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hog_test.py
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hog_test.py
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import matplotlib.pyplot as plt
from skimage.feature import hog
from skimage import data, exposure
import cv2
import os
from sklearn.linear_model import LogisticRegression
import numpy as np
import time
def main():
path = os.listdir("cropped1")
data0 = []
data1 = []
data = []
labels = []
total = 0
# f_prime = np.array([16,44,37,62,22,28,66,2,40,14,11,54,42,30,36,51,67,27,33,32])
# f_prime = np.array([16,44,37,28,66,40,22,2,33,11,32,48,35,14,39,56,62,53,30,27,54,51,68,71,31])
f_prime = np.array([16, 44, 37, 28, 36, 66, 2, 22, 32, 48, 56, 14, 62, 42, 54, 30, 27, 31, 72, 24, 41, 25, 51, 55, 38]) - np.ones((1,25), int)
f_prime =f_prime[0]
for i in path:
#Load image
img = cv2.imread("cropped1/"+i)
# print(i)
#Resize image
# res_img = cv2.resize(img, (48,48), interpolation=cv2.INTER_AREA)
#Get HOG features
a = time.time()
fd = hog(img, orientations=8, pixels_per_cell=(16,16), cells_per_block=(1,1),
multichannel=True, block_norm="L2-Hys", feature_vector=True)
# print(time.time() - a)
total+=time.time() - a
data.append(fd[f_prime])
#Negative and positive classes
if i == "2.jpg": print(fd)
try:
name_to_int = int(i.split(".")[0])
# print(name)
if name_to_int < 1000:
labels.append(1)
except:
labels.append(0)
# print(type(fd))
model = LogisticRegression().fit(data,np.transpose(labels))
pred = model.predict(data)
print("Training Accuracy: " + str(model.score(data, np.transpose(labels))))
tp = 0
tn = 0
fp = 0
fn = 0
for i in range(len(pred)):
if pred[i] == labels[i]:
if pred[i] == 1:
tn += 1
elif pred[i] == 0:
tp += 1
else:
if pred[i] == 0 and labels[i] == 1:
fp += 1
elif pred[i] == 1 and labels[i] == 0:
fn += 1
recall = tp/(tp+fn)
precision = tp/(tp+fp)
f1 = 2* precision*recall/(precision+recall)
print("Recall: " + str(recall))
print("Precision: " + str(precision))
print("F1 score: " + str(f1))
print("TP: "+str(tp))
print("FP: "+str(fp))
print(data[43].shape)
num = 50
a = time.time()
b = model.predict(np.expand_dims(data[num], axis=0))
print("PRED TIME: " + str(time.time() - a))
print("TIME: " + str(total/len(path)))
print("Actual: {0}, pred: {1}".format(labels[num], b))
if __name__ == "__main__":
main()