import visualize
import ai
if __name__ == "__main__":
field_size = (30, 30)
generation_size = 10
surviver_count = 5 #the amount of fittest selected to breed the next generation
child_per_survivor = generation_size // surviver_count
mutation = 1
generation = 1
generations = []
for i in range(generation_size):
game = snake.Game(
field_size, snake.Snake(snake.Vector(14, 14), snake.Vector.UP, 3))
model = ai.create_model(field_size)
generations.append((model, game))
vis = visualize.Visualizer((600, 450), "Snake", 5, (255, 0, 0),
(0, 255, 0))
last_update = 0
update_frequency = 200
max_ticks = 50
tick_count = 0
dead = 0
while vis.running:
vis.begin_frame()
for i in range(len(generations)): #display fields
x = i % 4
def video_processing(video_path, background):
face_mask_recognition_model = cv2.dnn.readNet(
'models/face_mask_recognition.prototxt',
'models/face_mask_recognition.caffemodel')
mask_detector_model = ai.create_model()
cap = cv2.VideoCapture(video_path)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
now_frame = 1
if not os.path.exists('outputs'):
os.mkdir('outputs')
out = None
colors = [(0, 255, 0), (0, 0, 255)]
labels = ['with_mask', 'without_mask']
# 1 프레임마다 반복
while cap.isOpened():
ret, image = cap.read()
if not ret:
break
height, width = image.shape[:2]
blob = cv2.dnn.blobFromImage(image,
scalefactor=1.,
size=(300, 300),
mean=(104., 177., 123.))
face_mask_recognition_model.setInput(blob)
face_locations = face_mask_recognition_model.forward()
result_image = image.copy()
# 예상에 대해 적합도를 측정
for i in range(face_locations.shape[2]):
confidence = face_locations[0, 0, i, 2]
if confidence < 0.5:
continue
# 얼굴의 위치값을 받아옴
left = int(face_locations[0, 0, i, 3] * width)
top = int(face_locations[0, 0, i, 4] * height)
right = int(face_locations[0, 0, i, 5] * width)
bottom = int(face_locations[0, 0, i, 6] * height)
face_image = image[top:bottom, left:right]
face_image = cv2.resize(face_image, dsize=(224, 224))
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
predict = ai.predict(mask_detector_model, face_image)
cv2.rectangle(result_image,
pt1=(left, top),
pt2=(right, bottom),
thickness=2,
color=colors[predict],
lineType=cv2.LINE_AA)
cv2.putText(result_image,
text=labels[predict],
org=(left, top - 10),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.8,
color=colors[predict],
thickness=2,
lineType=cv2.LINE_AA)
if out is None:
out = cv2.VideoWriter('outputs/output.wmv', fourcc,
cap.get(cv2.CAP_PROP_FPS),
(image.shape[1], image.shape[0]))
else:
out.write(result_image)
# (10/400): 11%, 영상 데이터 변환
print('(' + str(now_frame) + '/' + str(frame_count) + '): ' +
str(now_frame * 100 // frame_count) + '%')
now_frame += 1
# OPENCV 출력
if not background:
cv2.imshow('result', result_image)
if cv2.waitKey(1) == ord('q'):
break
out.release()
cap.release()