def original_image_processing(filename):
input_img = plt.imread(filename)
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade.
detector = Cascade(trained_file)
# Detect faces with scale factor to 1.2 and step ratio to 1
detected = detector.detect_multi_scale(img=input_img,
scale_factor=1.2,
step_ratio=1,
min_size=(50, 50),
max_size=(200, 200))
original_img = np.copy(input_img)
original_img = input_img / 255
i = 0
for d in detected:
i += 1
# Obtain the face cropped from detected coordinates
face = get_face_coordinates(d, input_img)
print("Face -", i)
plt.imshow(face)
plt.show()
blurred_face = gaussian(face, multichannel=True, sigma=8)
print("Blurred Face -", i)
plt.imshow(blurred_face)
plt.show()
resulting_image = merge_with_original(d, original_img, blurred_face)
return resulting_image
def re():
dir_path = os.path.dirname(os.path.realpath(__file__))
cwd = os.getcwd()
print(cwd)
os.chdir(dir_path)
image1 = ""
image = io.imread("somefilename.jpg")
# Load the trained file from data
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade
detector = Cascade(trained_file)
detected = detector.detect_multi_scale(img=image,
scale_factor=1.2,
step_ratio=1,
min_size=(50, 50),
max_size=(100, 100))
resulting_image = image
for detected_face in detected:
face = get_face(image, detected_face)
blurred_face = gaussian(face, multichannel=True, sigma=10)
#show_image(face)
resulting_image = merge_blurry_face(image, detected_face, blurred_face)
show_image(resulting_image, "Blurred faces")
os.remove("somefilename.jpg")
return 'Its Done!'
def load_weight(self, weight_path: Path) -> Any:
if weight_path.exists():
# TODO: Implement loading weight file from local path
pass
else:
logger.warning("No existing path was provided, using default weights")
weight_path = skimage_data.lbp_frontal_face_cascade_filename()
return weight_path
def test_detector_astronaut():
# Load the trained file from the module root.
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade.
detector = Cascade(trained_file)
img = data.astronaut()
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(60, 60),
max_size=(123, 123))
assert len(detected) == 1, 'One face should be detected.'
def test_detector_astronaut():
# Load the trained file from the module root.
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade.
detector = Cascade(trained_file)
img = data.astronaut()
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(60, 60),
max_size=(123, 123))
assert len(detected) == 1, 'One face should be detected.'
def face_detections(image,
scale_factor=1.1,
step_ratio=1.3,
min_size=(10, 10),
max_size=(400, 400)):
# Load the trained file from data
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade
detector = Cascade(trained_file)
# Detect faces with min and max size of searching window
detected_faces = detector.detect_multi_scale(
img=image,
scale_factor=scale_factor,
step_ratio=step_ratio,
min_size=min_size,
max_size=max_size,
)
return detected_faces
def fd():
from skimage.feature import Cascade
from skimage import data, color
import matplotib.pyplot as plt
from matplotlib.patches import Rectangle
path = ''
img = plt.imread(path)
plt.axis('off')
plt.imshow(img)
train_set = data.lbp_frontal_face_cascade_filename()
detector = Cascade(train_set)
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(10, 10),
max_size=(200, 200))
print('Detected')
def show_detected_face(result, detected, title="Detected Faces"):
plt.imshow(result)
img_desc = plt.gca()
plt.set_cmap('gray')
plt.title(title)
plt.axis('off')
for rec in detected:
img_desc.add_patch(
Rectangle((rec['c'], rec['r']),
rec['width'],
rec['height'],
fill=False,
color='g',
line_width=3))
plt.show()
show_detected_face(img, detected)
leaves the clusters that have a same or bigger number of detections in them.
You should also take into account that false detections are inevitable and if
you want to have a really precise detector, you will have to train it yourself
using `OpenCV train cascade utility
<https://docs.opencv.org/2.4/doc/user_guide/ug_traincascade.html>`_.
"""
from skimage import data
from skimage.feature import Cascade
import matplotlib.pyplot as plt
from matplotlib import patches
# Load the trained file from the module root.
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade.
detector = Cascade(trained_file)
img = data.astronaut()
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(60, 60),
max_size=(123, 123))
plt.imshow(img)
img_desc = plt.gca()
plt.set_cmap('gray')
#header files required
from skimage.feature import Cascade
from skimage import data
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
#Getting the image
path = ''
img = plt.imread(path)
plt.axis('off')
plt.imshow(img)
#reading the dataset for facial detection
train_set = data.lbp_frontal_face_cascade_filename()
detector = Cascade(train_set)
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(10, 10),
max_size=(200, 200))
#function to draw a rectangle around the detected faces
def show_detected_face(result, detected, title='Detected Faces'):
plt.imshow(result)
desc = plt.gca()
plt.set_cmap('gray')
plt.title(title)
plt.axis('off')
for rec in detected:
desc.add_patch(
leaves the clusters that have a same or bigger number of detections in them.
You should also take into account that false detections are inevitable and if
you want to have a really precise detector, you will have to train it yourself
using `OpenCV train cascade utility
<https://docs.opencv.org/doc/user_guide/ug_traincascade.html>`_.
"""
from skimage import data
from skimage.feature import Cascade
import matplotlib.pyplot as plt
from matplotlib import patches
# Load the trained file from the module root.
trained_file = data.lbp_frontal_face_cascade_filename()
# Initialize the detector cascade.
detector = Cascade(trained_file)
img = data.astronaut()
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(60, 60),
max_size=(123, 123))
plt.imshow(img)
img_desc = plt.gca()
plt.set_cmap('gray')