def classify(img_in, img_out):
images = list()
# load the VGG16 network
print("[INFO] loading network...")
model = VGG16(weights="imagenet")
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
for item in os.listdir(img_in):
if not item.startswith('.') and os.path.isfile(
os.path.join(img_in, item)):
print(item)
img = cv2.imread(os.path.join(img_in, item))
if img is not None:
images.append(img)
print("[INFO] loading and preprocessing image...")
image = cv2.resize(img, (224, 224))
image = image_utils.img_to_array(image)
# our image is now represented by a NumPy array of shape (3, 224, 224),
# but we need to expand the dimensions to be (1, 3, 224, 224) so we can
# pass it through the network -- we'll also preprocess the image by
# subtracting the mean RGB pixel intensity from the ImageNet dataset
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
# classify the image
print("[INFO] classifying image...")
preds = model.predict(image)
P = decode_predictions(preds)
(imagenetID, label, prob) = P[0][0]
#print('Predicted:', decode_predictions(preds,1))
print(decode_predictions(preds, 3))
# display the predictions to our screen
print("ImageNet ID: {}, Output label: {}, Prob: {}".format(
imagenetID, label, round(prob, 4)))
cv2.putText(img, "Output label: {}".format(label), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (102, 0, 255), 2)
cv2.putText(img, "Prob: {0:.0f}%".format(prob * 100), (10, 90),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
#create a new folder
foldoutcheck = os.path.exists(img_out)
if foldoutcheck == False:
os.makedirs(img_out)
foldcheck = os.path.exists(img_out + "/" + label)
if foldcheck == True:
cv2.imwrite(img_out + "/" + label + "/" + item, img)
else:
os.makedirs(img_out + "/" + label)
cv2.imwrite(img_out + "/" + label + "/" + item, img)
def classify_val(model, syn_data, idx):
img, val = get_val_img(idx, size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# val_truth = det_synsets[label]
clsloc_preds = decode_predictions(preds)
det_preds = syn_data.decode_predictions_det(clsloc_preds)
# print('Validation label: {},{},{}'.format(val_truth['id'],val_truth['wnid'],val_truth['name']))
print('Predicted clsloc:', clsloc_preds)
print('Predicted det:', det_preds)
return preds, det_preds, clsloc_preds
from models.imagenet_utils import preprocess_input ,decode_predictions
model=VGG16(include_top=True,weights='imagenet')
img=image.load_img("elephent.jpeg",target_size=(224,224))
x=image.img_to_array(img)
print(type(x))
x = np.expand_dims(x,axis=0)
x=preprocess_input(x)
predict=model.predict(x)
print("prediction",decode_predictions(predict))
model.summary()
model.layers[-1].get_config()
//////////////////////////////////////////////
model=VGG16(weights='imagenet',include_top=False)
model.summary()
model.layers[-1].get_config()
if include_top:
weights_path = get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
TF_WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
TF_WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
if K.backend() == 'theano':
convert_all_kernels_in_model(model)
return model
if __name__ == '__main__':
model = ResNet50(include_top=True, weights='imagenet')
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
print('Input image shape:', x.shape)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds))