Trending February 2024 # Image Classification Model Trained Using Google Colab # Suggested March 2024 # Top 2 Popular

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 This article was published as a part of the Data Science Blogathon.

Introduction

?

Using labeled sample photos, ing is image classification. Raw pixel data was the only input for early computer vision algorithms. However, pixel data alone does not provide a sufficiently consistent representation to encompass the many oscillations of an item as represented in an image. The placement of the object, the background behind it, ambient lighting, the camera angle, and the camera focus can all affect the raw pixel data.

Traditional computer vision models added new components derived from pixel data, such as textures, colour histograms, and shapes, to model objects more flexibly. The drawback of this approach was that feature engineering became very time-consuming because of the enormous number of inputs that needed to be changed. Which hues were crucial for categorizing cats? How flexible should the definitions of shapes be? Because characteristics had to be adjusted so precisely, it was difficult to create robust models.

Train Image Classification Model

A fundamental machine learning workflow is used in this tutorial:

Analyze dataset

Create an Input pipeline

Build the model

Train the model

Analyze the model

Setup And Import TensorFlow and other libraries import itertools import os import matplotlib.pylab as plt import numpy as np import tensorflow as tf import tensorflow_hub as hub print("TF version:", tf.__version__) print("Hub version:", hub.__version__) print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")

The output looks like this:

Select the TF2 Saved Model Module to Use

More TF2 models that produce feature vectors for images may be found here. (Note that TF1 Hub-format models won’t function here.)

There are numerous models that could work. Simply choose a different option from the list in the cell below, then proceed with the notebook. Here, I selected  Inception_v3 and automatically, it chose the Image size from the below list as 299 x 299. 

model_name = "resnet_v1_50" # @param ['efficientnetv2-s', 'efficientnetv2-m', 'efficientnetv2-l', 'efficientnetv2-s-21k', 'efficientnetv2-m-21k', 'efficientnetv2-l-21k', 'efficientnetv2-xl-21k', 'efficientnetv2-b0-21k', 'efficientnetv2-b1-21k', 'efficientnetv2-b2-21k', 'efficientnetv2-b3-21k', 'efficientnetv2-s-21k-ft1k', 'efficientnetv2-m-21k-ft1k', 'efficientnetv2-l-21k-ft1k', 'efficientnetv2-xl-21k-ft1k', 'efficientnetv2-b0-21k-ft1k', 'efficientnetv2-b1-21k-ft1k', 'efficientnetv2-b2-21k-ft1k', 'efficientnetv2-b3-21k-ft1k', 'efficientnetv2-b0', 'efficientnetv2-b1', 'efficientnetv2-b2', 'efficientnetv2-b3', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'bit_s-r50x1', 'inception_v3', 'inception_resnet_v2', 'resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152', 'resnet_v2_50', 'resnet_v2_101', 'resnet_v2_152', 'nasnet_large', 'nasnet_mobile', 'pnasnet_large', 'mobilenet_v2_100_224', 'mobilenet_v2_130_224', 'mobilenet_v2_140_224', 'mobilenet_v3_small_100_224', 'mobilenet_v3_small_075_224', 'mobilenet_v3_large_100_224', 'mobilenet_v3_large_075_224'] model_handle_map = { } model_image_size_map = { "efficientnetv2-s": 384, "efficientnetv2-m": 480, "efficientnetv2-l": 480, "efficientnetv2-b0": 224, "efficientnetv2-b1": 240, "efficientnetv2-b2": 260, "efficientnetv2-b3": 300, "efficientnetv2-s-21k": 384, "efficientnetv2-m-21k": 480, "efficientnetv2-l-21k": 480, "efficientnetv2-xl-21k": 512, "efficientnetv2-b0-21k": 224, "efficientnetv2-b1-21k": 240, "efficientnetv2-b2-21k": 260, "efficientnetv2-b3-21k": 300, "efficientnetv2-s-21k-ft1k": 384, "efficientnetv2-m-21k-ft1k": 480, "efficientnetv2-l-21k-ft1k": 480, "efficientnetv2-xl-21k-ft1k": 512, "efficientnetv2-b0-21k-ft1k": 224, "efficientnetv2-b1-21k-ft1k": 240, "efficientnetv2-b2-21k-ft1k": 260, "efficientnetv2-b3-21k-ft1k": 300, "efficientnet_b0": 224, "efficientnet_b1": 240, "efficientnet_b2": 260, "efficientnet_b3": 300, "efficientnet_b4": 380, "efficientnet_b5": 456, "efficientnet_b6": 528, "efficientnet_b7": 600, "inception_v3": 299, "inception_resnet_v2": 299, "nasnet_large": 331, "pnasnet_large": 331, } model_handle = model_handle_map.get(model_name) pixels = model_image_size_map.get(model_name, 224) print(f"Selected model: {model_name} : {model_handle}") IMAGE_SIZE = (pixels, pixels) print(f"Input size {IMAGE_SIZE}") BATCH_SIZE = 16#@param {type:"integer"}

The inputs are scaled correctly for the selected module. A larger dataset helps with training, especially when fine-tuning (i.e., random distortions of an image each time it is read).

Our unique dataset should be organized as shown in the figure below.

Our customized dataset must now be uploaded to Drive. We must set the data augmentation parameter to true once our dataset needs augmentation.

data_dir = "/content/Images" def build_dataset(subset): return tf.keras.preprocessing.image_dataset_from_directory(data_dir,validation_split=.10,subset=subset,label_mode="categorical",seed=123,image_size=IMAGE_SIZE,batch_size=1) train_ds = build_dataset("training") class_names = tuple(train_ds.class_names) train_size = train_ds.cardinality().numpy() train_ds = train_ds.unbatch().batch(BATCH_SIZE) train_ds = train_ds.repeat() normalization_layer = tf.keras.layers.Rescaling(1. / 255) preprocessing_model = tf.keras.Sequential([normalization_layer]) do_data_augmentation = False #@param {type:"boolean"} if do_data_augmentation: preprocessing_model.add(tf.keras.layers.RandomRotation(40)) preprocessing_model.add(tf.keras.layers.RandomTranslation(0, 0.2)) preprocessing_model.add(tf.keras.layers.RandomTranslation(0.2, 0)) # Like the old tf.keras.preprocessing.image.ImageDataGenerator(), # image sizes are fixed when reading, and then a random zoom is applied. # RandomCrop with a batch size of 1 and rebatch later. preprocessing_model.add(tf.keras.layers.RandomZoom(0.2, 0.2)) preprocessing_model.add(tf.keras.layers.RandomFlip(mode="horizontal")) train_ds = train_ds.map(lambda images, labels:(preprocessing_model(images), labels)) val_ds = build_dataset("validation") valid_size = val_ds.cardinality().numpy() val_ds = val_ds.unbatch().batch(BATCH_SIZE) val_ds = val_ds.map(lambda images, labels:(normalization_layer(images), labels)) Output: Defining the Model

All that is required is to use the Hub module to layer a linear classifier on top of the feature extractor layer.

We initially use a non-trainable feature extractor layer for speed, but you can alternatively enable fine-tuning for better precision.

do_fine_tuning = True print("Building model with", model_handle) model = tf.keras.Sequential([ # Explicitly define the input shape so the model can be properly # loaded by the TFLiteConverter tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE + (3,)), hub.KerasLayer(model_handle), tf.keras.layers.Dropout(rate=0.2), tf.keras.layers.Dense(len(class_names),activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(0.0001)) ]) model.build((None,)+IMAGE_SIZE+(3,)) model.summary()

Output look below

Model Training

optimizer=tf.keras.optimizers.SGD(learning_rate=0.005, momentum=0.9), loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1), metrics=[‘accuracy’])

steps_per_epoch = train_si validation_steps = valid_size hist = model.fit( train_ds, epochs=50, steps_per_epoch=steps_per_epoch, validation_data=val_ds, validation_steps=validation_steps).history

The output looks below:

Once training is completed, we need to save the model by using the following code:

model.save ("save_locationmodelname.h5") Conclusion

This blog post categorized pictures using convolutional neural networks (CNNs) based on their visual content. This data set was utilized for testing and training CNN. Its accuracy percentage is greater than 98 per cent. We must employ tiny, grayscale images as our teaching resources. These photos require a pervasive processing time compared to other regular JPEG photos. A model with more layers and more picture data used to train the network on a cluster of GPUs would classify images more accurately. Future development will concentrate on categorizing enormous coloured images that are very useful for the segmentation process of images.

Key Takeaways

Image classification, a branch of computer vision, classifies and labels sets of pixels or vectors inside an image using a set of specified tags or categories that an algorithm has been trained on.

It is possible to differentiate between supervised and unsupervised classification.

In supervised classification, the classification algorithm is trained using a set of images and their associated labels.

Unsupervised classification algorithms only use raw data for training.

You require a sizable diversity of datasets with accurately labelled data to create trustworthy picture classifiers.

Thanks for reading!

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Music Genres Classification Using Deep Learning Techniques

This article was published as a part of the Data Science Blogathon

Introduction:

In this blog, we will discuss the classification of music files based on the genres. Generally, people carry their favorite songs on smartphones. Songs can be of various genres. With the help of deep learning techniques, we can provide a classified list of songs to the smartphone user. We will apply deep learning algorithms to create models, which can classify audio files into various genres. After training the model, we will also analyze the performance of our trained model.

Dataset:

We will use GITZAN dataset, which contains 1000 music files. Dataset has ten types of genres with uniform distribution. Dataset has the following genres: blues, classical, country, disco, hiphop, jazz, reggae, rock, metal, and pop. Each music file is 30 seconds long.

Process Flow:

Figure 01 represents the overview of our methodology for the genre classification task. We will discuss each phase in detail. We train three types of deep learning models to explore and gain insights from the data.

Fig. 01

First, we need to convert the audio signals into a deep learning model compatible format. We use two types of formats, which are as follows:

1. Spectrogram generation:

A spectrogram is a visual representation of the spectrum signal frequencies as it varies with time. We use librosa library to transform each audio file into a spectrogram. Figure 02 shows spectrogram images for each type of music genre.

Fig. 02

2. Wavelet generation: –

The Wavelet Transform is a transformation that can be used to analyze the spectral and temporal properties of non-stationary signals like audio. We use librosa library to generate wavelets of each audio file. Figure 03 shows wavelets of each type of music genre.

Fig. 03

3, 4. Spectrogram and Wavelet preprocessing

From Figure 02 and 03, it is clear that we treat our data as image data. After generating spectrograms and wavelets, we apply general image preprocessing steps to generate training and testing data. Each image is of size (256, 256, 3).

5. Basic CNN model training:

 After preprocessing the data, we create our first deep learning model. We construct a Convolution Neural Network model with required input and out units. The final architecture of our CNN model is shown in Figure 04. We use only spectrogram data for the training and testing.

Fig. 04

We train our CNN model for 500 epochs with Adam optimizer at a learning rate of 0.0001. We use categorical cross-entropy as the loss function. Figure 05 shows the training and validation losses and model performance in terms of accuracy.

Fig. 05

6. Transfer learning-based model training

We have only 60 samples of each genre for training. In this case, transfer learning could be a useful option to improve the performance of our CNN model. Now, we use the pre-trained mobilenet model to train the CNN model. A schematic architecture is shown in Figure 06.

Fig. 06

The transfer learning-based model is trained with the same settings as used in the previous model. Figure 07 shows the training and validation loss and model performance in terms of accuracy. Here, also we use only spectrogram data for the training and testing.

Fig. 07

7. Multimodal training

We will pass both spectrogram and wavelet data into the CNN model for the training in this experiment. We are using the late-fusion technique in this multi-modal training. Figure 08 represents the architecture of our multi-modal CNN model. Figure 09 shows the loss and performance scores of the model with respect to epochs.

Fig. 08 Fig. 09

Comparison:

Figure 10 shows a comparative analysis of the loss and performance of all three models. If we analyze the training behavior of all three models, we found that the basic CNN model has large fluctuations in its loss values and performance scores for training and testing data. The multimodal model has shown the least variance in performance. Transfer learning model performance increases gradually compared to multimodal and basic CNN models. Validation loss value shot up suddenly after the 30 epochs. On the other side, validation loss decreases continuously for the other two models.

Fig. 10

Testing the models

 After training our models, we test each model on the 40% test data. We calculate precision, recall, and F-score for each music genre (class). Our dataset is balanced; therefore, the macro average and weighted average of precision, recall, and F-score are the same.

1. Basic CNN model

 Figure 11 presents the results of our CNN model on the test data. CNN model was able to classify “classical” genre music with the highest F1-score. CNN performed worst for “Rock” and “reggae” genre music. Figure 12 shows the confusion matrix of the CNN model on the test data.

Fig. 11

Fig. 12

2. Transfer learning based model

We used the transfer learning technique to improve the performance of genre classification. Figure 13 presents the results of the transfer learning-based model on test data. F1-score for “hiphop”, “jazz”, and “pop” genres increased due to transfer learning. If we look at overall results, we have achieved only a minor improvement after applying transfer learning. Figure 14 shows the confusion matrix for the transfer learning model on the test data.

Fig. 13

Fig. 14

3. Multimodal-based model: We have used both spectrogram and wavelet data to train the multimodal-based model. In the same way, we perform the testing. We have found very surprising results. Instead of improvement, our performance reduced drastically. We have achieved only 38% of F1-score while using a multi-modal approach. Figure 16 shows the confusion matrix of the multimodal-based model on the test data.

Fig. 15 Fig. 16

Conclusion:

In this post, we have performed music genre classification using Deep learning techniques. The transfer learning-based model has performed best among all three models. We have used the Keras framework for the implementation on the google Collaboratory platform. Source code is available at the following GitHub link along with spectrogram and wavelet data on google drive. You don’t need to generate spectrograms and wavelets from the audio files.

GitHub Link. . Spectrogram and wavelets data link.

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A Beginners’ Guide To Image Similarity Using Python

If you believe that our Test Image is similar to our first reference image you are right. If you do believe otherwise then let’s find out together with the power of mathematics and programming.

Every image is stored in our computer in the form of numbers and a vector of such numbers that can completely describe our image is known as an Image Vector.

Euclidean Distance:

Euclidean Distance represents the distance between any two points in an n-dimensional space. Since we are representing our images as image vectors they are nothing but a point in an n-dimensional space and we are going to use the euclidean distance to find the distance between them.

Histogram:

A histogram is a graphical display of numerical values. We are going to use the image vector for all three images and then find the euclidean distance between them. Based on the values returned the image with a lesser distance is more similar than the other.

To find the similarity between the two images we are going to use the following approach :

Read the image files as an array.

Since the image files are colored there are 3 channels for RGB values. We are going to flatten them such that each image is a single 1-D array.

Once we have our image files as an array we are going to generate a histogram for each image where for each index 0 – 255 we are going the count the occurrence of that pixel value in the image.

Once we have our histograms we are going to use the L2-Norm or Euclidean Distance to find the difference the two histograms.

Based on the distance between the histogram of our test image and the reference images we can find the image our test image is most similar to.

Coding for Image Similarity in Python Import the dependencies we are going to use from PIL import Image from collections import Counter import numpy as np

We are going to use NumPy for storing the image as a NumPy array, Image to read the image in terms of numerical values and Counter to count the number of times each pixel value (0-255) occurs in the images.

Reading the Image



We can see that out image has been successfully read as a 3-D array. In the next step, we need to flatten this 3-D array into a 1-Dimensional array.

flat_array_1 = array1.flatten() print(np.shape(flat_array_1)) >>> (245760, )

We are going to do the same steps for the other two images. I will skip that here so that you can try your hands on it too.

Generating the Count-Histogram-Vector : RH1 = Counter(flat_array_1)

The following line of code returns a dictionary where the key corresponds to the pixel value and the value of the key is the number of times that pixel is present in the image.

One limitation of Euclidean distance is that it requires all the vectors to be normalized i.e both the vectors need to be of the same dimensions. To ensure that our histogram vector is normalized we are going to use a for loop from 0-255 and generate our histogram with the value of the key if the key is present in the image else we append a 0.

H1 = [] for i in range(256): if i in RH1.keys(): H1.append(RH1[i]) else: H1.append(0)

The above piece of code generates a vector of size (256, ) where each index corresponds to the pixel value and the value corresponds to the count of the pixel in that image.

We follow the same steps for the other two images and obtain their corresponding Count-Histogram-Vectors. At this point we have our final vectors for both the reference images and the test image and all we need to do is calculate the distances and predict.

Euclidean Distance Function : def L2Norm(H1,H2): distance =0 for i in range(len(H1)): distance += np.square(H1[i]-H2[i]) return np.sqrt(distance)

The above function takes in two histograms and returns the euclidean distance between them.

Evaluation :

Since we have everything we need to find the image similarities let us find out the distance between the test image and our first reference image.

dist_test_ref_1 = L2Norm(H1,test_H) print("The distance between Reference_Image_1 and Test Image is : {}".format(dist_test_ref_1)) >>> The distance between Reference_Image_1 and Test Image is : 9882.175468994668

Let us now find out the distance between the test image and our second reference image.

dist_test_ref_2 = L2Norm(H2,test_H) print("The distance between Reference_Image_2 and Test Image is : {}".format(dist_test_ref_2)) >>> The distance between Reference_Image_2 and Test Image is : 137929.0223122023

How To Set The Padding Of Image Using Fabricjs?

In this tutorial, we are going to learn how to set the padding of Image using FabricJS. We can create an Image object by creating an instance of fabric.Image. Since it is one of the basic elements of FabricJS, we can also easily customize it by applying properties like angle, opacity etc. In order to set the padding of Image, we use the padding property.

Syntax Parameters

element − This parameter accepts HTMLImageElement, HTMLCanvasElement, HTMLVideoElement or String which denotes the image element. The String should be a URL and would be loaded as an image.

options (optional) − This parameter is an Object which provides additional customizations to our object. Using this parameter origin, stroke width and a lot of other properties can be changed related to the image object of which padding is a property.

callback (optional) − This parameter is a function which is to be called after eventual filters are applied.

Options Keys

padding − This property accepts a Number value which denotes the padding between an object and its controlling borders.

Default appearance of Image object when padding property is not used Example

Let’s see a code example to understand how the Image object appears when the padding property is not used.

Default appearance

of

Image object when padding property is not used You can select the image object to see that there is no padding between the object and its controlling borders

var

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“canvas”

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setWidth

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canvas

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setHeight

(

250

)

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var

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getElementById

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var

image

=

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Image

(

imageElement

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{

top

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left

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50

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stroke

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“green”

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strokeWidth

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5

,

}

)

;

canvas

.

add

(

image

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Passing padding property as key Example

In this example, we are assigning a value to the padding property. In this case, we have assigned it a value of 15. Therefore, there is 15px padding in between the image object and its controlling borders.

You can select the image object to see that there is

15

px padding between the object and its controlling borders

var

canvas

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new

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(

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;

canvas

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setWidth

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scrollWidth

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setHeight

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imageElement

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50

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left

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stroke

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strokeWidth

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5

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padding

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15

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}

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;

canvas

.

add

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image

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How To Straighten An Image With Animation Using Fabricjs?

In this tutorial, we are going to learn how to straighten an Image with animation using FabricJS. We can create an Image object by creating an instance of fabric.Image. Since it is one of the basic elements of FabricJS, we can also easily customize it by applying properties like angle, opacity etc. In order to straighten an Image with animation, we use the fxStraighten method.

Syntax fxStraighten(callbacks: Object): fabric.Object Parameters

callbacks − This parameter is an Object with callback functions which can be used to change certain properties related to the animation.

Using the straighten method Example

Let’s see a code example of how the Image object appears when the straighten method is used instead of fxstraighten. This will help us to realize the difference between them. The straighten method simply straightens the object rotating it from its current angle to 0, 90,180, 270 etc depending on which one is closer. However, fxstraighten works in the same way but with animation.

You can see that there is no animation but the image has been straightened

var

canvas

=

new

fabric

.

Canvas

(

“canvas”

)

;

canvas

.

setWidth

(

document

.

body

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scrollWidth

)

;

canvas

.

setHeight

(

250

)

;

var

imageElement

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document

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getElementById

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“img1”

)

;

var

image

=

new

fabric

.

Image

(

imageElement

,

{

top

:

10

,

left

:

110

,

skewX

:

15

,

angle

:

45

,

}

)

;

canvas

.

add

(

image

)

;

image

.

straighten

(

)

;

Using the fxstraighten method Example

In this example, we have used the fxstraighten method to straighten the image object and also display a simple animation. The image object has a 45 degree angle which gets straightened by rotating back to 0 degree. Along with this, the onChange function is invoked at every step of the animation while the onComplete function is invoked only at the completion of the animation which is why in the end, our image object is scaled horizontally by a factor of 1.5 and moves to the left by value of 130.

You can see that the image gets straightened

while

also displaying an animation

var

canvas

=

new

fabric

.

Canvas

(

“canvas”

)

;

canvas

.

setWidth

(

document

.

body

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scrollWidth

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;

canvas

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setHeight

(

250

)

;

var

imageElement

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getElementById

(

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)

;

var

image

=

new

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Image

(

imageElement

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{

top

:

10

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left

:

110

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skewX

:

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angle

:

45

,

}

)

;

canvas

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add

(

image

)

;

image

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fxStraighten

(

{

onChange

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)

{

canvas

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renderAll

(

)

;

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,

onComplete

(

)

{

image

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set

(

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)

;

image

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set

(

“scaleX”

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)

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canvas

.

renderAll

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)

;

}

,

}

)

;

Reverse Image Search On Google, Bing, Android & Iphone

Reverse image search is important to discover images from different sources around the Internet. It helps you to file DMCA and find copyright issues. Many times, it is helpful in finding a business specific image or creating content. This facility can also be used to find images that are copyright protected. It may also help you find the origin of an image, graph, or even artwork.

Here are a few solutions that help you to perform reverse image search:

Method 1: Google Reverse Image Search How to Perform Reverse Google Image Search on Desktop Browsers

This method works when you have loaded Google image search in your browser.

Perform the following steps:

Step 2) This will display a new search offering two options for you. In the first option, you have to paste the image by URL.

The second option requires you to choose an image file.

Both options will display the same result as shown in the following screen:

Google Reverse Image Search Using Chrome on Desktop/Android.

If you have Chrome, then you do not require to find the source of the image online. However, this method can be applied to the photos that are already uploaded and found while browsing online.

Reverse image search in iPhone/iPad/ Android

You can also perform a search with the digital image from Google search result in iPhone/iPad/Android device.

You need to perform the following steps for that:

Step 1) Open Google Images main page.

Search for the digital image you want to use.

Step 2) Tap on the image and open it.

Step 3) Tap on image

to view the result.

Step 4) You will see similar images as shown in the following screen:

Google Reverse Image Search Using Chrome on Android

Perform the following steps to reverse image search using Chrome on Android:

Step 1) Open Chrome browser and tap on

available at the top right corner of the screen.

Step 2) The following menu will be displayed.

Tap on “Desktop site.”

Step 3) Perform the same steps mentioned in the previous method.

Method 2: Bing Visual Search How to do a Reverse Image Search on Desktop Browsers/iOS/Android

Bing Visual Search is an image-based searching site that is available to users across a variety of surfaces. You can use the image as your input and get good knowledge and actions.

It also enables you to search the web, shop online, and more through your photo you have captured. Bing Visual Search uses image and its attributes to display similar pictures.

Perform the following steps to reverse Google image search on desktop browsers:

Step 2) The following popup will be displayed, which has two options. In the first option, you have to drag and drop or browse images from your PC.

The second option requires you to paste the URL of the image.

Both options will display the same result as shown in the following screen:

If you want to perform a reverse image search in mobile device, then there are two choices.

1) Using Bing website

2) Using Android and iOS apps. You need to follow the same steps mentioned in the above method.

Method 3: Third-Party Image Search Engines How to do a Reverse Google Image Search on Desktop Browsers /iOS/Android using TinEye

TinEye is a reverse search engine that enables you to submit the image and find out where it came from and how it is used. This website provides visually similar images, additional size of the same image, and more.

Perform the following steps to Reverse Google Image Search on desktop browsers /iOS/Android devices using TinEye:

Step 1) Open TinEye Images main page. It will give you two options. In the first option, you have to upload the image you want to search.

The second option requires you to paste the image URL.

Both options will display the same result as shown in the following screen:

If you want to perform a reverse image search in a mobile device, Android, or iOS device, then you need to perform the same steps mentioned above.

How to do a Reverse Google Image Search on Desktop Browsers /iPhone/ Android using Yandex

Yandex is a reverse image search engine that provides visually similar images, additional size of the same image, and more. It offers a unique search facility that works on mobile devices from your web browser.

Perform the following steps to Reverse Google Image Search on desktop browsers using Yandex:

Step 1) Open Yandex main page.

It will give you two options. In the first option, you have to select the image you want to search.

The second option requires you to paste the URL of the image.

Both options will display the same result as shown in the following screen:

If you want to perform a reverse search in iOS mobile device, then you need to download the Yandex browser application into your phone. For the Android OS, you need to download Yandex application and perform the same steps.

Method 4: Apps for Reverse Image Search on Android and iPhone CamFind

CamFind is an image recognition and visual search mobile application. It allows you to identify any item just by capturing photos from your mobile phone.

This app provides a wide range of information, including related images, price comparisons, web, and local shopping results. It is available free for both Android and iOS devices.

Reversee

Reversee is a reverse image search app for iOS that allows you to find by photo. You can use this app to discover web pages with picture and social network profiles of someone. This application can be used inside other apps like Chrome, Safari, and any other programs that export URL or image.

Eye Lens

Eye Lens is an extension that can be used to find related images from the Internet. This app makes it easy to find by images in Google, Yandex, and TinEye. The search result in this program includes similar images, websites that contain these pictures, and other size of the photo you have searched.

How Does Reverse Image Search Work?

The reverse image search facility uses deep learning algorithm to find the photos. All the tools or search engine uses the similar AI algorithms to find the pictures. Once you find any photo, these apps show the result with a list of websites where similar images are available. Many applications also display the pictures with metadata like size and color.

Applications of Reverse Image Search

Reverse image search has numerous different uses that can help you to perform powerful tasks.

Here are the applications of reverse image search:

Find similar images: It is helpful in finding a specific business image for creating content.

Discover people using your images: This is used when you have an eCommerce page to monitor your brand. It can also be used to find people who are using images you have used on your websites and blogs.

Find dimensions and metadata of any image: It is basically used to find the images which are copyright protected. Reverse search also helps you to file DMCA.

Checking spun content: This is applicable if a blog writer uses graphic images and writes content around it, and does not give credit to the author of the image. It helps to find such people on the Internet.

Finding image source to give credit: It is used when you want to give credit to a person using graphics or images. In case if you forgot when it came from, then reverse image search will give you a link to the source.

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