Backend

.gitignore

@@ -5,11 +5,25 @@ ML_modul/DifferentColorData/
 ML_modul/LS_Backend/
 ML_modul/labeling/*
 ML_modul/MyDataset/*/*
-ML_modul/special_venv/
+special_venv/
+test.py/
+
+Backend_modul/v1/__pycache__/*
+Backend_modul/v1/config/__pycache__/*
+Backend_modul/v1/core/__pycache__/*
+Backend_modul/v1/ml/__pycache__/*
+Backend_modul/v1/routers/__pycache__/*
+Backend_modul/__pycache__/*
+Backend_modul/app/*
+Backend_modul/db/*
 
 #файлы
 ML_modul/PBC_dataset_normal_DIB.zip
 ML_modul/result.txt
+ML_modul/labeling.rar
+Backend_modul/static/images/Buttons.ai
+Backend_modul/database_min.sql
+
 
 #игнор
 !ML_modul/labeling/export
@@ -18,4 +32,4 @@ ML_modul/result.txt
 !ML_modul/MyDataset/lymphocyte/_copied_files.txt
 !ML_modul/MyDataset/monocyte/_copied_files.txt
 !ML_modul/MyDataset/neutrophil/_copied_files.txt
-!ML_modul/MyDataset/platelet/_copied_files.txt
+!ML_modul/MyDataset/platelet/_copied_files.txt

Backend_modul/app/app.py

@@ -0,0 +1,59 @@
+from fastapi import FastAPI, File, UploadFile, Form
+from ultralytics import YOLO
+
+import supervision as sv
+import pandas as pd
+import numpy as np
+import shutil
+import cv2
+
+app = FastAPI()
+model = YOLO(r"D:\webBloodDetection\Backend_modul\ML\rbc_model.pt")
+
+
+@app.get("/")
+def read_root():
+    return {"message" : "Hello, World"}
+
+@app.post("/analyze")
+async def analyze(file: UploadFile = File(...)):
+#D:\webBloodDetection\Backend_modul>curl -v -X POST http://127.0.0.1:8000/analyze -F "file=@sample.jpg"
+    #Отправка в предобработку и тд.
+    #Генерация id 
+    #Отправка данных о вводе в БД
+    #Отправка клиенту ответа об начале анализа.
+
+    contents = await file.read()
+    nparr = np.frombuffer(contents, np.uint8)
+    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    results = model(
+             image,
+             verbose=False,
+             conf=0.3,
+             iou=0.3
+        )
+    if len(results)!=0:
+        result = results[0]
+        annotated_image = result.plot()
+        cv2.imwrite('D:/webBloodDetection/Backend_modul/images/annotated.jpg', annotated_image)
+        return {"analyze_status" : "good"}
+    else:
+        return {"analyze_status" : "bad"}
+
+
+@app.get("/status/{id}")
+async def search(id: int):
+    #поиск в PostgreSQL
+    if id==1:
+        return {"status" : "processed"}
+    if id==2:
+        return {"status" : "ready"}
+
+@app.get("/result/{id}")
+async def get_result(id: int):
+    #поиск в PostgreSQL
+    if id==1:
+        return {"result" : "report1"}
+    if id==2:
+        return {"result" : "report2"}

Backend_modul/conn.py

@@ -0,0 +1,24 @@
+from sqlalchemy import create_engine
+from model_type1 import create_db, drop_db, Images, Labels, Statistics_image
+from sqlalchemy.orm import sessionmaker
+from data_generator import generate
+
+
+def main():
+    engine = create_engine(
+        "postgresql+psycopg2://postgres:1414@localhost:5432/bloodcells",
+        echo=True
+    )
+    #drop_db(engine)
+    #create_db(engine)
+
+    Sessions = sessionmaker(bind=engine)
+    with Sessions() as session:
+        #stats, labels, images = generate()
+        #session.add_all(stats + labels + images)
+        session.commit()
+    return
+
+
+if __name__ == "__main__":
+    main()

Backend_modul/data_generator.py

@@ -0,0 +1,37 @@
+from ultralytics import YOLO
+from pathlib import Path
+from model_type1 import Images, Statistics_image, Labels
+
+def generate():
+    stats, labels, images = [], [], []
+    model = YOLO(r"D:\webBloodDetection\Backend_modul\ML\rbc_model.pt")
+    folder = Path(r".\images")
+    i = 1
+    for file in folder.iterdir():
+        if file.is_file():
+            image = file.resolve()
+            results = model(
+                    image,
+                    verbose=False,
+                    conf=0.3,
+                    iou=0.3
+                )
+
+            result = results[0]
+            #print(f"image{i} = Images(file_name='{file.name}', file_path='{image}')")
+            #print(f"stat{i} = Statistics_image(id_image={i}, descriptions='test')")
+            stats.append(Images(file_name=str(file.name), file_path=str(image)))
+            images.append(Statistics_image(id_image=i, descriptions="test"))
+            n = 0
+            for box in result.boxes:
+                x, y, w, h = box.xywh[0][0].item(), box.xywh[0][1].item(), box.xywh[0][2].item(), box.xywh[0][3].item() #там почему-то тензор двумерный массив, так что надо так писать [0][3]
+                conf = box.conf[0].item()
+                cls = int(box.cls[0].item()) #box.cls возвращается тензор, поэтому нужно использова обращение к первому и единственному элементу [0] 
+                #print(f"cls:{model.names[cls]}, conf:{conf:.2f}, x: {x:.2f}, y:{y:.2f}, w:{w:.2f}, h:{h:.2f}")
+                #print(f"label{n}_img{i} = Labels(id_image={i}, cls='{model.names[cls]}', conf={conf:.2f}, x={x:.2f}, y={y:.2f}, w={w:.2f}, h={h:.2f})")
+                labels.append(Labels(id_image=i, cls=model.names[cls], conf=conf, x=round(x, 2), y=round(y, 2), w=round(w, 2), h=round(h, 2)))
+            i += 1
+    return labels, images, stats
+
+if __name__=="__main__":
+    pass

Backend_modul/header/header.css

@@ -0,0 +1,156 @@
+@font-face {
+    font-family: 'Local Fonts';
+    src: local("RobotoSerif-Regular");
+}
+
+/* база */
+* {
+    margin: 0;
+}
+#content {
+    display: flex;
+    height: 100vh;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+/* head */
+#nav_head {
+    display: flex;
+    flex-direction: row;
+    justify-content: space-between;
+    width: 100%;
+    height: 3rem;
+    max-width: 100%;
+    padding: 0.5rem;
+    box-sizing: border-box;
+}
+#nameSite {
+    font-size: 2.3rem;
+    margin-right: auto;
+    font-family: "RobotoSerif-Regular", Local Fonts;
+    cursor: pointer;
+    user-select: none;
+}
+#hub_link {
+    padding-left: 10rem;
+}
+#navigation {
+    border-radius: 0rem 0rem 1.5rem 1.5rem;
+    border-bottom: 0.1rem solid black;
+    z-index: 998;
+    flex-shrink: 0;
+}
+#avatar {
+    height: 3rem;
+    width: auto;
+    border-radius: 50%;
+    cursor: pointer;
+    -webkit-user-drag: none;
+    user-select: none;
+}
+
+/* выдвигающиеся окно */
+#win_nav {
+    position: fixed;
+    right: -20rem;
+    transition: 0.2s right ease-in-out;
+    display: flex;
+    flex-direction: column;
+
+    height: 100%;
+    width: 20rem;
+    border-left: 0.1rem solid black;
+    box-sizing: border-box;
+    background: white;
+    z-index: 1000;
+    gap: 1rem;
+}
+#win_nav.open {
+    right: 0;
+}
+
+#win_title {
+    display: flex;
+    flex-direction: row;
+    gap: 1rem;
+    padding: 1rem;
+    align-items: center;
+    user-select: none;
+    font-family: "RobotoSerif-Regular", Local Fonts;
+}
+
+#win_nav_avatar {
+    height: 3rem;
+    width: auto;
+    border-radius: 50%;
+    margin-left: auto;
+    -webkit-user-drag: none;
+}
+#win_nav_cross {
+    height: 2rem;
+    width: auto;
+    border-radius: 50%;
+    cursor: pointer;
+    -webkit-user-drag: none;
+}
+#win_nav_name {
+    font-size: 1rem;
+}
+
+#list_buttons {
+    display: flex;
+
+    flex-direction: column;
+    justify-content: space-around;
+    margin-top: 10rem;
+}
+
+.win_nav_buttons {
+    display: flex;
+    flex-direction: row;
+    padding: 0.5rem 0rem;
+    gap: 1rem;
+    padding: 1rem;
+    align-items: center;
+    cursor: pointer;
+    user-select: none;
+    font-family: "RobotoSerif-Regular", Local Fonts;
+}
+
+.win_nav_buttons:hover {
+    background-color: lightgray;
+}
+.win_nav_images {
+    -webkit-user-drag: none;
+    height: 1.5rem;
+    width: 1.5rem;
+    border-radius: 14%;
+}
+
+.menu_overlay {
+    display: none;
+    z-index: 999;
+    position: fixed;
+    width: 100%;
+    height: 100%;
+    top: 0;
+    left: 0;
+    background-color: rgba(0, 0, 0, 0.25);
+}
+
+.menu_overlay.active {
+    display: block;
+}
+
+/* Canvas */
+#body {
+    display: flex;
+    flex-direction: row;
+    gap: 0.2rem;
+    padding: 3rem;
+    height: 100%;
+    box-sizing: border-box;
+    flex: 1;
+    min-height: 0;
+}