def test_faces(capsys):
file_name = os.path.join(
os.path.dirname(__file__),
'resources/face_no_surprise.jpg')
detect.detect_faces(file_name)
out, _ = capsys.readouterr()
assert 'Likelihood.POSSIBLE' in out
def app():
file_input_column = [[sg.Text('Выберите изображение')],
[
sg.In(key='-file_name-', enable_events=True),
sg.FileBrowse('Найти',
file_types=(("PNG files", "*.png"),
("JPG files", "*.jpg"),
("ALL files", "*")),
target='-file_name-')
]]
layout = [[sg.Column(file_input_column, justification='center')],
[sg.Column([[sg.Image(key='-image-')]], justification='center')],
[
sg.Button('Поиск лиц', key='-detect-'),
sg.SaveAs('Сохранить', target='-save-'),
sg.InputText(key='-save-',
do_not_clear=False,
enable_events=True,
visible=False)
]]
window = sg.Window('Нахождение лиц', layout, resizable=True, finalize=True)
while True:
event, values = window.read()
if event == '-file_name-':
file_name = values['-file_name-']
try:
window["-image-"].update(data=load_image(file_name))
except:
pass
elif event == '-detect-':
if window["-image-"].get_size() != (2, 2):
temp_path = os.path.join(os.getcwd(), "temp/temp.png")
detect_faces(values['-file_name-'], temp_path)
window["-image-"].update(data=load_image(temp_path))
elif event == '-save-':
if window["-image-"].get_size() != (2, 2):
path = values['-save-']
if path and os.path.exists("temp/temp.png"):
im = Image.open("temp/temp.png")
im.save(values['-save-'])
elif event == sg.WIN_CLOSED:
try:
os.remove("temp/temp.png")
except:
pass
break
window.close()
def is_correct(frame, previous=None):
if _change_scene_threshold is not None and previous is not None:
score, _ = structural_similarity(_small_img(frame), _small_img(previous), full=True, multichannel=True)
if 1 - score > _change_scene_threshold:
raise CheckFrameException("large difference with previous frame, probably another scene")
faces = detect.detect_faces(frame, threshold=_face_detect_threshold)
if len(faces) != 1:
raise CheckFrameException("detected %d faces, expected 1" % len(faces))
def oreintation(tilt, pan):
out = False
if tilt >= 3 and tilt <= 12:
if pan <= 7 and pan >= 1:
out = 1
elif pan >= -7 and pan <= -1:
out = 2
else:
out = out
elif tilt <= -3 and tilt >= 12:
if pan <= 7 and pan >= 1:
out = 3
elif pan >= -7 and pan <= -1:
out = 4
else:
out = out
else:
out = out
return out
detect_faces(path)
def recognize_face(recognizer, image, face_cascade, eye_cascade, face_size, threshold):
found_faces = []
gray, faces = detect_faces(image, face_cascade, eye_cascade, return_gray=1)
# If faces are found, try to recognize them
for ((x, y, w, h), eyedim) in faces:
label, confidence = recognizer.predict(cv2.resize(level_face(gray, ((x, y, w, h), eyedim)), face_size))
if confidence < threshold:
found_faces.append((label, confidence, (x, y, w, h)))
return found_faces
def _extract_faces(a_dir, folder, do_level_face=False):
faceCascade = cv2.CascadeClassifier(config.FACE_CASCADE_FILE)
eyeCascade = cv2.CascadeClassifier(config.EYE_CASCADE_FILE)
the_path = join(a_dir, folder)
result = []
for img in [f for f in os.listdir(the_path) if _supported_img(f)]:
img_path = join(the_path, img)
image, faces = detect_faces(cv2.imread(img_path), faceCascade, eyeCascade, True)
if len(faces) == 0:
print("No face found in " + img_path)
for ((x, y, w, h), eyedim) in faces:
if not do_level_face:
result.append(image[y:y+h, x:x+w])
else:
result.append(level_face(image, ((x, y, w, h), eyedim)))
#result.append(image[y:y+h, x:x+w])
return result
import cv2
import matplotlib.pyplot as plt
import time
import config
from detect import detect_faces
# opencv loads an image into BGR color space by default
def read_img(imgpath):
return cv2.imread(imgpath)
if config.test:
detect_faces(read_img('data/test/test2.jpg'), 1.1, True)
from detect import detect_faces
from detect import detect_faces_uri
if __name__ == "__main__":
file = 'image002.jpg'
if len(sys.argv) > 1:
for i in range(1, len(sys.argv)):
file = sys.argv[i]
response = detect_faces(file)
