Cv2 Sharpen Image

Image Processing and Enhancement CV2 frameworks. #See slide number 22 from IrfanEssa-CP-02-5-Filtering. We've provided some basic examples to help you discover possible uses for your Raspberry Pi and to get started with software available in Raspbian. I wanted to extract each pixel values so that i can use them for locating simple objects in an image. Also, as an integrated part of our EPSON RC+ development environment, Epson CV2 systems with Vision Guide help reduce development time for vision guidance applications. If the parameter is too small, multiple neighbor. $\begingroup$ @Emre: I like to implement an algorithm for low light noise reduction rather than using neat image every time. Sharpening images is an ill-posed problem. This series aims at providing an overview of the Open-CV library, Its functions, application & capabilities. Turns out that,the image was of very low resolution,hence the distortion in the edges. Edge Detection is an image processing technique to find boundaries of objects in the image. startWindowThread() cv2. Image Enhancement in Python For instance, when you scan a document, the output image might have a lower quality than the original input image. Cropping is one of the most common image operations performed to remove unwanted portions of an image as well as to add needed features to an image. Welcome to a tutorial series, covering OpenCV, which is an image and video processing library with bindings in C++, C, Python, and Java. Welcome to another OpenCV with Python tutorial, in this tutorial we are going to be covering some simple arithmetic operations that we can perform on images, along with explaining what they do. Image filtering. AJ94 ( 2017-01-09 01:26:38 -0500 ) edit Hmmm you can also take a look at the work of Michal Irani, she has worked on edge preservation and noise/blurring removal to sharpen up images and get the edges back stronger and undeformed. Image segmentation is the task of classifying every pixel in the image to some class. Image processing in Python. Here are some images to give you an idea of what the image sharpening process looks like:. On opencv we have two ways to subtract the background: A manual way which consists on taking the first frame and from that one subtricting each time the following frames from the first one. The size must be positive and odd. segmentation, representation). In addition this article extends conventional Image Erosion and Dilation implementations through partial colour variations of Image Erosion and Dilation. To sharpen single images, you need to somehow add constraints (assumptions) on what kind of image it is you want, and how it has become blurred. Creating Image. 1 range # This is why we multiply the image with 255 before saving cv2. process(hdr) # Tonemap operators create floating point images with values in the 0. Have you ever come across a noisy image? I mean an image that was not that clear when viewing it? I think we do come across such images very often, especially when many images nowadays are taken by. This means we usually select one image to the right or left of the correct image (which is still happily accepted as the correct answer by MintEye). durand = cv2. There are many techniques for image enhancement, but I will be covering two techniques in this tutorial: image inverse and power law transformation. import cv2 # 开源的计算机视觉库 import numpy # 提供科学计算相关的功能 import time # 提供时间相关的功能 import threading # 提供多线程相关的功能 from lib. Image enhancement is usually used as a preprocessing step in the fundamental steps involved in digital image processing (i. Re: How do I sharpen a TIFF image in AutoCAD 2016? Hi sm2016, When you view something on your screen the resolution required to make it look good is considerably less than that for what your printer can produce. Lunapics Image software free image, art & animated Gif creator. The main package of skimage only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages:. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. We're going to look into two commonly used edge detection schemes - the gradient (Sobel - first order. The Laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection (see zero crossing edge. namedWindow("input", cv. This means we usually select one image to the right or left of the correct image (which is still happily accepted as the correct answer by MintEye). This is where Python and the Python Imaging Library (or PIL) come in, allowing you to write scripts that process images in batch. We’ll have a look at how we can. Let me show you the process by using this image which have sharp edge. There is scope for automation, but for this exercise, the location of eye was manually engineered to “work”. After that, the image manipulation used, in which the resizing, cropping, blurring and sharpening of the images done if needed. This seems to be because the undistorted image is somewhere sharper than the distorted images and hence has sharper gradients resulting in larger derivative values. Our Vision Guide product is well known in the industry for providing a powerful yet easy to use point and click interface for developing robot vision applications. This opinionated guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a. OpenCV-Python. imwrite('durand_image. Optional: Building a CLI Tool for Your New OCR Engine. There is scope for automation, but for this exercise, the location of eye was manually engineered to “work”. skimage) is a collection of algorithms for image processing and computer vision. Applying the sharpening filter will sharpen the edges in the image. Many ways to sharpen images in. Filtering is often used for : image enhancement (denoise. exposure import rescale_intensity import numpy as np import argparse import cv2 def convolve (image, kernel): # grab the spatial dimensions of the image, along with # the spatial dimensions of the kernel (iH, iW) = image. Laplacian/Laplacian of Gaussian. # Load image as grayscale image = cv2. createTonemapDurand(gamma=2. Therefore, image denoising plays an important role in a wide range of applications such as image restoration, visual tracking, image registration, and image segmentation. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Some of the problems are from the exercises from this book (available on amazon). Jar Red Line's CV-2 Synthetic Grease is designed to withstand the extreme temperatures and pressures which occur in high-performance wheel bearings and CV-joints. Let me show you the process by using this image which have sharp edge. jpg', gray) There are more Python-implemented examples of OpenCV in this collection of tutorials. To Create a 3 channel image of 400x200, you can use the following code. It will wait for a key press before closing the image. When you are satisfied with the result you can close all the extraneous windows. To sharpen single images, you need to somehow add constraints (assumptions) on what kind of image it is you want, and how it has become blurred. imwrite ('graytest. In simpler terms Image Dilation can be defined by this quote: Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion. Every image is made up of pixels and when these values are extracted using python, four values are obtained for each pixel (R,G,B,A). # "Sharpen" an image by multiplying every pixel by 2, and then subtracting # the average value of the neighborhood from it. Editing images with cv2 library in python. output array or dtype, optional. addWeighted(). The zoom factor along the axes. Someone told me of a technique called "image sharpening" that may be the answer to my prayers. This seems to be because the undistorted image is somewhere sharper than the distorted images and hence has sharper gradients resulting in larger derivative values. Learn how to process images using the Pillow Python library. This article will focus on Pillow, a library that is powerful, provides a wide array of image processing features, and is simple to use. imread ('images/plane_256x256. Image processing with convolutions in Python. image = cv2. Hello, and welcome to the “Image processing with Python & Open-CV tutorial series”. We will be looking at arithmetic operations, and filters (blurring, and sharpening). If you feel your image needs more/less sharpening, then go back to the LAB window, undo twice and redo the unsharp mask step above. Photos taken with a 28 mm lens (left) and with a 70 mm lens (right) show change in angle of view (or field of view). ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples) # execute 0 to 5 of the following (less important) augmenters per image # don't execute all of them, as that would often be way too strong. Sharpening Applying the sharpening filter will sharpen the edges in the image. The Laplacian is a 2-D isotropic measure of the 2nd spatial derivative of an image. Learn to convert images to binary images using global thresholding, Adaptive thresholding, Otsu’s binarization etc Smoothing Images Learn to blur the images, filter the images with custom kernels etc. Try this on both color and grayscale images. imwrite ('graytest. Image Convolutions is a simpler method to do convolutions on images. And they have a lot of uses too. sharpen Sharpen the thumbnail image (using the PIL sharpen filter) detail Add detail to the image, like a mild *sharpen* (using the PIL ``detail`` filter). So of course, OpenCV does have a way to do image convolutions easily and super efficiently! OpenCV's Convolution Function The C++ convolution function. imshow("input", src) # sharpen_op. filter2D() to convolve a kernel with an image. The watershed algorithm is a classic image segmentation algorithm. This seems to be because the undistorted image is somewhere sharper than the distorted images and hence has sharper gradients resulting in larger derivative values. There is scope for automation, but for this exercise, the location of eye was manually engineered to "work". png', ldr * 255) Using Python, you can also create your own operators if you need more control over the process. Supports float radius of arbitrary size. The result of the Sobel-Feldman operator is a 2-dimensional map of the gradient at each point. This gives a sharpening effect to the image. The quality of an image cannot be increased 100%. An important thing to mention is that Microsoft's Cimage class stores color bits differently than the usual bitmap. minDist – Minimum distance between the centers of the detected circles. cli import FullPaths, argparse. Hi everyone, today we are going to do a short tutorial on unsharp masking with Python and OpenCV. We will be looking at arithmetic operations, and filters (blurring, and sharpening). exposure import rescale_intensity import numpy as np import argparse import cv2 def convolve (image, kernel): # grab the spatial dimensions of the image, along with # the spatial dimensions of the kernel (iH, iW) = image. It can be accessed in Adobe Photoshop by clicking on the following drop-down menus: Filter > Sharpen > Unsharp Mask. Tip: When you point your mouse at a thumbnail image in the gallery, the picture on your slide changes to give you a preview of the effect of the option you're pointing at. We then multiply each value in the kernel matrix with the corresponding values in the image, and then sum it up. Should be odd. Five snippets to help get your ideas and creative juices flowing. Limiting it to a FOV of 140 means we wouldn't need 1080 or 2080 level of hardware even at 4k/4k per eye (less things need to be drawn) and FOV/render will keep it into that less than 20-30% of the view in full detail (witch will have a sharper image due to the fact the PPD would be much much higher - double - that of the rift). This opinionated guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a. IMREAD_COLOR) The flags option is used to control how the image is read. Approaches to do sharpening hold these statistics explicitly or implicitly in their algorithms (deep learning being the most implicitly coded ones). imwrite('durand_image. 7, OpenCV 2. Output image location. B = imgaussfilt(A) filters image A with a 2-D Gaussian smoothing kernel with standard deviation of 0. jpg', gray) There are more Python-implemented examples of OpenCV in this collection of tutorials. Image Erosion and Dilation. Python Newb Code Snippets #17. This helpful in enhancing quality of the image (e. Uses an unsharp mask to sharpen input image. Photo, scketch and paint effects. Say we are smoothing this image (we can see noise in the image), and now we are dealing with the pixel at middle of the blue rect. 3*edge_image). 2D Convolution ( Image Filtering ) As in one-dimensional signals, images also can be filtered with various low-pass filters(LPF), high-pass filters(HPF) etc. Results ( detailEnhance ) The image below shows the result detail enhancing filter. Proceedings of The Seventh International Conference on Informatics and Applications (ICIA2018), Japan, 2018 Face Detection and Face Recognition in Python Programming Language Primož Podržaj Boris Kuster Faculty of Mechanical Engineering, University of Ljubljana Aškerčeva 6, 1000 Ljubljana, Slovenia primoz. In this article, you already known about type of filters and how to apply them to the images. Now that our image is grayscaled and the size is consistent, let's perform a morphological operation: # apply a tophat (whitehat) morphological operator to find light # regions against a dark background (i. Convolution. Have you ever come across a noisy image? I mean an image that was not that clear when viewing it? I think we do come across such images very often, especially when many images nowadays are taken by. Contrast Free Online Photo Editor. Image sharpening¶. It helps us reduce the amount of data (pixels) to process and maintains the structural aspect of the image. If necessary, you can adjust advanced settings to further sharpen the image. Editing images with cv2 library in python. segmentation, representation). Computer Vision: Computer Vision is the field which deals with the techniques to extract high level information from digital. Our Vision Guide product is well known in the industry for providing a powerful yet easy to use point and click interface for developing robot vision applications. # Load image as grayscale image = cv2. I encourage you to google them , there are lots and lots of examples and code snippets. We hope that you can use these functions and pipelines as a starting place for your project. Pillow is a fork of the. Let me show you the process by using this image which have sharp edge. Turns out that,the image was of very low resolution,hence the distortion in the edges. For Tumblr, Facebook, Chromebook or Your WebSite. Blurring, Sharpening, Greyscaling, and other uses of image kernels. We thus need a way to improve the quality of output images so they can be visually more expressive for the viewer, and this is where image enhancement comes into play. Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion. 15) The parameters are the same as the Edge Enhancing Filter. OK, I Understand. In Low pass filters process blocks the high-frequency pixel and allows low-intensity values or frequency to pass through it. Lunapics Image software free image, art & animated Gif creator. OpenCV is used for all sorts of image and video analysis. An image can be compared to a function giving an intensity at each point. This article will focus on Pillow, a library that is powerful, provides a wide array of image processing features, and is simple to use. Parameters input array_like. While I'm not looking to classify the images that are pan-sharpened, I am looking to use them for comparison and display purposes. If the image contains noise, this method will not produce satisfactory results, like most of the other sharpening filters. Image enhancement is usually used as a preprocessing step in the fundamental steps involved in digital image processing (i. Photo, scketch and paint effects. imread('Top-bike-wallpaper. If necessary, you can adjust advanced settings to further sharpen the image. You can use these parameters to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in the scene. Proceedings of The Seventh International Conference on Informatics and Applications (ICIA2018), Japan, 2018 Face Detection and Face Recognition in Python Programming Language Primož Podržaj Boris Kuster Faculty of Mechanical Engineering, University of Ljubljana Aškerčeva 6, 1000 Ljubljana, Slovenia primoz. And they have a lot of uses too. There are many techniques for image enhancement, but I will be covering two techniques in this tutorial: image inverse and power law transformation. imwrite('durand_image. Different types of Colour Bands and the role of Colour Bands in forming an image. Image Convolutions is a simpler method to do convolutions on images. As an example, we will try an averaging filter on an image. If a sequence, zoom should contain one value for each axis. shape[: 2] (kH, kW) = kernel. Turns out that,the image was of very low resolution,hence the distortion in the edges. axis int, optional. I tried two methods to get an binary image (1 = background, 0 = edges), to do an Hough Transforma. Common Names: Dilate, Grow, Expand Brief Description. addWeighted. How to Sharpen Rift Visuals with Supersampling Using Oculus Debug Tool a way to significantly improve image quality during your time in VR and your chosen game doesn't include any options. It may help to unminimize the original image for comparison. Editing images with cv2 library in python. Roughly speaking, a demo is made up of an initialization script. Image segmentation is the task of classifying every pixel in the image to some class. On opencv we have two ways to subtract the background: A manual way which consists on taking the first frame and from that one subtricting each time the following frames from the first one. The size must be positive and odd. Object and Face Detection and live object Tracking using python and OpenCV. Should be odd. ksize – Aperture size used to compute the second-derivative filters. Furthermore, certain operations on images, like color space conversions, brightness/contrast adjustments, sharpening, complex interpolation (bi-cubic, Lanczos) can produce val-ues out of the available range. The Gaussian filter works better for the Zorro video. Roughly speaking, a demo is made up of an initialization script. Do you want to sharpen your photos? By applying sharpening effects, you can remove fuzziness from your photos. Many ways to sharpen images in. Image Enhancement: To extract the useful information from an image the image is so adjusted to make the display better. ddepth – Desired depth of the destination image. IMREAD_COLOR : Loads a color image. This is where Python and the Python Imaging Library (or PIL) come in, allowing you to write scripts that process images in batch. 03/22/2018; 8 minutes to read +2; In this article. The purpose of this series is to do an overview of the Python libraries that may be useful for GeeXLab demos AND that work with GeeXLab. It is available free of charge and free of restriction. Raw integer images can have more bit depth for processing, but must be reduced to 8 bit (values 0…255) to be displayed. jpg', gray) There are more Python-implemented examples of OpenCV in this collection of tutorials. Uses an unsharp mask to sharpen input image. minDist - Minimum distance between the centers of the detected circles. We thus need a way to improve the quality of output images so they can be visually more expressive for the viewer, and this is where image enhancement comes into play. The image should be in the working directory or a full path of image should be given. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an. Tutorial: NIR Image Workflow¶. Common Names: Dilate, Grow, Expand Brief Description. I wanted to extract each pixel values so that i can use them for locating simple objects in an image. Input Image Eroded image Notice the change in eyes, illuminates spots in the eyes are removed because in the input image there is a stark change in illumination at points. from skimage. I'm attempting to pan sharpen four band images in Python with a higher resolution panchromatic band image. pdf # # Jay Summet 2015 # #Python 2. Contrast Free Online Photo Editor. About the presenter: A 17 year old from Rio de Janeiro, Brazil, Fontes shoots. The quality of an image cannot be increased 100%. An image cannot be resized or rescaled inplace in OpenCV. This opinionated guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a. We use cv2. output array or dtype, optional. Image segmentation is important for extracting the relevant parts from an image. Restoration of defocused and blurred images by Vladimir Yuzhikov Restoration of distorted images is one of the most interesting and important problems of image processing - from the theoretical, as well as from the practical point of view. Say we are smoothing this image (we can see noise in the image), and now we are dealing with the pixel at middle of the blue rect. 7*old_image + 0. GaussianBlur and skimage. There is scope for automation, but for this exercise, the location of eye was manually engineered to “work”. There are many techniques for image enhancement, but I will be covering two techniques in this tutorial: image inverse and power law transformation. This example shows how to sharpen an image in noiseless situation by applying the filter inverse to the blur. Every image is made up of pixels and when these values are extracted using python, four values are obtained for each pixel (R,G,B,A). Jar Red Line's CV-2 Synthetic Grease is designed to withstand the extreme temperatures and pressures which occur in high-performance wheel bearings and CV-joints. This gives a sharpening effect to the image. Photo, scketch and paint effects. Article purpose The purpose of this article is aimed at exploring the concepts of Image Erosion, Image Dilation, Open Morphology and Closed Morphology. The main package of skimage only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages:. GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together. GeeXLab has a very simple way of working. We hope that you can use these functions and pipelines as a starting place for your project. segmentation, representation). I read a brilliant work by Aditya Prakash – OpenCV C++ Program to blur an image, so I decided to come up with something similar but this time in Python. As this process is same of convolution so filter masks are also known as convolution masks. 15) The parameters are the same as the Edge Enhancing Filter. Implement this and try it on some sample images. Turns out that,the image was of very low resolution,hence the distortion in the edges. pdf # # Jay Summet 2015 # #Python 2. Edge Detection is an image processing technique to find boundaries of objects in the image. bilateralFilter(image, 9, 75, 75) The bilateral filter leaves a more grainy/pixely result especially in his face even with sharpening and denoising applied. So, here is a very simple program with basically the same result. morphologyEx(gray, cv2. In case of photo editing, I would suggest you to use Adobe Photoshop which is currently the best photo editing. It include various process such as noise removal, histogram equalisation, and image sharpening etc. Photo, scketch and paint effects. In simpler terms Image Dilation can be defined by this quote: Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion. We hope that you can use these functions and pipelines as a starting place for your project. See getDerivKernels() for details. Each kernel is useful for a spesific task, such as sharpening, blurring, edge detection, and more. In this tutorial, we shall learn to find edges of focused objects in an image using Canny Edge Detection Technique. We're going to look into two commonly used edge detection schemes - the gradient (Sobel - first order. Resizing or rescaling a Mat is somewhat easier than dealing with a IplImage. When you are satisfied with the result you can close all the extraneous windows. How to Sharpen Rift Visuals with Supersampling Using Oculus Debug Tool a way to significantly improve image quality during your time in VR and your chosen game doesn't include any options. Some of the problems are from the exercises from this book (available on amazon). gaussian_filter libraries, but I get significantly different results. The quality of an image cannot be increased 100%. imread() to read an image. Addition and…. It is the default flag. Blurring, Sharpening, Greyscaling, and other uses of image kernels. We then loop over the images in our directory on Line 26, load the image from disk on Line 28, convert the image to grayscale on Line 29, and apply a Gaussian blur with a 3 x 3 kernel to help remove high frequency noise on Line 30. Let me show you the process by using this image which have sharp edge. imread() function to read an image. A HPF filters helps in finding edges in an image. This filter is very useful when we want to enhance the edges in an image that's not - Selection from OpenCV with Python By Example [Book]. Contrast stretching of an image modifies the pixel values of the image in such a way that the intensities are transformed into a bigger range. Tutorial: NIR Image Workflow¶. 7*old_image + 0. PlantCV is composed of modular functions that can be arranged (or rearranged) and adjusted quickly and easily. For example, if dp=1, the accumulator has the same resolution as the input image. Jar Red Line's CV-2 Synthetic Grease is designed to withstand the extreme temperatures and pressures which occur in high-performance wheel bearings and CV-joints. See the white patch on the left side of the apple. The following images illustrate this, by showing the computation of the Sobel-Feldman operator on a simple image. Lunapics Image software free image, art & animated Gif creator. The Laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection (see zero crossing edge. Creating Image. The quality of an image cannot be increased 100%. Syntax of cv2 gaussianblur function. convolutional. Load Image As Greyscale. Check them out!. IMREAD_COLOR : Loads a color image. I'm curious as to why, and what can be done to make skimage look more like cv2. There are many techniques for image enhancement, but I will be covering two techniques in this tutorial: image inverse and power law transformation. About the presenter: A 17 year old from Rio de Janeiro, Brazil, Fontes shoots. Hello, and welcome to the “Image processing with Python & Open-CV tutorial series”. This opinionated guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a. As this process is same of convolution so filter masks are also known as convolution masks. 7*old_image + 0. We will be looking at arithmetic operations, and filters (blurring, and sharpening). I read a brilliant work by Aditya Prakash – OpenCV C++ Program to blur an image, so I decided to come up with something similar but this time in Python. You will need to create another image with the new size or scale and apply a resize operation. The PNG2GIF converter I wrote back in August is just one example of batch image processing using Python and PIL. This helpful in enhancing quality of the image (e. Tutorial: NIR Image Pipeline¶. GaussianBlur and skimage. scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. Pipe your binary input in and get the sharpened image as binary output. B = imgaussfilt(A) filters image A with a 2-D Gaussian smoothing kernel with standard deviation of 0. Sharpen Free Online Photo Editor. The next time, i will utilize the filter to detect edges and sharpen the image. NumPy can be downloaded from the Python Package Index(PyPI): $. We thus need a way to improve the quality of output images so they can be visually more expressive for the viewer, and this is where image enhancement comes into play. Clearly, this produces a sharper image than the above 2 methods. This filter is very useful when we want to enhance the edges in an image that's not - Selection from OpenCV with Python By Example [Book]. QUESTION: I'd like to see more fine detail in my image. Contrast Free Online Photo Editor. IMREAD_COLOR) The flags option is used to control how the image is read. The image should be in the working directory or a full path of image should be given. sobel¶ scipy. In other words, blurring is a lossy operation, and going back from it is in general not possible. Using Topaz DeNoise to sharpen an image? Now that's something new to us. And they have a lot of uses too. Images from imgToDenoiseIndex-temporalWindowSize / 2 to imgToDenoiseIndex-temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. Therefore, image denoising plays an important role in a wide range of applications such as image restoration, visual tracking, image registration, and image segmentation. A LPF helps in removing noise, or blurring the image. It is typically applied to binary images, but there are versions that work on grayscale images. I have tried anisotropic diffusion filter, bilateral filter also to remove noise but i dont achieve the result as that of neat image. addWeighted(). •We can think of a (grayscale) image as a function, f, from R2 to R (or a 2D signal): -f (x,y) gives the intensity at position (x,y) -A digital image is a discrete (sampled, quantized) version of this function What is an image? x y f (x, y) snoop 3D view. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: