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face_detection.py
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face_detection.py
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import numpy as np
import pickle
from viola_jones import ViolaJones
from cascade import CascadeClassifier
import time
def train_viola(t):
with open("training.pkl", 'rb') as f:
training = pickle.load(f)
clf = ViolaJones(T=t)
clf.train(training, 2429, 4548)
evaluate(clf, training)
clf.save(str(t))
def test_viola(filename):
with open("test.pkl", 'rb') as f:
test = pickle.load(f)
clf = ViolaJones.load(filename)
evaluate(clf, test)
def train_cascade(layers, filename="Cascade"):
with open("training.pkl", 'rb') as f:
training = pickle.load(f)
clf = CascadeClassifier(layers)
clf.train(training)
evaluate(clf, training)
clf.save(filename)
def test_cascade(filename="Cascade"):
with open("test.pkl", "rb") as f:
test = pickle.load(f)
clf = CascadeClassifier.load(filename)
evaluate(clf, test)
def evaluate(clf, data):
correct = 0
all_negatives, all_positives = 0, 0
true_negatives, false_negatives = 0, 0
true_positives, false_positives = 0, 0
classification_time = 0
for x, y in data:
if y == 1:
all_positives += 1
else:
all_negatives += 1
start = time.time()
prediction = clf.classify(x)
classification_time += time.time() - start
if prediction == 1 and y == 0:
false_positives += 1
if prediction == 0 and y == 1:
false_negatives += 1
correct += 1 if prediction == y else 0
print("False Positive Rate: %d/%d (%f)" % (false_positives, all_negatives, false_positives/all_negatives))
print("False Negative Rate: %d/%d (%f)" % (false_negatives, all_positives, false_negatives/all_positives))
print("Accuracy: %d/%d (%f)" % (correct, len(data), correct/len(data)))
print("Average Classification Time: %f" % (classification_time / len(data)))