def get_image_data():
with open("./word.txt", "r") as f:
words = f.read().split(",")
words = [word.strip("\n") for word in words]
images = []
labels = []
for index, word in enumerate(words):
dir_path = "./images/{}".format(word)
image_paths = os.listdir(dir_path)
bar = pyprind.ProgBar(len(image_paths),
track_time=True,
title="Image {}".format(word))
for j, image_path in enumerate(image_paths):
bar.update()
if "jpg" not in image_path:
continue
image = np.asarray(Image.open(dir_path + "/" + image_path))
image_size = get_image_size()
if image.shape != image_size:
continue
images.append(image)
labels.append(index)
images = np.asarray(images)
labels = np.asarray(labels)
return images, labels
def make_ensemble_predict_results(data_dir, target, batch_size, imglst,
labels_txt, results_log, top_k, gpus):
with open(imglst) as lst:
test_list = [(l.split('\t')[1].strip(), l.split('\t')[2].strip())
for l in lst.readlines()]
with open(labels_txt) as syn:
labels = [l.split(' ')[-1].strip() for l in syn.readlines()]
model_size_array = []
imgrec_array = []
for model_prefix in model_prefix_array:
size = functions.get_image_size(model_prefix)
model_size_array.append(size)
imgrec_array.append("%s/images-%s-%d.rec" % (data_dir, target, size))
print(model_size_array)
print(imgrec_array)
with open(results_log, 'w') as result:
result.write("model_prefix: %s\n" % ','.join(model_prefix_array))
result.write("model_epoch: %s\n" %
','.join([str(epoch) for epoch in model_epoch_array]))
result.write("data: %s\n" % ','.join(imgrec_array))
pred_arrays = []
for model_prefix, model_epoch, model_size in zip(
model_prefix_array, model_epoch_array, model_size_array):
data_shape = (3, model_size, model_size)
imgrec = "%s/images-%s-%d.rec" % (data_dir, target, model_size)
mod = load_model(model_prefix, model_epoch, batch_size, model_size,
gpus)
test_rec = load_image_record(imgrec, batch_size, data_shape)
pred_array = []
for preds, i_batch, batch in mod.iter_predict(test_rec,
reset=False):
for batch_index, (pred, label) in enumerate(
zip(preds[0].asnumpy(), batch.label[0].asnumpy())):
pred_array.append(pred)
pred_arrays.append(pred_array)
del mod
del test_rec
if weights:
w = np.array(weights)
w = w / np.sum(w)
try:
preds = np.average(pred_arrays, axis=0, weights=w)
except ValueError:
print(
'Length of weights not compatible with number of models.')
sys.exit(1)
else:
preds = np.average(pred_arrays, axis=0)
for i in range(len(preds)):
sorted_pred = heapq.nlargest(top_k,
enumerate(preds[i]),
key=lambda x: x[1])
results = []
for sorted_index, value in sorted_pred:
results.append("%s %s" % (sorted_index, value))
result.write("%s %s %s\n" %
(test_list[i][1], int(float(
test_list[i][0])), ' '.join(results)))
preds = np.average(pred_arrays, axis=0)
for i in range(len(preds)):
sorted_pred = heapq.nlargest(top_k,
enumerate(preds[i]),
key=lambda x: x[1])
results = []
for sorted_index, value in sorted_pred:
results.append("%s %s" % (sorted_index, value))
result.write("%s %s %s\n" %
(test_list[i][1], int(float(
test_list[i][0])), ' '.join(results)))
model_prefix_array = [re.sub(r'-\d+$', '', model) for model in models]
print(model_prefix_array)
model_epoch_array = [
int(re.sub(r'^[\w\-\.]+-', '', model)) for model in models
]
print(model_epoch_array)
labels_txt = "model/%s-labels.txt" % model_prefix_array[
0] # use first labels.txt in models
size = functions.get_image_size(model_prefix_array[0])
imglst = "%s/images-%s-%d.lst" % (data_dir, target, size)
results_log = "logs/%s-%s-results.txt" % (model_prefix, target)
make_ensemble_predict_results(data_dir, target, batch_size, imglst, labels_txt,
results_log, top_k, gpus)
print("Saved predict results to \"%s\"" % results_log)
from PIL import Image
import os
import numpy as np
import pyprind
from functions import get_image_size
with open("./word.txt", "r") as f:
words = f.read().split(",")
words = [word.strip("\n") for word in words]
for index, word in enumerate(words):
dir_path = "./Images/{}".format(word)
image_names = os.listdir(dir_path)
bar = pyprind.ProgBar(len(image_names),
track_time=True,
title="Image {}".format(word))
for j, image_name in enumerate(image_names):
bar.update()
if "jpg" not in image_name:
continue
image_path = dir_path + "/" + image_name
image = Image.open(image_path)
image_size = get_image_size(reversed=True)
resized_size = [image_size[0], image_size[1]]
resized_image = image.resize(resized_size, Image.ANTIALIAS)
resized_image.save("{}/{}.jpg".format(dir_path, j))
os.remove(image_path)