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DetectFaceUsingPhoto.py
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DetectFaceUsingPhoto.py
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import numpy as np
import cv2
import matplotlib.pyplot as plt
import time
def convertToRGB(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
haar_face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt.xml')
test1 = cv2.imread('Images/1.jpeg')
gray_img = cv2.cvtColor(test1, cv2.COLOR_BGR2GRAY)
plt.imshow(gray_img, cmap='gray')
faces = haar_face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)
print('Faces found: ', len(faces))
for (x, y, w, h) in faces:
cv2.rectangle(test1, (x, y), (x + w, y + h), (0, 255, 0), 2)
plt.imshow(convertToRGB(test1))
def detect_faces(f_cascade, colored_img, scaleFactor=1.1):
img_copy = np.copy(colored_img)
gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
faces = f_cascade.detectMultiScale(gray, scaleFactor=scaleFactor, minNeighbors=5);
for (x, y, w, h) in faces:
cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
return img_copy
#2nd Test
test2 = cv2.imread('Images/2.jpeg')
faces_detected_img = detect_faces(haar_face_cascade, test2)
plt.imshow(convertToRGB(faces_detected_img))
test2 = cv2.imread('Images/3.jpeg')
faces_detected_img = detect_faces(haar_face_cascade, test2)
plt.imshow(convertToRGB(faces_detected_img))
test2 = cv2.imread('Images/4.jpeg')
faces_detected_img = detect_faces(haar_face_cascade, test2, scaleFactor=1.2)
plt.imshow(convertToRGB(faces_detected_img))
lbp_face_cascade = cv2.CascadeClassifier('data/lbpcascade_frontalface.xml')
test2 = cv2.imread('Images/2.jpeg')
faces_detected_img = detect_faces(lbp_face_cascade, test2)
plt.imshow(convertToRGB(faces_detected_img))
test2 = cv2.imread('Images/3.jpeg')
faces_detected_img = detect_faces(lbp_face_cascade, test2)
plt.imshow(convertToRGB(faces_detected_img))
haar_face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt.xml')
lbp_face_cascade = cv2.CascadeClassifier('data/lbpcascade_frontalface.xml')
test1 = cv2.imread('Images/5.jpeg')
test2 = cv2.imread('Images/6.jpeg')
t1 = time.time()
haar_detected_img = detect_faces(haar_face_cascade, test1)
t2 = time.time()
dt1 = t2 - t1
t1 = time.time()
lbp_detected_img = detect_faces(lbp_face_cascade, test1)
t2 = time.time()
dt2 = t2 - t1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.set_title('Haar Detection time: ' + str(round(dt1, 3)) + ' secs')
ax1.imshow(convertToRGB(haar_detected_img))
ax2.set_title('LBP Detection time: ' + str(round(dt2, 3)) + ' secs')
ax2.imshow(convertToRGB(lbp_detected_img))
t1 = time.time()
haar_detected_img = detect_faces(haar_face_cascade, test2)
t2 = time.time()
dt1 = t2 - t1
t1 = time.time()
lbp_detected_img = detect_faces(lbp_face_cascade, test2)
t2 = time.time()
dt2 = t2 - t1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.set_title('Haar Detection time: ' + str(round(dt1, 3)) + ' secs')
ax1.imshow(convertToRGB(haar_detected_img))
ax2.set_title('LBP Detection time: ' + str(round(dt2, 3)) + ' secs')
ax2.imshow(convertToRGB(lbp_detected_img))