def main(base_model_name, weights_file, image_source, predictions_file, img_format='jpg'):
# load samples
if os.path.isfile(image_source):
image_dir, samples = image_file_to_json(image_source)
else:
image_dir = image_source
samples = image_dir_to_json(image_dir, img_type='jpg')
# build model and load weights
nima = Nima(base_model_name, weights=None)
nima.build()
nima.nima_model.load_weights(weights_file)
# initialize data generator
data_generator = TestDataGenerator(samples, image_dir, 64, 10, nima.preprocessing_function(),
img_format=img_format)
# get predictions
predictions = predict(nima.nima_model, data_generator)
# calc mean scores and add to samples
for i, sample in enumerate(samples):
sample['mean_score_prediction'] = calc_mean_score(predictions[i])
print(json.dumps(samples, indent=2))
if predictions_file is not None:
save_json(samples, predictions_file)
def predict(self, image_source):
img_format = "jpg"
if os.path.isfile(image_source):
image_dir, samples = image_file_to_json(image_source)
else:
image_dir = image_source
samples = image_dir_to_json(image_dir, img_type='jpg')
data_generator = TestDataGenerator(samples, image_dir, 64, 10, self.a_nima.preprocessing_function(),
img_format=img_format)
a_predictions = predict(self.a_nima.nima_model, data_generator)
for i, sample in enumerate(samples):
sample['a_mean_score_prediction'] = calc_mean_score(a_predictions[i])
data_generator = TestDataGenerator(samples, image_dir, 64, 10, self.t_nima.preprocessing_function(),
img_format=img_format)
t_predictions = predict(self.t_nima.nima_model, data_generator)
for i, sample in enumerate(samples):
sample['t_mean_score_prediction'] = calc_mean_score(t_predictions[i])
print(json.dumps(samples, indent=2))
return samples
def start_Button_clicked(self):
print('Start Button Clicked')
SelfDestructingBox.showWithTimeout(10, "Please wait while your images are analyzed.\nAnother message will appear when the process is complete.", "In progress")
QApplication.processEvents()
# build model and load weights
nima = Nima("MobileNet", weights=None)
nima.build()
nima.nima_model.load_weights("weights_mobilenet_aesthetic_0.07.hdf5")
totalSamples = []
j = 0
for (root, directories, files) in os.walk(self.selected_directory, topdown=True):
print("[INFO] ROOT: " + root)
self.samples = []
# Only grab the jpg images
img_paths = glob.glob(os.path.join(root, '*.jpg'))
for img_path in img_paths:
print("[INFO] IMG PATH IN ROOT: " + img_path)
totalSamples.append({'image_path': img_path})
self.importedFiles_id = os.path.basename(img_path).split('.')[0]
self.samples.append({'image_id': self.importedFiles_id})
# initialize data generator
data_generator = TestDataGenerator(self.samples, root, 64, 10, nima.preprocessing_function(), img_format='jpg')
# TODO: Circumvent multiprocessing error on Windows
predictions = nima.nima_model.predict_generator(data_generator, workers=1, use_multiprocessing=False, verbose=1)
# print(predictions)
# calc mean scores and add to samples
for i, sample in enumerate(self.samples):
sample['mean_score_prediction'] = calc_mean_score(predictions[i])
totalSamples[j]['mean_score_prediction'] = calc_mean_score(predictions[i])
j += 1
print("[INFO] Directory specific samples:")
print(json.dumps(self.samples, indent=2))
# sort totalSamples by score
print("[INFO] Sorted (descending) Total samples:")
totalSamples.sort(key=lambda i: i['mean_score_prediction'], reverse=True)
print(json.dumps(totalSamples, indent=2))
modelPath = os.path.sep.join(["personalModel", "model.cpickle"])
bUsePM = False
paths_with_scores = []
if os.path.exists(modelPath):
print("[INFO] MODEL PATH EXISTS: {}".format(modelPath))
bUsePM = True
# Initialize model to use its feature extraction method
personal_model = PersonalModel(modelPath)
# Grab top 50% of General Model's output
top_half = totalSamples[:len(totalSamples)//2]
feat_vecs = []
img_paths = []
for datum in top_half:
feat_vecs.append(personal_model.getFeatureVector(datum['image_path']))
img_paths.append(datum['image_path'])
feat_vecs_np = np.array(feat_vecs)
feat_vecs_np = np.squeeze(feat_vecs_np)
predictions = personal_model.model.predict(feat_vecs_np)
print("[INFO] Number of samples for PM: {}".format(len(top_half)))
print("[INFO] Predictions: {}".format(predictions))
paths_with_scores = list(zip(img_paths, predictions))
# sort (descending) by the second tuple value (the score)
paths_with_scores.sort(key=lambda x: x[1], reverse=True)
print("[INFO] ***paths with scores***")
for tup in paths_with_scores:
print("[INFO] {}, {}".format(tup[0], tup[1]))
# Creates new directory name. Checks if previous directory exists.
self.new_dir_title = self.create_directory_name()
# create new directory to hold top rated pics
self.new_dir = os.path.sep.join([self.selected_directory, self.new_dir_title])
if os.path.exists(self.new_dir):
shutil.rmtree(self.new_dir)
os.mkdir(self.new_dir)
# determine number of pics to copy into new dir
# 20% for now
num_to_pick = int(len(totalSamples) * 0.2)
# so before copying pics into new dir, see if we need to use the PM
# if so, pass 40% of PM's picks into
if bUsePM:
print("[INFO] PM Used")
for i, sample in enumerate(paths_with_scores):
if i == num_to_pick:
break
shutil.copy(sample[0], self.new_dir)
else:
# since totalSamples is now sorted with the highest rated
# pics first, the loop will only copy the first 'num_to_pick' images
print("[INFO] GENERAL used")
for i, sample in enumerate(totalSamples):
if i == num_to_pick:
break
shutil.copy(sample['image_path'], self.new_dir)
self.done_msg = QtGui.QMessageBox()
self.done_msg.setWindowTitle("Processing Complete!")
self.done_msg.setText("The best photos were copied into a new directory.\nYou can find this new directory inside of the directory you initially chose.")
self.done_msg.setIcon(QMessageBox.Information)
self.done_msg.exec_()
print("[INFO] Done copying into new dir.")