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decision_tree.py
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decision_tree.py
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import cv2
import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import skimage.io as io
import os
import numpy as np
from skimage import feature
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from PIL import Image
import cv2
from skimage.feature import local_binary_pattern
from sklearn.decomposition import PCA
# Define the directory where the hand gesture images are stored
dataset_dir = "dataset\dataset\Woman"
# dataset_dir = "dataset_sample\Women"
images = []
labels = []
descriptors = []
features=[]
arr=[]
# Define the HOG parameters
pixels_per_cell = (8, 8)
cells_per_block = (2, 2)
num_orientations = 9
for sub_dir in os.listdir(dataset_dir):
sub_dir_path = os.path.join(dataset_dir, sub_dir)
if not os.path.isdir(sub_dir_path):
continue
# Iterate through each image file in the subdirectory
for file_name in os.listdir(sub_dir_path):
if not file_name.endswith(".JPG"):
continue
image_path = os.path.join(sub_dir_path, file_name)
# Load the image and compute its HOG features
# image = np.asarray(Image.open(image_path))
image = cv2.imread(image_path)
image= cv2.resize(image,(128,128))
# cv2.namedWindow('mask', cv2.WINDOW_NORMAL)
# cv2.resizeWindow('mask', 800, 600)
# cv2.imshow('mask', image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# image = cv2.resize(image, (256, 256))
ycrcb = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)
# Apply a skin color range filter to the YCrCb image
lower_skin = np.array([0, 135, 85])
upper_skin = np.array([255, 180, 135])
mask = cv2.inRange(ycrcb, lower_skin, upper_skin)
# cv2.namedWindow('mask', cv2.WINDOW_NORMAL)
# cv2.resizeWindow('mask', 800, 600)
# cv2.imshow('mask', mask)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# Define HOG parameters
win_size = (64, 64)
block_size = (16, 16)
block_stride = (8, 8)
cell_size = (8, 8)
nbins = 9
# Initialize HOG descriptor
hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, nbins)
# Compute HOG features
hog_features = hog.compute(mask)
# print(hog_features.shape)
# print(hog_features)
features.append(hog_features)
print(sub_dir)
labels.append(sub_dir)
# descriptors = np.vstack(descriptors)
# descriptors.append(des)
# descriptors = np.array(descriptors)
features = np.array(features)
# total=np.concatenate((descriptors, features), axis=1)
# print('hog',features)
# print('hof shape',features.shape)
# surf_des=np.array(surf_des)
labels = np.array(labels)
# print(surf_des.shape)
# descriptors = descriptors.reshape(descriptors.shape[0], descriptors.shape[1])
# descriptors = np.reshape(descriptors, (len(labels), -1))
# print('sift shape',descriptors.shape)
print(labels.shape)
# print('sift',descriptors)
# for image in images:
# kp, des = sift.detectAndCompute(image, None)
# descriptors.append(des)
# descriptors = np.array(descriptors)
# labels = np.array(labels)
# Split the dataset into training and testing sets
train_features, test_features, train_labels, test_labels = train_test_split(
features, labels, test_size=0.25, random_state=42)
print('Shape of train_images:', train_features.shape)
print('Shape of train_labels:', train_labels.shape)
print('Shape of test_images:', test_features.shape)
print('Shape of test_labels:', test_labels.shape)
# Train a decision tree classifier
clf = DecisionTreeClassifier(max_depth=1000, criterion='entropy')
clf.fit(train_features, train_labels)
# Predict the labels of the test set using the trained SVM classifier
predicted_labels = clf.predict(test_features)
# Compute the accuracy of the SVM classifier
accuracy = accuracy_score(test_labels, predicted_labels)
print("Accuracy: {:.2f}%".format(accuracy * 100))
# # Split data into training and testing sets
# train_descriptors, test_descriptors, train_labels, test_labels = train_test_split(
# descriptors, labels, test_size=0.2, random_state=42)
# # Train the SVM classifier
# clf = svm.SVC(kernel='linear')
# clf.fit(train_descriptors, train_labels)
# # Load new test images
# test_images = []
# test_dir_path = 'test_images'
# for filename in os.listdir(test_dir_path):
# img = cv2.imread(os.path.join(test_dir_path, filename))
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# test_images.append(gray)
# # Extract SIFT features from test images
# test_descriptors = []
# for image in test_images:
# kp, des = sift.detectAndCompute(image, None)
# test_descriptors.append(des)
# test_descriptors = np.array(test_descriptors)
# # Classify test images using SVM classifier
# predicted_labels = clf.predict(test_descriptors)
# # Print predicted labels
# print(predicted_labels)
# # Evaluate accuracy on test set
# accuracy = accuracy_score(test_labels, predicted_labels)
# print("Accuracy:", accuracy)