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UI.py
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UI.py
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import os
import tkinter as tk
from tkinter import filedialog, Label, Frame, Button
from PIL import Image as PilImage, ImageTk
import numpy as np
import joblib
import skimage.io
from skimage.transform import resize
import cv2
from model.Image import Image
from sklearn.preprocessing import StandardScaler
class App:
def __init__(self, master):
self.master = master
master.title("People Detection")
self.screen_width = master.winfo_screenwidth()
self.screen_height = master.winfo_screenheight()
self.title = Frame(master, bg='white', height=self.screen_height * 0.1)
self.title.pack(side=tk.TOP, fill=tk.X, expand=False)
self.title_label = Label(self.title, text="People detection with an artificial neural network", bg='white', font=("Arial", 18))
self.title_label.pack(pady=20)
self.image_frame = Frame(master, bg='white', height= self.screen_height * 0.5)
self.image_frame.pack(side=tk.TOP, fill=tk.X, expand=False)
self.legend_frame = Frame(master, bg='white', height=self.screen_height * 0.2)
self.legend_frame.pack(side=tk.TOP, padx= self.screen_width * 0.35,expand=False)
self.control_frame = Frame(master, bg='white', height=self.screen_height * 0.2)
self.control_frame.pack(side=tk.BOTTOM, fill=tk.X, expand=False)
self.model = joblib.load('model.pkl') # Carga el modelo entrenado
self.images = []
self.current_image_index = 0
# Botones en el panel de control
self.btn_selectImage = Button(self.control_frame, text="Select Image", command=self.open_image)
self.btn_selectImage.config(height=4, width=40)
self.btn_selectImage.pack(pady=40, side=tk.TOP)
def open_image(self):
file_path = filedialog.askopenfilename()
if file_path:
self.process_image(file_path)
def process_image(self, image_path):
image = skimage.io.imread(image_path)
image = cv2.resize(image, (1024, 1024))
grid_size = 128
num_grids = 1024 // grid_size
images = [image]
grids = [[img[i*grid_size:(i+1)*grid_size, j*grid_size:(j+1)*grid_size] for i in range(num_grids) for j in range(num_grids)] for img in images]
feature_vectors = []
for grid in grids[0]:
imgObj = Image(grid, "grid_V1_4_40_P.png")
feature_vector = np.array(imgObj.generateFeatureVector())
feature_vectors.append(feature_vector)
scaler = StandardScaler()
feature_vectors = scaler.fit_transform(feature_vectors)
predictions = self.model.predict(feature_vectors)
image_pred = self.create_prediction_image(predictions, grid_size, num_grids)
image_person = np.copy(image)
for i in range(image_pred.shape[0]):
for j in range(image_pred.shape[1]):
if np.array_equal(image_pred[i, j], [76, 175, 80]):
pass
else:
image_person[i, j] = [0, 0, 0]
gray = cv2.cvtColor(image_person, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(image_person, contours, -1, (76, 175, 80), 5)
for i in range(image_person.shape[0]):
for j in range(image_person.shape[1]):
if np.array_equal(image_person[i, j], [0, 0, 0]):
image_person[i, j] = image[i, j]
size_display = (int(self.screen_width * 0.248), int(self.screen_width * 0.248))
image = cv2.resize(image, (size_display))
image_pred = cv2.resize(image_pred, (size_display))
image_fused = cv2.addWeighted(image, 0.5, image_pred, 0.5, 0)
image_person = cv2.resize(image_person, (size_display))
self.display_results(image, image_pred, image_fused, image_person)
def create_prediction_image(self, predictions, grid_size, num_grids):
image_pred = np.zeros((1024, 1024, 3), dtype=np.uint8)
for i in range(num_grids):
for j in range(num_grids):
index = i * num_grids + j
color = [255, 152, 0] if predictions[index] == 0 else [33, 150, 243] if predictions[index] == 1 else [76, 175, 80]
image_pred[i*grid_size:(i+1)*grid_size, j*grid_size:(j+1)*grid_size] = color
return image_pred
def display_results(self, image, image_pred, image_fused, image_person):
for widget in self.image_frame.winfo_children():
widget.destroy()
image = PilImage.fromarray(image)
image = ImageTk.PhotoImage(image)
label = Label(self.image_frame, image=image)
label.image = image
label.pack(side=tk.LEFT, pady=30)
image_pred = PilImage.fromarray(image_pred)
image_pred = ImageTk.PhotoImage(image_pred)
label_pred = Label(self.image_frame, image=image_pred)
label_pred.image = image_pred
label_pred.pack(side=tk.LEFT, pady=30)
image_fused = PilImage.fromarray(image_fused)
image_fused = ImageTk.PhotoImage(image_fused)
label_fused = Label(self.image_frame, image=image_fused)
label_fused.image = image_fused
label_fused.pack(side=tk.LEFT, pady=30)
image_person = PilImage.fromarray(image_person)
image_person = ImageTk.PhotoImage(image_person)
label_person = Label(self.image_frame, image=image_person)
label_person.image = image_person
label_person.pack(side=tk.LEFT, pady=30)
self.display_legend()
def display_legend(self):
for widget in self.legend_frame.winfo_children():
widget.destroy()
A = PilImage.new('RGB', (50, 50), color=(255, 152, 0))
A = ImageTk.PhotoImage(A)
label_A = Label(self.legend_frame, image=A)
label_A.image = A
label_A.pack(side=tk.LEFT, padx=10, pady=50)
label_A_text = Label(self.legend_frame, text="No person", bg='white')
label_A_text.pack(side=tk.LEFT, padx=10, pady=50)
animal = PilImage.new('RGB', (50, 50), color=(33, 150, 243))
animal = ImageTk.PhotoImage(animal)
label_animal = Label(self.legend_frame, image=animal)
label_animal.image = animal
label_animal.pack(side=tk.LEFT, padx=10, pady=50)
label_animal_text = Label(self.legend_frame, text="Animal", bg='white')
label_animal_text.pack(side=tk.LEFT, padx=10, pady=50)
person = PilImage.new('RGB', (50, 50), color=(76, 175, 80))
person = ImageTk.PhotoImage(person)
label_person = Label(self.legend_frame, image=person)
label_person.image = person
label_person.pack(side=tk.LEFT, padx=10, pady=50)
label_person_text = Label(self.legend_frame, text="Person", bg='white')
label_person_text.pack(side=tk.LEFT, padx=10, pady=50)
if __name__ == '__main__':
root = tk.Tk()
root.state('zoomed')
root.configure(bg='white')
app = App(root)
root.mainloop()