Hapus komentar dari file python
#Importing all the important models and install them if not installed on your device
import cv2
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from PIL import Image
import PIL.ImageOps
import os, ssl, time
#Fetching the data
X = np.load('image.npz')['arr_0']
y = pd.read_csv("labels.csv")["labels"]
print(pd.Series(y).value_counts())
classes = ['A', 'B', 'C', 'D', 'E','F', 'G', 'H', 'I', 'J', "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]
nclasses = len(classes)
#Splitting the data and scaling it
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=9, train_size=3500, test_size=500)
#scaling the features
X_train_scaled = X_train/255.0
X_test_scaled = X_test/255.0
#Fitting the training data into the model
clf = LogisticRegression(solver='saga', multi_class='multinomial').fit(X_train_scaled, y_train)
#Calculating the accuracy of the model
y_pred = clf.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print("The accuracy is :- ",accuracy)
# #Starting the camera
# cap = cv2.VideoCapture(0)
# print("camera started")
# while(True):
# # Capture frame-by-frame
# try:
# ret, frame = cap.read()
# print("Inside the loop")
# # Our operations on the frame come here
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# #Drawing a box in the center of the video
# height, width = gray.shape
# upper_left = (int(width / 2 - 56), int(height / 2 - 56))
# bottom_right = (int(width / 2 + 56), int(height / 2 + 56))
# cv2.rectangle(gray, upper_left, bottom_right, (0, 255, 0), 2)
# #To only consider the area inside the box for detecting the digit
# #roi = Region Of Interest
# roi = gray[upper_left[1]:bottom_right[1], upper_left[0]:bottom_right[0]]
# #Converting cv2 image to pil format
# im_pil = Image.fromarray(roi)
# # convert to grayscale image - 'L' format means each pixel is
# # represented by a single value from 0 to 255
# image_bw = im_pil.convert('L')
# image_bw_resized = image_bw.resize((28,28), Image.ANTIALIAS)
# #invert the image
# image_bw_resized_inverted = PIL.ImageOps.invert(image_bw_resized)
# pixel_filter = 20
# #converting to scalar quantity
# min_pixel = np.percentile(image_bw_resized_inverted, pixel_filter)
# #using clip to limit the values between 0,255
# image_bw_resized_inverted_scaled = np.clip(image_bw_resized_inverted-min_pixel, 0, 255)
# max_pixel = np.max(image_bw_resized_inverted)
# #converting into an array
# image_bw_resized_inverted_scaled = np.asarray(image_bw_resized_inverted_scaled)/max_pixel
# #creating a test sample and making a prediction
# test_sample = np.array(image_bw_resized_inverted_scaled).reshape(1,784)
# test_pred = clf.predict(test_sample)
# print("Predicted class is: ", test_pred)
# # Display the resulting frame
# cv2.imshow('frame',gray)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
# except Exception as e:
# pass
# # When everything done, release the capture
# cap.release()
# cv2.destroyAllWindows()
