x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
model.load_weights('weights/nasnet_weights.h5')
score_list = np.empty((len(imgs), 11), dtype=object)
step = 20000
i = 0
while i < len(imgs):
print(i)
imgs_temp = imgs[i:i + step]
img_array = np.empty((len(imgs_temp), 224, 224, 3))
file_names = []
for ind, j in tqdm.tqdm(enumerate(imgs_temp)):
x = preprocess_input(
np.expand_dims(img_to_array(
load_img(j, target_size=target_size)),
axis=0))
img_array[ind, ] = x
file_names.append(Path(j).name.lower())
scores = model.predict(img_array, batch_size=100, verbose=0)
#si = np.arange(1, 11, 1)
#mean = np.sum(scores * si, axis=1)
#std = np.sqrt(np.sum(((np.array([si, ] * len(imgs_temp)) - mean.reshape(len(imgs_temp), -1)) ** 2) * scores, axis=1))
#result = np.stack([file_names, mean, std]).transpose()
#result = np.stack([file_names, scores]).transpose()
#score_list[i:i + step, ] = result
pooling='avg',
weights=None)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
model.load_weights('weights/nasnet_weights.h5')
score_list = []
for img_path in imgs:
img = load_img(img_path, target_size=target_size)
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
scores = model.predict(x, batch_size=1, verbose=0)[0]
mean = mean_score(scores)
std = std_score(scores)
file_name = Path(img_path).name.lower()
score_list.append((file_name, mean))
print("Evaluating : ", img_path)
print("NIMA Score : %0.3f +- (%0.3f)" % (mean, std))
print()
if rank_images:
print("*" * 40, "Ranking Images", "*" * 40)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
model.load_weights('weights/nasnet_weights.h5')
score_list = np.empty((len(imgs), 11), dtype=object)
step = 20000
i = 0
while i < len(imgs):
print(i)
imgs_temp = imgs[i:i + step]
img_array = np.empty((len(imgs_temp), 224, 224, 3))
file_names = []
for ind, j in tqdm.tqdm(enumerate(imgs_temp)):
x = preprocess_input(np.expand_dims(img_to_array(load_img(j, target_size=target_size)), axis=0))
img_array[ind, ] = x
file_names.append(Path(j).name.lower())
scores = model.predict(img_array, batch_size=100, verbose=0)
#si = np.arange(1, 11, 1)
#mean = np.sum(scores * si, axis=1)
#std = np.sqrt(np.sum(((np.array([si, ] * len(imgs_temp)) - mean.reshape(len(imgs_temp), -1)) ** 2) * scores, axis=1))
#result = np.stack([file_names, mean, std]).transpose()
#result = np.stack([file_names, scores]).transpose()
#score_list[i:i + step, ] = result
score_list[i:i + step, 0] = file_names
pooling='avg',
weights=None)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
model.load_weights('weights/nasnet_weights.h5')
score_list = []
for img_path in imgs:
img = load_img(img_path, target_size=target_size)
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x,
data_format=tf.keras.backend.image_data_format())
scores = model.predict(x, batch_size=1, verbose=0)[0]
mean = mean_score(scores)
std = std_score(scores)
file_name = Path(img_path).name.lower()
score_list.append((file_name, mean))
print("Evaluating : ", img_path)
print("NIMA Score : %0.3f +- (%0.3f)" % (mean, std))
print()
if rank_images:
print("*" * 40, "Ranking Images", "*" * 40)