I am getting an UnboundLocalError that says 'local variable 'image' referenced before assignment' . I saw another solution that said to make the variable global. but the error stills keeps popping..Any help would be appreciated.
import numpy as np
import os
from keras.models import load_model
import matplotlib.pyplot as plt
from skimage import io, color , util
from skimage.transform import resize
import pandas as pd
st.set_option('deprecation.showPyplotGlobalUse', False)
st.write("""
# Image Restoration
""")
image_process = ['Colourization','Denoise','SuperResolution']
#model_name = ['CNN','RCNN','AECNN']
img_file = st.sidebar.file_uploader('Upload An Image')
sel_process = st.sidebar.selectbox("Select Restoration Process",image_process)
# sel_model = st.sidebar.selectbox("Select a model to be used",model_name)
if(sel_process == 'Colourization') :
st.write(""" ### Colourization : """)
img_size = 128
#get input and output tensor for image:
def ret_input_output_tensor(name,img):
input_tensor = np.empty((1, img_size, img_size, 1))
output_tensor = np.empty((1, img_size, img_size, 2))
if img_file is not None :
image = io.imread(img_file)
image = resize(image, (img_size, img_size, 3), anti_aliasing=False, mode='constant')
image = color.rgb2lab(image)
#//////////////ERROR part//////////////////////
if image.shape == (img_size, img_size, 3): # if not a BW image
# array image for output tensor
output_tensor[0, :] = (image[:, :, 1:] / 128)
# array values for input tensor
input_tensor[0, :] = (image[:, :, 0] / 100).reshape(img_size, img_size, 1)
return input_tensor, output_tensor
#input representation
input_tensor, output_tensor = ret_input_output_tensor(sel_process,img_file)
Related
I am working on a tensor model.
I have trained it and now testing and evaluating it.
Img_score with the prediction is working correctly.
I want also to print the seg_mask image (seg_out in the code), but plt.imshow does not work, even if I permute it.
Moreover if I print the values of the seg_out image they are all negatives which I don't understand how to handle them in images.
Here is what I am using.
INPUT_WIDTH = 232
INPUT_HEIGHT = 640
INPUT_CHANNELS = 3
device = "cpu"
model = SegDecNet(device, INPUT_WIDTH, INPUT_HEIGHT, INPUT_CHANNELS)
model.set_gradient_multipliers(0)
model_path = "/final_state_dict.pth"
model.load_state_dict(torch.load(model_path, map_location=device))
# %%
img_path = '/20118.png'
img = cv2.imread(img_path) if INPUT_CHANNELS == 3 else cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (INPUT_WIDTH, INPUT_HEIGHT))
print(img.shape)
img = np.transpose(img, (2, 0, 1)) if INPUT_CHANNELS == 3 else img[np.newaxis]
img_t = torch.from_numpy(img)[np.newaxis].float() / 445400 # must be [BATCH_SIZE x CHANNELS x HEIGHT x WIDTH]
dec_out, seg_out = model(img_t)
img_score = torch.sigmoid(dec_out)
print(img_score)
print(seg_out[0][0].shape)
print(seg_out[0][0].permute(0, 1))
#plt.imshow(seg_out[0].permute(2, 0, 1))
thanks
While trying to import VGG19 model, the code below generates an error of non tensor inputs. Although I am following another this code snippet here.
Code:
from keras.applications.vgg19 import VGG19
import keras.backend as K
from keras.models import Model
import imageio as iio
image_shape = (384,384,3)
vgg19 = VGG19(include_top=False, weights='imagenet', input_shape=image_shape)
vgg19.trainable = False
# Make trainable as False
for l in vgg19.layers:
l.trainable = False
model = Model(inputs=vgg19.input, outputs=vgg19.get_layer('block5_conv4').output)
model.trainable = False
img1 = iio.imread('img1.jpg')
img2 = iio.imread('img2.jpg')
mean = K.mean(K.square(model(img1) - model(img2)))
Error:
...,
[164, 90, 0, 255],
[164, 90, 0, 255],
[164, 90, 0, 255]]]], dtype=uint8)]. All inputs to the layer should be tensors.
unable to figure out why.
Maybe try converting your images to tensors:
import numpy
from PIL import Image
from keras.applications.vgg19 import VGG19
import keras.backend as K
from keras.models import Model
import imageio as iio
# Create random images
for n in range(2):
a = numpy.random.rand(384,384,3) * 255
im = Image.fromarray(a.astype('uint8')).convert('RGB')
im.save('test%0d.jpg' % n)
image_shape = (384,384,3)
vgg19 = VGG19(include_top=False, weights='imagenet', input_shape=image_shape)
vgg19.trainable = False
# Make trainable as False
for l in vgg19.layers:
l.trainable = False
model = Model(inputs=vgg19.input, outputs=vgg19.get_layer('block5_conv4').output)
model.trainable = False
img1 = iio.imread('test0.jpg')
img2 = iio.imread('test1.jpg')
img1 = tf.expand_dims(tf.constant(img1), axis=0)
img2 = tf.expand_dims(tf.constant(img2), axis=0)
mean = K.mean(K.square(model(img1) - model(img2)))
print(mean)
tf.Tensor(5.283036, shape=(), dtype=float32)
Instead of tf.expand_dims, you could also just do this:
img1 = tf.constant([img1])
img2 = tf.constant([img2])
There is also an option to load your images with tf.keras.preprocessing.image.load_img:
img1 = tf.keras.preprocessing.image.load_img('test0.jpg')
img2 = tf.keras.preprocessing.image.load_img('test1.jpg')
img1 = tf.constant([tf.keras.preprocessing.image.img_to_array(img1)])
img2 = tf.constant([tf.keras.preprocessing.image.img_to_array(img2)])
mean = K.mean(K.square(model(img1) - model(img2)))
print(mean)
the code from this page works fine.
i changed the code little bit.
image_shape = (384,384,3)
base_model = VGG19(include_top=False, weights='imagenet', input_shape=image_shape)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block5_conv4').output)
img01 = iio.imread('test0.jpg').astype('float32')
img11 = iio.imread('test1.jpg').astype('float32')
imgx1 = normalize(img01)
imgx2 = normalize(img11)
img1 = np.expand_dims(imgx1, axis=0)
img2 = np.expand_dims(imgx2, axis=0)
mean = np.mean((model.predict(img1) - model.predict(img2))**2)
print(mean)
i tried to create a python script for detect the object in a picture and return the similar images, but it return always an error:
TypeError: only integer scalar arrays can be converted to a scalar index
the ids : is the matrix returned after the detection; and it returned perfectly
but the problem is only in the last line: scores = [img_paths[id] for id in ids].
My code :
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
from PIL import Image
#from feature_extractor import FeatureExtractor
from datetime import datetime
from flask import Flask, request, render_template
from pathlib import Path
from keras.optimizers import Adam
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten
class FeatureExtractor:
def __init__(self):
input_shape = (224, 224, 3)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
for layer in base_model.layers:
layer.trainable = False
last = base_model.layers[-1].output
x = Flatten()(last)
x = Dense(1000, activation='relu', name='fc1')(x)
x = Dropout(0.3)(x)
x = Dense(10, activation='softmax', name='predictions')(x)
model = Model(base_model.input, x)
model.compile(optimizer=Adam(lr=0.001),
loss = 'categorical_crossentropy',metrics=['accuracy'])
self.model = Model(inputs=base_model.input, outputs=base_model.layers[-1].output)
def extract(self, img):
"""
Extract a deep feature from an input image
Args:
img: from PIL.Image.open(path) or tensorflow.keras.preprocessing.image.load_img(path)
Returns:
feature (np.ndarray): deep feature with the shape=(4096, )
"""
img = img.resize((224, 224)) # VGG must take a 224x224 img as an input
img = img.convert('RGB') # Make sure img is color
x = image.img_to_array(img) # To np.array. Height x Width x Channel. dtype=float32
x = np.expand_dims(x, axis=0) # (H, W, C)->(1, H, W, C), where the first elem is the number of img
x = preprocess_input(x) # Subtracting avg values for each pixel
feature = self.model.predict(x)[0] # (1, 4096) -> (4096, )
return feature / np.linalg.norm(feature) # Normalize
path = "/home/virtuag/www/storage/searchSCB.jpg"
img = Image.open(path)
app = Flask(__name__)
fe = FeatureExtractor()
features = []
img_paths = []
for feature_path in Path("/home/virtuag/www/storage/images_article").glob("*.npy"):
features.append(np.load(feature_path))
img_paths.append(Path("/home/virtuag/www/storage/images_article") / (feature_path.stem + ".jpg"))
features = np.array(features)
query = fe.extract(img)
dists = np.linalg.norm(features-query, axis=1)
ids = np.argsort(dists)[:30]
scores = [img_paths[id] for id in ids]
when i tryed to execute my python code, i get this error: (ValueError: No such layer: fc1) : error capture
i use in my code TensorFlow and Keras package to detect Object in image and return the similar images from custom Dataset.
it s work perfectly on local, but when i trayed in the server OVH there is always the error
(i trayed to change the layer to 'block5_pool' but it's not working with my code.)
my code :
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
from PIL import Image
from datetime import datetime
from flask import Flask, request, render_template
from pathlib import Path
class FeatureExtractor:
def __init__(self):
base_model = VGG16(weights='imagenet', include_top=False)
self.model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
def extract(self, img):
"""
Extract a deep feature from an input image
Args:
img: from PIL.Image.open(path) or tensorflow.keras.preprocessing.image.load_img(path)
Returns:
feature (np.ndarray): deep feature with the shape=(4096, )
"""
img = img.resize((224, 224))
img = img.convert('RGB')
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
feature = self.model.predict(x)[0]
return feature / np.linalg.norm(feature)
path = "/home/virtuag/www/storage/searchSCB.jpg"
img = Image.open(path)
app = Flask(__name__)
fe = FeatureExtractor()
features = []
img_paths = []
for feature_path in Path("/home/virtuag/www/storage/images_article").glob("*.npy"):
features.append(np.load(feature_path))
img_paths.append(Path("/home/virtuag/www/storage/images_article") / (feature_path.stem + ".jpg"))
features = np.array(features)
query = fe.extract(img)
dists = np.linalg.norm(features-query, axis=1) # L2 distances to features
ids = np.argsort(dists)[:30] # Top 30 results
scores = [img_paths[id] for id in ids]
print (scores)```
thank you
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
from PIL import Image
#from feature_extractor import FeatureExtractor
from datetime import datetime
from flask import Flask, request, render_template
from pathlib import Path
from keras.optimizers import Adam
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten
class FeatureExtractor:
def __init__(self):
input_shape = (224, 224, 3)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
for layer in base_model.layers:
layer.trainable = False
last = base_model.layers[-1].output
x = Flatten()(last)
x = Dense(1000, activation='relu', name='fc1')(x)
x = Dropout(0.3)(x)
x = Dense(10, activation='softmax', name='predictions')(x)
model = Model(base_model.input, x)
model.compile(optimizer=Adam(lr=0.001),
loss = 'categorical_crossentropy',metrics=['accuracy'])
self.model = Model(inputs=base_model.input, outputs=base_model.layers[-1].output)
def extract(self, img):
"""
Extract a deep feature from an input image
Args:
img: from PIL.Image.open(path) or tensorflow.keras.preprocessing.image.load_img(path)
Returns:
feature (np.ndarray): deep feature with the shape=(4096, )
"""
img = img.resize((224, 224))
img = img.convert('RGB')
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
feature = self.model.predict(x)[0]
return feature / np.linalg.norm(feature)
path = "/home/virtuag/www/storage/searchSCB.jpg"
#path = "c:/xamppp/htdocs/projet/V-stock/PWA/public/storage/searchSCB.jpg"
img = Image.open(path)
app = Flask(__name__)
fe = FeatureExtractor()
features = []
img_paths = []
for feature_path in Path("/home/virtuag/www/storage/images_article").glob("*.npy"):
#for feature_path in Path("c:/xamppp/htdocs/projet/V-stock/PWA/public/storage/images_article").glob("*.npy"):
features.append(np.load(feature_path))
#img_paths.append(Path("c:/xamppp/htdocs/projet/V-stock/PWA/public/storage/images_article") / (feature_path.stem + ".jpg"))
img_paths.append(Path("/home/virtuag/www/storage/images_article") / (feature_path.stem + ".jpg"))
features = np.array(features)
query = fe.extract(img)
dists = np.linalg.norm(features-query, axis=1)
ids = np.argsort(dists)[:30]
scores = [img_paths[id] for id in ids]
#print (img_paths)
#print(query)
and the error :
raceback (most recent call last): File "server.py", line 71, in <module> scores = [img_paths[id] for id in ids] File "server.py", line 71, in <listcomp> scores = [img_paths[id] for id in ids] TypeError: only integer scalar arrays can be converted to a scalar index
I am trying to run this on Amazon Sagemaker but I am getting this error while when I try to run it on my local machine, it works very fine.
this is my code:
import tensorflow as tf
import IPython.display as display
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False
import numpy as np
import PIL.Image
import time
import functools
def tensor_to_image(tensor):
tensor = tensor*255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor)>3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
content_path = tf.keras.utils.get_file('YellowLabradorLooking_nw4.jpg', 'https://example.com/IMG_20200216_163015.jpg')
style_path = tf.keras.utils.get_file('kandinsky3.jpg','https://example.com/download+(2).png')
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
def imshow(image, title=None):
if len(image.shape) > 3:
image = tf.squeeze(image, axis=0)
plt.imshow(image)
if title:
plt.title(title)
content_image = load_img(content_path)
style_image = load_img(style_path)
plt.subplot(1, 2, 1)
imshow(content_image, 'Content Image')
plt.subplot(1, 2, 2)
imshow(style_image, 'Style Image')
import tensorflow_hub as hub
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
tensor_to_image(stylized_image)
file_name = 'stylized-image5.png'
tensor_to_image(stylized_image).save(file_name)
This is the exact error I get:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-c47a4db4880c> in <module>()
53
54
---> 55 content_image = load_img(content_path)
56 style_image = load_img(style_path)
57
in load_img(path_to_img)
34
35 shape = tf.cast(tf.shape(img)[:-1], tf.float32)
---> 36 long_dim = max(shape)
37 scale = max_dim / long_dim
38
~/anaconda3/envs/amazonei_tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in iter(self)
475 if not context.executing_eagerly():
476 raise TypeError(
--> 477 "Tensor objects are only iterable when eager execution is "
478 "enabled. To iterate over this tensor use tf.map_fn.")
479 shape = self._shape_tuple()
TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.
Your error is being raised in this function load_img:
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
Specifically, this line:
long_dim = max(shape)
You are passing a tensor to the built-in Python max function in graph execution mode. You can only iterate through tensors in eager-execution mode. You probably want to use tf.reduce_max instead:
long_dim = tf.reduce_max(shape)