def generate_batch_data(vocPath, imageNameFile, batch_size, sample_number):
"""
Args:
vocPath: the path of pascal voc data
imageNameFile: the path of the file of image names
batchsize: batch size, sample_number should be divided by batchsize
Funcs:
A data generator generates training batch indefinitely
"""
class_num = 20
#Read all the data once and dispatch them out as batches to save time
TotalimageList = prepareBatch(0, sample_number, imageNameFile, vocPath)
while 1:
batches = sample_number // batch_size
for i in range(batches):
images = []
boxes = []
sample_index = np.random.choice(sample_number,
batch_size,
replace=True)
#sample_index = [3]
for ind in sample_index:
image = TotalimageList[ind]
#print image.imgPath
image_array = crop_detection(image.imgPath,
new_width=448,
new_height=448)
#image_array = np.expand_dims(image_array,axis=0)
images.append(image_array)
boxes.append(np.reshape(image.boxes, -1))
#return np.asarray(images),np.asarray(boxes)
yield np.asarray(images), np.asarray(boxes)
def generate_batch_data(vocPath, imageNameFile, batch_size, sample_number):
"""
Args:
vocPath: the path of pascal voc data
imageNameFile: the path of the file of image names
batchsize: batch size, sample_number should be divided by batchsize
Funcs:
A data generator generates training batch indefinitely
"""
class_num = 20
#Read all the data once and dispatch them out as batches to save time
TotalimageList = prepareBatch(0, sample_number, imageNameFile, vocPath)
while 1:
batches = sample_number // batch_size
for i in range(batches):
images = []
boxes = []
sample_index = np.random.choice(sample_number,
batch_size,
replace=True)
#sample_index = [3]
for ind in sample_index:
image = TotalimageList[ind]
#print image.imgPath
image_array = crop_detection(image.imgPath,
new_width=448,
new_height=448)
#image_array = np.expand_dims(image_array,axis=0)
y = []
for i in range(7):
for j in range(7):
box = image.boxes[i][j]
'''
############################################################
#x,y,h,w,one_hot class label vector[0....0],objectness{0,1}#
############################################################
'''
if (box.has_obj):
obj = box.objs[0]
y.append(obj.x)
y.append(obj.y)
y.append(obj.h)
y.append(obj.w)
labels = [0] * 20
labels[obj.class_num] = 1
y.extend(labels)
y.append(1) #objectness
else:
y.extend([0] * 25)
y = np.asarray(y)
#y = np.reshape(y,[1,y.shape[0]])
images.append(image_array)
boxes.append(y)
#return np.asarray(images),np.asarray(boxes)
yield np.asarray(images), np.asarray(boxes)
def generate_batch_data(vocPath,imageNameFile,batch_size,sample_number):
"""
Args:
vocPath: the path of pascal voc data
imageNameFile: the path of the file of image names
batchsize: batch size, sample_number should be divided by batchsize
Funcs:
A data generator generates training batch indefinitely
"""
class_num = 20
#Read all the data once and dispatch them out as batches to save time
TotalimageList = prepareBatch(0,sample_number,imageNameFile,vocPath)
while 1:
batches = sample_number // batch_size
for i in range(batches):
images = []
boxes = []
sample_index = np.random.choice(sample_number,batch_size,replace=True)
#sample_index = [3]
for ind in sample_index:
image = TotalimageList[ind]
#print image.imgPath
image_array = crop_detection(image.imgPath,new_width=448,new_height=448)
#image_array = np.expand_dims(image_array,axis=0)
images.append(image_array)
boxes.append(np.reshape(image.boxes,-1))
#return np.asarray(images),np.asarray(boxes)
yield np.asarray(images),np.asarray(boxes)
def generate_batch_data(vocPath,imageNameFile,batch_size,sample_number):
"""
Args:
vocPath: the path of pascal voc data
imageNameFile: the path of the file of image names
batchsize: batch size, sample_number should be divided by batchsize
Funcs:
A data generator generates training batch indefinitely
"""
class_num = 20
#Read all the data once and dispatch them out as batches to save time
TotalimageList = prepareBatch(0,sample_number,imageNameFile,vocPath)
while 1:
batches = sample_number // batch_size
for i in range(batches):
images = []
boxes = []
sample_index = np.random.choice(sample_number,batch_size,replace=True)
#sample_index = [3]
for ind in sample_index:
image = TotalimageList[ind]
#print image.imgPath
image_array = crop_detection(image.imgPath,new_width=448,new_height=448)
#image_array = np.expand_dims(image_array,axis=0)
y = []
for i in range(7):
for j in range(7):
box = image.boxes[i][j]
'''
############################################################
#x,y,h,w,one_hot class label vector[0....0],objectness{0,1}#
############################################################
'''
if(box.has_obj):
obj = box.objs[0]
y.append(obj.x)
y.append(obj.y)
y.append(obj.h)
y.append(obj.w)
labels = [0]*20
labels[obj.class_num] = 1
y.extend(labels)
y.append(1) #objectness
else:
y.extend([0]*25)
y = np.asarray(y)
#y = np.reshape(y,[1,y.shape[0]])
images.append(image_array)
boxes.append(y)
#return np.asarray(images),np.asarray(boxes)
yield np.asarray(images),np.asarray(boxes)
image_array = np.rollaxis(image_array, 2, 0)
image_array = np.rollaxis(image_array, 2, 0)
print image_array.shape
scipy.misc.imsave('recovered.jpg', image_array)
# center should be in (3,3)
labels = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
out = y[0]
imgPath = os.path.join(os.getcwd(), 'recovered.jpg')
img = Image.open(imgPath)
image_arr, img_resize = crop_detection(imgPath, 448, 448, save=True)
drawable = ImageDraw.Draw(img_resize)
#Draw orignal bounding boxes
count = 0
for i in range(49):
preds = out[i * 25:(i + 1) * 25]
if (preds[24] > 0.3):
count = count + 1
#print preds[0:4],preds[24]
row = i / 7
col = i % 7
print row, col
centerx = 64 * col + 64 * preds[0]
centery = 64 * row + 64 * preds[1]
image_array = image_array[0,...,...,...]
#scipy.misc.imsave('recovered.jpg', image_array)
print image_array.shape
image_array = (image_array + 1.0) / 2.0 * 225.0
image_array = np.rollaxis(image_array,2,0)
image_array = np.rollaxis(image_array,2,0)
print image_array.shape
scipy.misc.imsave('recovered.jpg', image_array)
# center should be in (3,3)
labels = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train","tvmonitor"]
out = y[0]
imgPath = os.path.join(os.getcwd(),'recovered.jpg')
img = Image.open(imgPath)
image_arr,img_resize = crop_detection(imgPath,448,448,save=True)
drawable = ImageDraw.Draw(img_resize)
#Draw orignal bounding boxes
count = 0
for i in range(49):
preds = out[i*25:(i+1)*25]
if(preds[24] > 0.3):
count = count + 1
#print preds[0:4],preds[24]
row = i/7
col = i%7
print row,col
centerx = 64 * col + 64 * preds[0]
centery = 64 * row + 64 * preds[1]
