def sliding_window(image, model, overlapping_percentage, size_of_images):
c1, c2 = 0, 0
a, b = image.size
number_of_steps = int(b / (size_of_images * (1 - overlapping_percentage)))
x, y = int(size_of_images / 2) + 1, int(size_of_images / 2) + 1
L = []
for i in range(0, number_of_steps - 1):
y = int(size_of_images / 2) + 1
while x < b - size_of_images / 2 and y < a - size_of_images / 2:
c1 += 1
cropped_image = image.crop(
(y - size_of_images / 2, x - size_of_images / 2,
y + size_of_images / 2, x + size_of_images / 2))
crop = data_transforms(cropped_image)
crop = crop.unsqueeze(0)
prob = model(crop)
probability = round(sigmoid(float(prob[0][1])), 1)
prediction = prob.data.numpy().argmax()
# print(y)
if prediction == 1:
# print(c2)
# print(c1)
L.append([cropped_image, x, y, probability])
c2 += 1
y += int(size_of_images * (1 - overlapping_percentage))
x += int(size_of_images * (1 - overlapping_percentage))
return (L)
print('Using CPU')
from data import data_transforms
test_dir = args.data + '/test_images/mistery_category'
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
output_file = open(args.outfile, "w")
output_file.write("Id,Category\n")
for f in tqdm(os.listdir(test_dir)):
if 'jpg' in f:
data = data_transforms(pil_loader(test_dir + '/' + f))
data = data.view(1, data.size(0), data.size(1), data.size(2))
if use_cuda:
data = data.cuda()
output = model(data)
pred = output.data.max(1, keepdim=True)[1]
output_file.write("%s,%d\n" % (f[:-4], pred))
output_file.close()
print("Succesfully wrote " + args.outfile +
', you can upload this file to the kaggle competition website')
state_dict = torch.load(args.model)
model = Net()
model.load_state_dict(state_dict)
model.eval()
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
for f in tqdm(os.listdir(test_dir)):
if 'ppm' in f:
data = data_transforms(pil_loader('/' + f))
data = data.view(1, data.size(0), data.size(1), data.size(2))
data = Variable(data, volatile=True)
output = model(data)
pred = output.data.max(1, keepdim=True)[1]
file_id = f[0:5]
output_file.write("%s,%d\n" % (file_id, pred))
if (pred == 0):
classification = 'Non - Recyclable'
else:
classification = 'Recyclable'
print(classification)
if __name__ == '__main__':
