add app files

2024-03-30 06:46:09 +03:00
parent 0a7410c34c
commit 8d203a0069
12 changed files with 413 additions and 0 deletions
--- a/notebooks/app/PredictServer.py
+++ b/notebooks/app/PredictServer.py
@@ -0,0 +1,117 @@
+import pickle
+import customtkinter as ctk
+import os
+import cv2
+from PIL import Image, ImageTk
+from deepface import DeepFace
+from mtcnn import MTCNN
+from catboost import CatBoostClassifier
+import pandas as pd
+import numpy as np
+
+class Server(ctk.CTkFrame):
+    def __init__(self, master):
+        super().__init__(master)
+        self.counter = 0
+        self.storage_name: str
+        self.grid_columnconfigure(0, weight=1)
+        self.detector = MTCNN()
+        import socket
+        self.s = socket.socket()
+        host = "192.168.120.240"
+        port = 12345
+        self.s.bind((host, port))
+        self.s.listen(5)
+        self.catboost_model_usa = CatBoostClassifier()
+        self.catboost_model_usa.load_model("../catboost_usa.cbm")
+        with open('../model/saved_dictionary.pkl', 'rb') as f:
+            self.name_usa = pickle.load(f)
+       # self.grid_rowconfigure(0, weight=1)
+        self.label = ctk.CTkLabel(self, text="Server", fg_color="blue", text_color="white")
+        self.label.grid(row=0, column=0, sticky="ew")
+        self.button_start_predict = ctk.CTkButton(self, text="Get photo from server", command=self.__start_predict)
+        self.button_start_predict.grid(row=1, column=0, pady=10, sticky="ew")
+
+
+    def __open_file_dialog(self):
+        root = ctk.CTk()
+        root.withdraw()
+        file_path = ctk.filedialog.askdirectory(title='Choose image dataset')
+        if file_path != '':
+            root.destroy()
+            self.storage_name = file_path
+            self.iterator = iter(os.listdir(self.storage_name))
+            return file_path
+    def __start_predict(self):
+        while True:
+            c = 0
+            con, addr = self.s.accept()
+            with open('FDJ.jpg', 'wb') as f:
+                while True:
+                    c+=1
+                    print(1)
+                    data = con.recv(65536)
+                    if c >= 3:
+                        f.write(data)
+                        break
+                    if not data:
+                        f.write(data)
+                        break
+                        break
+            #break
+
+                    f.write(data)
+
+                break
+
+            #break
+
+            print("break")
+            break
+        if self.counter > 0:
+            self.label.destroy()
+        self.counter+=1
+        filename = 'FDJ.jpg'
+        dicter3 = {}
+        if filename.endswith("png") or filename.endswith("jpg"):
+            print(filename)
+            img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (1080, 720))
+            detections = self.detector.detect_faces(img)
+            if len(detections) > 1:
+                print("AAAAAAAAA")
+            for detection in detections:
+                confidence = detection["confidence"]#
+                if confidence > 0.9:
+                    x, y, w, h = detection["box"]
+                    detected_face = img[int(y):int(y + h), int(x):int(x + w)]##
+                    image = cv2.rectangle(img, (int(x), int(y)), (int(x+w), int(y+h)), (255, 0, 0), 2)
+                    embedding = DeepFace.represent(detected_face, model_name='Facenet', enforce_detection=False)
+                    ebd = embedding[0]["embedding"]
+                    dicter3[1] = ebd
+                    data_usa = pd.DataFrame.from_dict(dicter3.items())
+                    data_usa.rename(columns={0: "id", 1: "embd"}, inplace=True, errors="ignore")
+                    new_cols = pd.DataFrame(data_usa['embd'].apply(pd.Series))
+                    df_usa = pd.concat([data_usa, new_cols], axis=1)
+                    df_usa.drop(["embd"], axis=1, inplace=True, errors="ignore")
+                    #df_usa["id"] = df_usa["id"].apply(lambda x: str(x)[:str(x).find("_")])
+                    y, X = df_usa["id"], df_usa.drop(["id"], axis=1)
+                    result = self.catboost_model_usa.predict(X)
+                    index_usa = result[0][0]
+                    print(result)
+                    string = self.name_usa[result[0][0]]
+                    encoded_string = string.encode('utf-8')  # Кодируем строку в UTF-8
+                    binary_representation = ''.join(format(byte, '08b') for byte in encoded_string)
+                    con.send(binary_representation.encode('utf-8'))
+                    con.close()
+                    print(self.name_usa[result[0][0]])
+                    for images in os.listdir(f'C://Users//fatik//PycharmProjects//UfaHack2024//data//actors_usa//{index_usa}'):
+                        image_usa = cv2.imread(os.path.join(f'C://Users//fatik//PycharmProjects//UfaHack2024//data//actors_usa//{index_usa}', images))
+                        break
+                    image[0:128, 0:128] = cv2.resize(cv2.cvtColor(image_usa, cv2.COLOR_BGR2RGB), (128,128))
+                    #print(np.argmax(result[0], axis=0))
+                    #print(len(result[0]))
+                    self.image = Image.fromarray(image)
+                    self.image_tk = ctk.CTkImage(self.image, size=(self.image.width, self.image.height))
+                    self.label = ctk.CTkLabel(self, image=self.image_tk, text=self.name_usa[result[0][0]], text_color="red")
+                    self.label.grid(row=3, column=0, pady=10, sticky="ew")