def decode_predictions(preds, top=5):
"""Decodes the prediction result from the model.
Arguments
preds: Numpy tensor encoding a batch of predictions.
top: Integer, how many top-guesses to return.
Returns
A list of lists of top class prediction tuples
`(class_name, class_description, score)`.
One list of tuples per sample in batch input.
Raises
ValueError: In case of invalid shape of the `preds` array (must be 2D).
"""
return imagenet_utils.decode_predictions(preds, top=top)
def upload():
if request.method == 'POST':
# Get the file from post request
f = request.files['image']
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(basepath, 'uploads',
secure_filename(f.filename))
f.save(file_path)
# Make prediction
preds = model_predict(file_path, model)
# Process your result for human
# pred_class = preds.argmax(axis=-1) # Simple argmax
pred_class = decode_predictions(preds, top=1) # ImageNet Decode
result = str(pred_class[0][0][1]) # Convert to string
return result
return None
def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
def decode_predictions(preds, top=5):
"""Decodes the prediction result from the VGG16 model."""
return imagenet_utils.decode_predictions(preds, top=top)
# define the model
model = vgg16.VGG16(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000)
layers_names = [
layer.name for layer in model.layers
if (layer.__class__.__name__ == 'Conv2D')
]
prediction = model.predict(pImg)
print(imagenet_utils.decode_predictions(prediction, top=3)[0])
for layer_name in layers_names:
layer_output = model.get_layer(layer_name).output
model_name = model.name
if K.image_data_format() == 'channels_first':
n_features = layer_output.shape.as_list()[1]
else:
n_features = layer_output.shape.as_list()[-1]
print("%s filter n_features %d" % (layer_name, n_features))
heat_map = generate_heat_map(model, pImg, layer_name, n_features)
plt.matshow(heat_map)
plt.title(layer_name)
#
# model = mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True,
# weights='imagenet', input_tensor=None, pooling=None, classes=1000)
#
# model = mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=True,
# weights='imagenet', input_tensor=None, pooling=None, classes=1000)
#
# model = nasnet.NASNetMobile(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None,
# classes=1000)
model = vgg16.VGG16(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000)
model.summary()
# record spend time
begin_time = time.clock()
prediction = model.predict(pImg)
end_time = time.clock()
print('Spend: %f ms' % (end_time - begin_time))
# obtain the top-3 predictions
results = imagenet_utils.decode_predictions(prediction, top=3)
print(results)
assert "Egyptian_cat" in [item[1] for item in results[0]]
image = load_img(args["image"], target_size=inputShape)
image = img_to_array(image)
# our input image is now represented as a NumPy array of shape
# (inputShape[0], inputShape[1], 3) however we need to expand the
# dimension by making the shape (1, inputShape[0], inputShape[1], 3)
# so we can pass it through the network
image = np.expand_dims(image, axis=0)
# pre-process the image using the appropriate function based on the
# model that has been loaded (i.e., mean subtraction, scaling, etc.)
image = preprocess(image)
# classify the image
print("[INFO] classifying image with ’{}’...".format(args["model"]))
preds = model.predict(image)
P = imagenet_utils.decode_predictions(preds)
# loop over the predictions and display the rank-5 predictions +
# probabilities to our terminal
for (i, (imagenetID, label, prob)) in enumerate(P[0]):
print(f"{i+1}. {label}: {prob*100:.2f}%")
# load the image via OpenCV, draw the top prediction on the image,
# and display the image to our screen
orig = cv2.imread(args["image"])
(imagenetID, label, prob) = P[0][0]
cv2.putText(orig, f"Label: {label}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 255, 0), 2)
cv2.imshow("Classification", orig)
cv2.waitKey(0)
