class Objectdetecter:
def __init__(self):
self.detecter = Detecter()
self.detecter.setup(
'./data/ssd_mobilenet_v1/frozen_inference_graph.pb',
'./data/label_map.pbtxt')
self.THRESHOLD = 0.8
def __check(self, image):
detect_image = np.copy(image)
image = np.expand_dims(image, axis=0)
(boxes, scores, classes, num) = self.detecter.detect(image)
list = []
self.detecter.viaulize(detect_image, boxes, classes, scores,
self.THRESHOLD)
for output in zip(classes, scores, boxes):
if output[1] >= self.THRESHOLD:
y1, x1, y2, x2 = output[2]
y = int((y2 - y1) * 480)
list.append((output, y))
return list, detect_image
def order(self, image):
lists, detect_image = self.__check(image)
sign = 5
tra = 5
st = 300
fw = 5
for list in lists:
if list[0][0] == 5:
c = list[1]
if c > sign:
return 'stopsign', detect_image
elif list[0][0] == 6:
c = list[1]
if c > tra:
return 'trash', detect_image
elif list[0][0] == 1:
c = list[1]
if c < st:
return 'stop', detect_image
elif list[0][0] == 2:
c = list[1]
if c > fw:
return 'forward', detect_image
return None, detect_image
class ImageViewer(Thread):
def __init__(self, name, queue):
super().__init__()
self.name = name
self.queue = queue
self.THRESHOLD = 0.25
self.detecter = Detecter()
self.detecter.setup('./frozen_inference_graph.pb',
'./mscoco_label_map.pbtxt')
pass
def run(self):
while (True):
frame = self.queue.get(timeout=2)
frame_ex = np.expand_dims(frame, axis=0)
(boxes, scores, classes, num) = self.detecter.detect(frame_ex)
self.detecter.viaulize(frame, boxes, classes, scores,
self.THRESHOLD)
cv2.imshow(self.name, frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
pass
print('go')
# 테스트 이미지 파일 리스트
TEST_IMAGE_PATHS = ['Traffic-Lights.jpg']
THRESHOLD = 0.3
detecter = Detecter()
detecter.setup('./frozen_inference_graph.pb', './mscoco_label_map.pbtxt')
for image_path in TEST_IMAGE_PATHS:
image, image_ex = get_detect_image(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
(boxes, scores, classes, num) = detecter.detect(image_ex)
detecter.viaulize(image, boxes, classes, scores, THRESHOLD)
cv2.imshow('image', image)
object_list = filter(lambda item: item[1] > THRESHOLD,
zip(boxes, scores, classes))
(height, width, _) = image.shape
for ix, object in enumerate(object_list):
box = object[0]
classes = object[2] # 분류맵
(sy, sx, ey, ex) = (int(box[0] * height), int(box[1] * width),
int(box[2] * height), int(box[3] * width))
sub_image = image[sy:ey, sx:ex]
imgName = "object." + str(ix)
# print(sub_image.shape)