def carregar_preparar_dados(self, empty_path = 'dataset/rawdataset/empty', occupied_path = 'dataset/rawdataset/occupied'):
empty = image_utils().load_image_from_path(empty_path)
occup = image_utils().load_image_from_path(occupied_path)
#print(empty)
train_empty = empty[0:100]
test_empty = empty[100:150]
train_occup = occup[0:100]
test_occup = occup[100:150]
train_empty = np.asarray(train_empty)
#print(train_empty.shape)
base_train = np.concatenate([train_empty, train_occup])
base_test = np.concatenate([test_empty, test_occup])
#print(base_train.shape)
#print(base_test.shape)
labels_train = np.zeros(200, dtype=np.int)
labels_train[100:200] = np.ones(100, dtype=np.int)
labels_test = np.zeros(100, dtype=np.int)
labels_test[50:100] = np.ones(50, dtype=np.int)
#print(labels_train[0:100])
train_zip = zip (base_train,labels_train)
test_zip = zip (base_test,labels_test)
return base_train,base_test, labels_train, labels_test
def training (self):
base_train,base_test, labels_train, labels_test = self.carregar_preparar_dados()
features_train = image_utils().extract_features(base_train)
features_test = image_utils().extract_features(base_test)
SVM().train(features_train,labels_train,features_test,labels_test)
#SVM().test(features_test,labels_test)
features_test = features_test[94].reshape(1,-1)
SVM().predict(features_test)
def testing (self):
base_train,base_test, labels_train, labels_test = self.carregar_preparar_dados()
features_train = image_utils().extract_features(base_train)
features_test = image_utils().extract_features(base_test)
SVM().test(features_test,labels_test)
#Call_SVM().training()
def generateVideo(boxes, statusWindow):
path = 'videos/p2.mp4'
vs = cv2.VideoCapture(path)
fps = 12
capSize = (640, 360)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter()
success = out.open('./teste5.mp4', fourcc, fps, capSize, True)
num_frames = count_frames(path)
#print(num_frames)
get_point = 0
i = 0
try:
while i < num_frames:
ret, frame = vs.read()
frame = imutils.resize(frame, width=450)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#cv2.imshow("gray ", gray )
gau = cv2.GaussianBlur(gray, (7, 7), 0)
img_resize = image_utils.getRotateRect(gau, boxes)
feature = image_utils().extract_features(img_resize)
timestamp = datetime.datetime.now()
score = SVM().predict(feature)
available_slots = []
for index, scr in enumerate(score):
if scr == 0:
cv2.polylines(frame, np.int32([boxes[index]]), True,
(0, 0, 255), 2)
s = False
i = 0
else:
## if the parking slot is availble
slot_first_x_cord = float(boxes[index][1][0])
if (checkMinimum(available_slots, slot_first_x_cord)):
cv2.polylines(frame, np.int32([boxes[index]]), True,
(255, 0, 0), 2)
if s == False:
cv2.putText(
frame,
timestamp.strftime(
" Available: %d %m %Y %I:%M:%S"),
(10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 0, 0), 2)
i += 1
if i > 100:
s = True
else:
cv2.polylines(frame, np.int32([boxes[index]]), True,
(0, 255, 0), 2)
if s == False:
cv2.putText(
frame,
timestamp.strftime(
" Available: %d %m %Y %I:%M:%S"),
(10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)
i += 1
if i > 100:
s = True
msg = "Parking slots are available "
clr = 'green'
updateStatusWindow(msg, clr)
available_slots.append(slot_first_x_cord)
if not available_slots:
msg = "Parking slots are not available "
clr = 'red'
updateStatusWindow(msg, clr)
cv2.imshow("frame", frame)
key = cv2.waitKey(10) & 0xFF
if key == ord("q"):
break
if key == ord('p'):
hist = cv2.calcHist([a[1]], [0], None, [256], [0, 256])
plt.plot(hist)
plt.show()
i += 1
except KeyboardInterrupt:
pass
else:
box1 = np.array([(130.14516129032256, 226.74999999999994),
(85.68951612903224, 223.12096774193543),
(121.97983870967742, 175.0362903225806),
(155.5483870967742, 179.57258064516125)])
box2 = np.array([(137.4032258064516, 225.84274193548384),
(166.43548387096774, 173.22177419354836),
(210.89112903225805, 177.758064516129),
(196.375, 226.74999999999994)])
boxes = [box1, box2]
#print('afdasdf')
#print(box1[0].shape)
img_resize = image_utils.getRotateRect(gau, boxes)
feature = image_utils().extract_features(img_resize)
'''feature1 = feature.reshape(-1, 1)
score0 = SVM().predict(feature1)
score1 = SVM().predict(feature[1])
print (score0, score1)'''
timestamp = datetime.datetime.now()
score = SVM().predict(feature)
if score[0] == 0:
cv2.polylines(frame, np.int32([box1]), True, (0, 0, 255), 2)
saida = False
i = 0
else: