def main():
input_file = "image.jpg"
output_file = "labels.png"
# Download the model from https://goo.gl/ciEYZi
saved_model_path = "/data/download/crfrnn_keras_model.h5"
model = get_crfrnn_model_def()
model.load_weights(saved_model_path)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
probs = model.predict(img_data, verbose=False)[0, :, :, :]
segmentation = util.get_label_image(probs, img_h, img_w)
segmentation.save(output_file)
def main():
input_file = 'image.jpg'
output_file = 'labels.png'
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def()
model.load_weights(saved_model_path)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
probs = model.predict(img_data, verbose=False)[0, :, :, :]
segmentation = util.get_label_image(probs, img_h, img_w)
segmentation.save(output_file)
def main():
input_file = 'image.jpg'
output_file = 'labels.png'
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def()
#model.load_weights(saved_model_path)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
tic = time.clock()
probs = model.predict(img_data, verbose=False)[0, :, :, :]
toc = time.clock()
segmentation = util.get_label_image(probs, img_h, img_w)
segmentation.save(output_file)
print("Time taken: " + str(toc - tic))
def main():
input_file = 'image.jpg'
output_file = 'labels.png'
segment_file = 'segment.jpg'
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def(num_segs=1)
model.load_weights(saved_model_path)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
seg_data, seg_h, seg_w = util.get_preprocessed_image(segment_file)
# img_data = img_data.reshape([1, 500, 500, 3])
# seg_data = seg_data.reshape([1, 500, 500, 3])
probs = model.predict([img_data, seg_data], verbose=False)[0, :, :, :]
segmentation = util.get_label_image(probs, img_h, img_w)
segmentation.save(output_file)
def main():
input_file = 'hiking.jpg'
mask_file = 'labels.png'
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def()
model.load_weights(saved_model_path)
img_data, img_h, img_w, size = util.get_preprocessed_image(input_file)
probs = model.predict(img_data, verbose=False)[0]
segmentation = util.get_label_image(probs, img_h, img_w, size)
segmentation.save(mask_file)
input_content = cv2.imread(input_file)
mask_content = cv2.imread(mask_file)
input_content[mask_content == 0] = 255
print(input_content.shape)
cv2.imwrite('output.jpg', input_content)
def main():
input_file = inp_f
tmp = path_leaf(inp_f)
print(tmp)
output_file = '../saliency_maps/' + tmp
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def()
model.load_weights(saved_model_path)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
probs = model.predict(img_data, verbose=False)[0, :, :, :]
segmentation = util.get_label_image(probs, img_h, img_w)
a = np.array(segmentation)
print(np.shape(a))
for i in range(a.shape[0]):
for j in range(a.shape[1]):
if (a[i][j] > 0):
a[i][j] = 255
segmentation = PIL.Image.fromarray(a)
segmentation.save(output_file)
saved_model_path = "SegmentationModel_Weights.h5"
nTest = 128
font = ImageFont.truetype("/usr/share/fonts/TTF/Anonymous Pro.ttf",
8,
encoding="unic")
label_seg_gt = u"Segmentation (Ground Truth)"
label_im = u"Image"
label_seg = u"Computed Segmentation"
text_width_gt, text_height_gt = font.getsize(label_seg_gt)
text_width_im, text_height_im = font.getsize(label_im)
text_width_seg, text_height_seg = font.getsize(label_seg)
model = get_crfrnn_model_def()
model.load_weights(saved_model_path, by_name=True)
for i in range(nTest):
seg_gt, im, im_input = MakeInputData(n=np.random.randint(low=1, high=5))
probs = model.predict(im_input, verbose=False)[0, :, :, :]
labels = probs.argmax(axis=2)
seg_gt = Image.fromarray((seg_gt * 255).astype("uint8"))
im = Image.fromarray((im * 255).astype("uint8"))
labels = Image.fromarray((labels * 255).astype("uint8"))
labels = labels.resize((GenImSize, GenImSize), Image.NEAREST)
seg_gt = image_tint(seg_gt, tint='#8AFAAB')
im = image_tint(im, tint='#8AA0FA')
labels = image_tint(labels, tint='#F98A8A')
def main():
input_file = 'image.jpg'
output_file = 'labels.png'
# Download the model from https://goo.gl/ciEYZi
saved_model_path = 'crfrnn_keras_model.h5'
model = get_crfrnn_model_def()
from keras.optimizers import Adam
#model = build_model()
model.compile(optimizer=Adam(),
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=90,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2)
image_datagen = keras.preprocessing.image.ImageDataGenerator(
**data_gen_args)
mask_datagen = keras.preprocessing.image.ImageDataGenerator(
**data_gen_args)
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
#xtrain=/home/qwe/Downloads/crfrnn2/xtrain
#ytrain='/home/qwe/Downloads/crfrnn2/ytrain'
#grayx=cv2.cvtColor(xtrain,cv2.COLOR_BGR2GRAY)
#pathx='/home/qwe/Downloads/crfrnn2/xtrain/*.*'
#pathy='/home/qwe/Downloads/crfrnn2/ytrain'
#c2='/home/qwe/Downloads/crfrnn2/ytrain'
#c1='/home/qwe/Downloads/crfrnn2/xtrain'
#xtrain= get_image_files(c2)
#ytrain=get_image_files(c1)
#image_datagen.fit(xtrain)
#mask_datagen.fit(ytain, augment=False, seed=seed)
# Provide the same seed and keyword arguments to the fit and flow methods
image_generator = image_datagen.flow_from_directory('xtrain',
target_size=(500, 500),
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory('ytrain',
target_size=(500, 500),
class_mode=None,
seed=seed)
# combine generators into one which yields image and masks
train_generator = zip(image_generator, mask_generator)
#print(type(train_generator),'akash')
model.fit_generator(train_generator, steps_per_epoch=2000, epochs=50)
img_data, img_h, img_w = util.get_preprocessed_image(input_file)
probs = model.predict(img_data, verbose=False)[0, :, :, :]
segmentation = util.get_label_image(probs, img_h, img_w)
segmentation.save(output_file)