PlotDigitizer - How to Automatically Extract Data from Graph Image (#3)

Summary

This video tutorial introduces the process of automatically extracting data from a graph image using PlotDigitizer. It emphasizes the efficiency of automation over manual extraction and outlines the three-step autotracing method involving color selection, mask application, and algorithm selection. The importance of calibration, the use of different mask types, and the need for minor adjustments due to the algorithms being in beta phase are also highlighted. The tutorial guides users through extracting red colored scattered points and curves, showcasing the software's capabilities and directing them to the official documentation for further information.

Takeaways

• 📈 Start by uploading the graph image to PlotDigitizer.com for data extraction.
• 🎨 Calibrate the graph by setting X1, X2, Y1, and Y2 markers to known coordinates.
• 🖌️ Choose the color of the data points or curves you wish to extract using the color picker tool.
• 🎭 Apply a mask to the area of interest; options include pen mask and box mask for precise selection.
• ✏️ Use the eraser mask to remove unwanted portions, such as overlapping or interfering elements.
• 🔍 Select the appropriate algorithm for the type of data extraction; point, curve, or histogram algorithms are available.
• 🔄 Algorithms are in beta, so minor adjustments may be necessary for accurate data extraction.
• 🔄 For overlapping points, manual corrections can be made to ensure data accuracy.
• 📊 View extracted data in the dataset table and save it for further analysis or processing.
• 🔄 Repeat the process for different colors or types of data within the same graph.
• 📈 Adjust point density for curves using the provided slider for a clearer representation.

Q & A

What is the main topic of the video tutorial?

The main topic of the video tutorial is how to automatically extract data from a graph image using PlotDigitizer.com.

Why is watching the earlier videos in the series important before starting this tutorial?

Watching the earlier videos is important because they provide foundational knowledge and context necessary for understanding the more advanced concepts taught in this video.

What is the first step in the automatic data extraction process?

The first step in the automatic data extraction process is to calibrate the graph by moving the calibration markers X1 and X2, and Y1 and Y2 to known X and Y coordinates.

How does one calibrate a graph in PlotDigitizer?

To calibrate a graph in PlotDigitizer, you use the zoom panel to drag and drop the calibration markers to the respective known X and Y coordinates on the graph.

What are the different types of masks available for autotracing in PlotDigitizer?

The different types of masks available for autotracing in PlotDigitizer are the pen mask and the box mask.

What is the purpose of the pen mask in autotracing?

The pen mask is used for extracting curves and provides more flexibility as you can control its thickness, making it ideal for precise tracing.

How can the eraser mask be utilized in the autotracing process?

The eraser mask is used to remove or eliminate unwanted portions from the masked region, such as noises or interfering objects with the same color as the object of interest.

What is the recommended algorithm for extracting points in PlotDigitizer?

For extracting points, the point algorithm is recommended in PlotDigitizer.

What should one do if the algorithms detect overlapping points as a single point?

If the algorithms detect overlapping points as a single point, manual corrections can be made to fix such minor issues.

How can one view the extracted data?

The extracted data can be viewed in the dataset table on the PlotDigitizer interface.

What is the process for extracting data from both curves and scattered points in the graph?

The process involves calibrating the graph, selecting the desired color for extraction, applying the appropriate mask, choosing the correct algorithm, and making manual adjustments if necessary. This is repeated for each type of data to be extracted.

Outlines

00:00

📊 Introduction to Automatic Data Extraction with PlotDigitizer

This paragraph introduces viewers to the video tutorial on PlotDigitizer.com, which focuses on the automatic extraction of data from graph images. It emphasizes the time-saving benefits of automation over manual data extraction and encourages viewers to watch previous tutorials for context. The video then proceeds to demonstrate the upload and calibration of a graph image, highlighting the importance of accurate calibration. The paragraph outlines the three-step process of autotracing: color picking, mask application, and algorithm selection. It also explains the use of the zoom panel for precision and the need for careful color picking to avoid errors in autotracing.

05:04

🛠️ Selecting and Applying Algorithms for Data Extraction

In this paragraph, the tutorial delves into the various algorithms available in PlotDigitizer for data extraction, such as cluster, points, curves, and histogram algorithms. It directs viewers to the official documentation for more information on these algorithms. The video then demonstrates the selection of the point algorithm for extracting points and acknowledges that the algorithms are in beta, which may require minor adjustments. The paragraph also addresses the issue of overlapping points being detected as one and suggests manual corrections. Finally, it mentions the ability to adjust point density for curves and teases that other algorithms will be discussed in subsequent videos.

Mindmap

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Keywords

💡PlotDigitizer

PlotDigitizer is a software tool featured in the video that enables users to extract data from graph images automatically. It is the central theme of the tutorial, as the entire video is dedicated to demonstrating how to use this tool effectively. The script outlines various functionalities of PlotDigitizer, such as calibration, color picking, mask application, and algorithm selection for data extraction.

💡Data Extraction

Data extraction is the process of obtaining raw data from various sources and converting it into a format that can be easily analyzed or used for further processing. In the context of the video, data extraction refers to the act of pulling numerical data from graph images, which can be done manually or, as shown, automatically using PlotDigitizer.

💡Calibration

Calibration in the context of the video refers to the process of adjusting the scale and axes of the graph image in PlotDigitizer to match known values. This step is crucial for ensuring the accuracy of the data extracted from the graph image, as it sets the reference points for the X and Y coordinates.

💡Color Picker

The color picker is a tool within PlotDigitizer that allows users to select a specific color from the graph image for autotracing. This feature is essential for the software to identify and extract data points or curves of a particular color, ensuring that the correct data is extracted.

💡Mask

A mask in PlotDigitizer is a tool used to define the region of interest for data extraction. It can be applied to isolate specific data points or curves, allowing the user to focus on the relevant parts of the graph image. The script mentions two types of masks: pen mask and box mask, each serving a different purpose based on the shape and distribution of the data.

💡Algorithm

In the context of the video, an algorithm refers to the set of rules or procedures used by PlotDigitizer to process and extract data from the graph image. Different algorithms are designed for different types of data, such as points, curves, histograms, and more. The choice of algorithm affects the accuracy and efficiency of the data extraction process.

💡Autotracing

Autotracing is a feature in PlotDigitizer that automates the process of identifying and extracting data points or curves from a graph image based on the user's selected color and mask settings. It streamlines the data extraction process, saving time and effort compared to manual extraction methods.

💡Dataset Table

The dataset table is an interface within PlotDigitizer that displays the extracted data points or values in a structured format. It allows users to review, edit, and save the extracted data for further analysis or use.

💡Point Density

Point density refers to the number of data points or the level of detail in the representation of a curve or graph. In PlotDigitizer, adjusting the point density allows users to control the smoothness and precision of the extracted curves, which can be important for accurate data analysis.

💡Eraser Mask

The eraser mask is a tool in PlotDigitizer used to remove or eliminate unwanted portions from the masked region during the data extraction process. It helps in cleaning up the data by erasing noise or interfering objects that may have been included in the initial mask.

💡Algorithms (Cluster, Points, Curves, Histogram)

The video mentions various algorithms available in PlotDigitizer, each designed for extracting different types of data. Cluster, points, curves, and histogram algorithms cater to specific data extraction needs, allowing users to choose the most appropriate method for their analysis. The algorithms are in beta phase, which means they may require minor adjustments for optimal results.

Highlights

Introduction to PlotDigitizer's video tutorial series on automatic data extraction from graph images.

Manual data extraction is tedious; automation saves time and effort.

Uploading the graph image is the first step in the data extraction process.

Calibration of the graph is necessary, using known X and Y coordinates for markers X1, X2, Y1, and Y2.

The coordinates of the cursor are displayed after calibration, aiding in precise data extraction.

Autotracing, or automatic extraction, is divided into three simple steps for ease of use.

The first step in autotracing is selecting the color of the data points to be extracted using the color picker.

Masking is the second step, where you apply either a pen mask or a box mask to the area from which data is to be extracted.

The pen mask offers flexibility for extracting curves, while the box mask is suitable for large, scattered objects.

The eraser mask is used to remove unwanted portions such as noises or interfering objects from the masked area.

Algorithm selection is the final step, with options like cluster, points, curves, and histogram.

PlotDigitizer's algorithms are in beta, and minor adjustments may be needed for accurate data extraction.

Extracted data points can overlap, and manual correction may be necessary for precise results.

All extracted data is displayed on a dataset table for review and saving.

Adjusting point density is possible for curves to refine the extracted data.

The tutorial series will cover additional algorithms in upcoming videos.

Transcripts

Hello everyone,

Welcome to PlotDigitizer.com. In this video tutorial, we will learn how to automatically

extract data from the graph image.

Before we begin, this tutorial is a part of the video series to a complete guide on PlotDigitizer.

If you haven’t watched the earlier videos, please do so.

In one of the previous videos, we have experienced extracting data in the manual mode is a very

tedious and unexciting exercise. Automating data extraction can save a lot of your time

and effort.

Before we move forward, let's upload the graph image.

As we can see it is an X Y graph.

There are two curves. And there are scattered points.

First, we have to calibrate this graph.

Move the calibration markers X1 and X2 to two different positions where we know X coordinates.

And the same for Y1 and Y2.

Use the zoom panel while dragging and dropping the markers.

Now, the graph has been calibrated. You can see the coordinates of the cursor beneath

the zoom panel.

To make things simple, automatic extraction, or what I call autotracing, can be divided

into three simple steps.

The first step in automatic extraction is picking the color. You have to pick the color

of the object that you want to extract.

So, in this graph, I first wish to extract red colored scattered points.

Pick the color of the points using the color picker from the menu bar.

Use the zoom panel while color picking.

Otherwise, you might end up picking the wrong shade of color. And in that case, the autotracing

might not work as expected.

Once picked, you can see the color over on the icon.

Now, the second step is to add the mask to the region from which we want to extract data.

In the case of our example, we have to apply the mask to the entire area occupied by scattered

points.

Now, there are two types of masks available to us.

The first is the pen mask.

And the second is the box mask.

The box mask, as the name says, overlays the image with a box.

You can apply the box multiple times if you want.

On the other hand, the pen mask is like a pen.

You can control the thickness of the pen mask.

You can increase the thickness

And You can also decrease the thickness.

The pen mask is best for extracting curves, since it gives you more flexibility, while

the box mask is perfect in situations where objects are spread over a large portion of

the image, like scattered points.

The third option is the eraser mask. It is an eraser.

And you can also change the size of the eraser with this slider.

The erase mask is applied when you want to remove or eliminate the unwanted portion from

the masked region.

The unwanted portions can be noises, as this legend.

Or it could be any interfering objects having the same color.

In our graph, this red curve is interfering with the red-colored points.

So, let’s erase this curve from the masked portion.

The final option clears everything.

We wish to extract red colored points first, so let’s mask them.

After masking, we have to select the right algorithm.

PlotDigitizer is packed with several algorithms, like cluster, points, curves, histogram, etc.

There are also vertical and horizontal bar algorithms, which we will cover in one of

the next videos. You can find more about each of these algorithms in our official documentation.

Visit plotdigitizer.com/docs to know more.

Since we are extracting points, we will select the point algorithm.

Done. All the points are extracted.

Note to consider here, the algorithms are still in the beta phase, so sometimes you

might need to do minor adjustments.

In our case, there are two points overlapping over here, but they are detected as one single

point.

You can always manually fix such minor corrections.

All the extracted data can be seen on the dataset table.

We will save them first and then proceed to the others.

We will repeat the same steps for the rest.

For the curve, you can increase or decrease the point density with this slider.

Now, we have extracted both curves and scattered points. There are other algorithms too, which

we will discuss in the next videos.