||
self.face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')
|
||||
|
||||
def check_frame(self, frame):
|
||||
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
|
||||
faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
|
||||
return faces
|
||||
|
||||
class VisualizeWorker():
|
||||
def drawer(self, image, faces, door_status, permit_list, fps=None):
|
||||
output = image.copy()
|
||||
|
||||
if fps:
|
||||
cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
|
||||
|
||||
white_position = list()
|
||||
if permit_list:
|
||||
for person in permit_list:
|
||||
try:
|
||||
white_position.append(white_list[person]["position"])
|
||||
except:
|
||||
pass
|
||||
|
||||
crop_img = list()
|
||||
for position, face in enumerate(faces):
|
||||
coords = faces[position].astype(np.int32)
|
||||
for i in range(len(coords)):
|
||||
if coords[i] < 0:
|
||||
coords[i] = 0
|
||||
|
||||
if door_status and position in white_position:
|
||||
color = (0, 255, 0)
|
||||
else:
|
||||
color = (0, 0, 255)
|
||||
|
||||
cv.rectangle(output, (coords[0], coords[1]), (coords[0] + coords[2], coords[1] + coords[3]), color, 2)
|
||||
cv.rectangle(output, (coords[0], coords[1]), (coords[0] + coords[2] + 10, coords[1] + coords[3] + 40), (0, 255, 0), 2)
|
||||
crop_img.append(output[coords[1]:coords[1] + coords[3]+40, coords[0]:coords[0] + coords[2]+10])
|
||||
|
||||
return output, crop_img
|
||||
|
||||
|
||||
def resize(img):
|
||||
height_size = 640
|
||||
print(height_size, img.shape[0])
|
||||
scale = height_size / img.shape[0]
|
||||
|
||||
width = int(img.shape[1] * scale)
|
||||
height = int(img.shape[0] * scale)
|
||||
|
||||
return cv.resize(img, (width, height))
|
||||
|
||||
def permit(check_list):
|
||||
permit_ids = list()
|
||||
if len(check_list) != 0:
|
||||
for person in check_list:
|
||||
if check_list[person]["counter"] >= 3:
|
||||
permit_ids.append(person)
|
||||
return permit_ids
|
||||
else:
|
||||
return
|
||||
|
||||
if __name__ == '__main__':
|
||||
ADDRESS = "http://127.0.0.1:5000"
|
||||
device_id = 0
|
||||
|
||||
white_list = dict()
|
||||
door_close = True
|
||||
|
||||
cap = cv.VideoCapture(device_id)
|
||||
tm = cv.TickMeter()
|
||||
|
||||
cv.namedWindow('libfacedetection demo', cv.WINDOW_NORMAL)
|
||||
cv.setWindowProperty('libfacedetection demo', cv.WND_PROP_FULLSCREEN, cv.WINDOW_FULLSCREEN)
|
||||
|
||||
visualize = VisualizeWorker()
|
||||
recognition = RecognitionWorker(cap=cap)
|
||||
request = HttpWorker()
|
||||
door = DoorWorker()
|
||||
|
||||
while cv.waitKey(1) < 0:
|
||||
has_frame, frame = cap.read()
|
||||
if not has_frame:
|
||||
print('No frames grabbed!')
|
||||
|
||||
permit_list = permit(white_list)
|
||||
|
||||
tm.start()
|
||||
faces = recognition.check_frame(frame)
|
||||
tm.stop()
|
||||
|
||||
if faces is not None:
|
||||
frame, crop_imgs = visualize.drawer(frame, faces, door.door_status, permit_list, fps=tm.getFPS())
|
||||
|
||||
for position, img in enumerate(crop_imgs):
|
||||
resize_frame = resize(img)
|
||||
|
||||
_, im_buf_arr = cv.imencode(".jpg", resize_frame)
|
||||
byte_im = im_buf_arr.tobytes()
|
||||
|
||||
threading.Thread(target=request.search_face, args=(position, {'image': byte_im},)).start()
|
||||
|
||||
if permit_list:
|
||||
threading.Thread(target=door.open_door).start()
|
||||
|
||||
cv.imshow('libfacedetection demo', frame)
|
||||
tm.reset()
|
||||
|
||||
print("END")
|
||||
|
||||
Binary file not shown.
@@ -0,0 +1,13 @@
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
ALLOWED_MIME_TYPES = {"image/png", "image/jpeg"}
|
||||
|
||||
class AddPersonJSON(BaseModel):
|
||||
username: str
|
||||
|
||||
class BaseResponse(BaseModel):
|
||||
result: Any
|
||||
error: bool = False
|
||||
@@ -0,0 +1,20 @@
|
||||
from pydantic import BaseModel, NonNegativeInt, ConfigDict
|
||||
from typing import Any
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
from numpy import ndarray, dtype
|
||||
|
||||
class Person(BaseModel):
|
||||
person_id: NonNegativeInt = 0
|
||||
person_name: str
|
||||
|
||||
encodings: list[float]
|
||||
image_name: str
|
||||
|
||||
adding_date: str = datetime.now().isoformat()
|
||||
active_status: bool = True
|
||||
|
||||
model_config = ConfigDict(
|
||||
arbitrary_types_allowed=True,
|
||||
)
|
||||
@@ -0,0 +1,61 @@
|
||||
#!/usr/bin/python3
|
||||
import json
|
||||
|
||||
import cv2
|
||||
import uvicorn, asyncio
|
||||
import numpy as np
|
||||
|
||||
from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Depends
|
||||
|
||||
from schemas.api_schemas import *
|
||||
|
||||
from workers.mongo_worker import MongoWorker
|
||||
from workers.face_rec_worker import FaceRecognition
|
||||
|
||||
app = FastAPI()
|
||||
mongo_worker = MongoWorker()
|
||||
face_recognition_worker = FaceRecognition()
|
||||
|
||||
@app.post('/add_person')
|
||||
async def add_person(json_data: str = Form(...),
|
||||
img_data: UploadFile = File(...),
|
||||
face_recognition: FaceRecognition = Depends(lambda: face_recognition_worker),
|
||||
mongo: MongoWorker = Depends(lambda: mongo_worker)) -> BaseResponse:
|
||||
json_validate = AddPersonJSON(**json.loads(json_data))
|
||||
if img_data.content_type not in ALLOWED_MIME_TYPES:
|
||||
raise HTTPException(400, detail="Invalid file type")
|
||||
|
||||
file_data = await img_data.read()
|
||||
face_img = cv2.imdecode(np.fromstring(file_data, np.uint8), cv2.IMREAD_COLOR)
|
||||
encodings = face_recognition.get_encodings(face_img)
|
||||
if not encodings:
|
||||
raise HTTPException(422, detail="Can't find human face")
|
||||
|
||||
result = mongo.add_person(json_validate.username, encodings[0], img_data.filename, face_img)
|
||||
if result.error:
|
||||
raise HTTPException(422, detail=str(result))
|
||||
return BaseResponse(result="Success")
|
||||
|
||||
@app.post('/find_by_img')
|
||||
async def find_by_img(img_data: UploadFile = File(...),
|
||||
face_recognition: FaceRecognition = Depends(lambda: face_recognition_worker),
|
||||
mongo: MongoWorker = Depends(lambda: mongo_worker)) -> BaseResponse:
|
||||
if img_data.content_type not in ALLOWED_MIME_TYPES:
|
||||
raise HTTPException(400, detail="Invalid file type")
|
||||
|
||||
file_data = await img_data.read()
|
||||
face_img = cv2.imdecode(np.fromstring(file_data, np.uint8), cv2.IMREAD_COLOR)
|
||||
encodings = face_recognition.get_encodings(face_img)
|
||||
if not encodings:
|
||||
raise HTTPException(422, detail="Can't find human face")
|
||||
res = face_recognition_worker.face_distance(encodings)
|
||||
print(res)
|
||||
return BaseResponse(result=res)
|
||||
|
||||
async def main():
|
||||
config = uvicorn.Config("server:app", host="0.0.0.0", port=5000, log_level="info")
|
||||
server = uvicorn.Server(config)
|
||||
await server.serve()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,65 @@
|
||||
import face_recognition
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
from workers.mongo_worker import MongoWorker
|
||||
|
||||
class FaceRecognition():
|
||||
def __init__(self):
|
||||
self.mongo_worker = MongoWorker()
|
||||
self.face_encodings_in_cache = self.mongo_worker.get_all_encodings()
|
||||
|
||||
def get_encodings(self, face_image):
|
||||
return face_recognition.face_encodings(face_image)
|
||||
|
||||
def softmax(self, values):
|
||||
exp_values = np.exp(values)
|
||||
exp_values_sum = np.sum(exp_values)
|
||||
|
||||
return exp_values / exp_values_sum
|
||||
|
||||
def kl_divergence(self, face_encodings, face_to_compare):
|
||||
if len(face_encodings) == 0:
|
||||
return np.empty((0))
|
||||
|
||||
face_encodings = np.asarray(self.softmax(face_encodings))
|
||||
face_to_compare = np.asarray(self.softmax(face_to_compare))
|
||||
|
||||
face_encodings = face_encodings / face_encodings.sum(axis=1, keepdims=True)
|
||||
face_to_compare = face_to_compare / face_to_compare.sum()
|
||||
|
||||
epsilon = 1e-10
|
||||
face_encodings = np.clip(face_encodings, epsilon, 1)
|
||||
face_to_compare = np.clip(face_to_compare, epsilon, 1)
|
||||
|
||||
return np.sum(face_encodings * np.log(face_encodings / face_to_compare), axis=1)
|
||||
|
||||
def face_distance(self, face_encodings:list[float]):
|
||||
result = {}
|
||||
if self.mongo_worker.count_persons() > len(self.face_encodings_in_cache):
|
||||
self.face_encodings_in_cache = self.mongo_worker.get_all_encodings()
|
||||
|
||||
for person_id in self.face_encodings_in_cache:
|
||||
result.update({person_id: self.kl_divergence(face_encodings, self.face_encodings_in_cache[person_id])})
|
||||
|
||||
print(result.values())
|
||||
try:
|
||||
minimal = min(result.values())
|
||||
print(minimal)
|
||||
if minimal > 0.001:
|
||||
return None
|
||||
return [key for key, val in result.items() if val == minimal][0]
|
||||
except:
|
||||
best_in_frame = dict()
|
||||
for key, elements in result.items():
|
||||
best_in_frame.update({key:min(elements)})
|
||||
minimal = min(best_in_frame.values())
|
||||
print(minimal)
|
||||
if minimal > 0.001:
|
||||
return None
|
||||
return [key for key, val in best_in_frame.items() if val == minimal][0]
|
||||
|
||||
if __name__ == "__main__":
|
||||
face_recognition_worker = FaceRecognition()
|
||||
img = cv2.imread("obama.jpg")
|
||||
print(face_recognition_worker.get_encodings(img))
|
||||
@@ -0,0 +1,79 @@
|
||||
import os
|
||||
|
||||
import gridfs
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
from pymongo import MongoClient
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from schemas.base_schemas import *
|
||||
from schemas.api_schemas import *
|
||||
|
||||
class MongoWorker():
|
||||
def __init__(self):
|
||||
load_dotenv()
|
||||
self.client = MongoClient(host=os.getenv('MONGO_HOST'),
|
||||
port=int(os.getenv('MONGO_PORT')),
|
||||
username=os.getenv('MONGO_USER'),
|
||||
password=os.getenv('MONGO_PASS'))
|
||||
self.db = self.client["Face_recognition"]
|
||||
self.persons = self.db["persons"]
|
||||
self.counters = self.db["counters"]
|
||||
self.gfs = gridfs.GridFS(self.db)
|
||||
|
||||
def count_persons(self):
|
||||
docs_count = self.persons.count_documents({})
|
||||
return docs_count
|
||||
|
||||
def get_and_update_counter(self, counter_name: str) -> int:
|
||||
counter = self.counters.find_one_and_update(
|
||||
{"counter_name": counter_name},
|
||||
{"$inc": {"counter": 1}},
|
||||
upsert=True,
|
||||
return_document=True)
|
||||
return counter["counter"]
|
||||
|
||||
def add_person(self, person_name:str, encodings:ndarray[Any, dtype], image_name:str, face_img:np.ndarray) -> BaseResponse:
|
||||
try:
|
||||
new_person_id = self.get_and_update_counter("persons")
|
||||
new_person = Person(
|
||||
person_id = new_person_id,
|
||||
person_name = person_name,
|
||||
encodings = encodings.tolist(),
|
||||
image_name = f"{new_person_id}_{image_name}")
|
||||
|
||||
file_id = self.save_image_to_gridfs(face_img, image_name)
|
||||
mongo_result = self.persons.insert_one(new_person.model_dump())
|
||||
return BaseResponse(result={"file_id": file_id, "mongo_result": mongo_result})
|
||||
except Exception as exception:
|
||||
print(exception)
|
||||
return BaseResponse(result=exception, error=True)
|
||||
|
||||
def save_image_to_gridfs(self, image_array, filename):
|
||||
success, encoded_image = cv2.imencode('.png', image_array)
|
||||
if not success:
|
||||
raise ValueError("Не удалось закодировать изображение")
|
||||
file_id = self.gfs.put(encoded_image.tobytes(), filename=filename)
|
||||
return file_id
|
||||
|
||||
def get_all_encodings(self):
|
||||
result = {}
|
||||
projection = {"person_id": 1, "encodings": 1, "_id": 0}
|
||||
|
||||
for document in self.persons.find({}, projection):
|
||||
entry = {document["person_id"]: document["encodings"]}
|
||||
result.update(entry)
|
||||
|
||||
return result
|
||||
|
||||
def retrieve_image_from_gridfs(self, file_id):
|
||||
try:
|
||||
gridfs_file = self.gfs.get(file_id)
|
||||
image_bytes = gridfs_file.read()
|
||||
image_array = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
|
||||
if image_array is None:
|
||||
raise ValueError("Не удалось декодировать изображение")
|
||||
return image_array
|
||||
except gridfs.errors.NoFile:
|
||||
raise FileNotFoundError(f"Файл с ID {file_id} не найден в GridFS")
|
||||
Submodule Дипломная работа/Исходный код системы deleted from 17aa78baff
Reference in New Issue
Block a user