Negative image filter

6,992 image memes with text descriptions, along with their sentiment labels. It’s pretty easy to import a Kaggle dataset directly into Google Colab, so I recommend you do that.We downloaded our dataset into a folder called memes-dataset and the directory structure of the dataset looks like this:The nested images folder contains the images of the memes, and the labels.csv file contains the image name, meme text, and the corresponding sentiment labels.The following script imports the labels.csv file in the pandas dataframe and displays the dataframe header.dataset = pd.read_csv("/content/memes-dataset/labels.csv")dataset.head()Output:You can see that the dataframe has four relevant columns: image_name, text_ocr, text_corrected, and overall_sentiment.The image_name column contains the names of the images; the text_ocr column contains the text extracted via an OCR (optical character recognizer.); the text_corrected column contains the manually corrected text of the memes, and the overall_sentiment column contains the sentiment labels.We will create a new column, image_path, in our dataset, which contains the complete path to the image file for each record in the dataset.We will also filter out any rows that have missing or empty values in the text_corrected column.Finally, we will filter the text_corrected, image_path, and overall_sentiment, columns and remove the remaining columns from our dataset.The following script performs the above tasks.image_folder_path = '/content/memes-dataset/images/images'dataset['image_path'] = dataset['image_name'].apply(lambda x: os.path.join(image_folder_path, x))dataset = dataset[dataset['text_corrected'].notna() & (dataset['text_corrected'] != '')]dataset = dataset.filter(["text_corrected", "image_path", "overall_sentiment"])print("==============================================")print(f'The shape of the dataset is: {dataset.shape}')print("==============================================")print(f'The number of sentiments in each category is:\n{dataset.overall_sentiment.value_counts()}')print("==============================================")dataset.head(10)Output:You can see that we have five categories of sentiments: very_positive, positive, neutral, negative, and very_negative. The dataset is highly imbalanced across these categories.Let’s print a sample meme along with its text and sentiment label.index = 523image_path = dataset["image_path"].iloc[index]sample_image = Image.open(image_path)sample_text = dataset["text_corrected"].iloc[index]sentiment = dataset["overall_sentiment"].iloc[index]print(sample_text)print(sentiment)sample_imageOutput:The next preprocessing step is to convert the sentiment labels into numeric values that our model can use. We will use the following mapping: very_positive -> 2 positive -> 2 neutral -> 1 negative -> 0 very_negative -> 0The above mapping will also reduce the number of classes from five to three by merging the very positive and positive classes and the very negative and negative classes. This mapping may make the task easier for our model, but it may also lose some information about the intensity of the sentiments. You can keep all five classes if you want.The following script applies the sentiment mapping to the overall_sentiment column. In the output, you can see the number of records for each sentiment

Determined by comparing the sentiment events found in the first half of the interaction to the sentiment found in the second half of the interaction. For this reason, the sentiment trend may be updated when additional follow-ups occur within the same interaction. Only the customer phrases of the transcript are analyzed to detect sentiment events. The agent phrases of the transcript are ignored in the sentiment trend calculation. There is a minimum number of customer phrases required for the sentiment trend to be calculated, usually around 6 or more customer phrases are required. For more information, see Sentiment analysis – What is the customer sentiment trend?. Sentiment trend values – There are 5 sentiment trend values. Events panel – Contains three lists (topics, positive and negative) of all the detected topic and sentiment markers together with their corresponding phrase. From the Events panel (located on the right side of the Transcript tab), you can click the preferred Event type to filter the lists to only display topics, positive sentiment markers, or negative sentiment markers. Click the word Events to select/deselect all of them. In addition, from the Events panel you can hover over the positive or negative sentiment marker and phrase to view a tooltip with the sentiment phrase.Note: Sentiment markers, the overall interaction sentiment, and the interaction sentiment trend are updated when new segments of the same interaction are retrieved by the system or when new phrases are added to the Sentiment feedback page. For more information, see Work with sentiment analysis.In the image below you can see the sentiment markers in the transcript, Events panel, and in the interaction overview waveform above the transcript. Click the image to enlarge.

Operator: Use the site operator to search for images within a specific website or domain. For example, site:www.unsplash.com will retrieve images from Unsplash.com.Filetype operator: Use the filetype operator to search for images with a specific file type, such as filetype:jpg or filetype:png.Size operator: Use the size operator to search for images of a specific size. For example, size:large or size:extra large will retrieve high-resolution images.Using Keywords and DescriptionsIn addition to using search operators, using relevant keywords and descriptions can also help you find high-resolution images on Google. Here are some tips:Use specific keywords: Use specific keywords related to your search query to retrieve more relevant results. For example, if you’re searching for high-resolution images of a specific product, use keywords like product name, brand name, or product features.Use quotes: Use quotes to search for exact phrases. For example, "product name" will retrieve images that contain the exact phrase "product name".Use negative keywords: Use negative keywords to exclude irrelevant results from your search. For example, -logo will exclude images that contain the word "logo".Using Google’s Search FeaturesGoogle’s search features can also be used to find high-resolution images. Here are some features you can use:Image labels: Use the image labels feature to search for images with specific labels. For example, label:landscape will retrieve images that are labeled as landscapes.Image filters: Use the image filters feature to filter your search results by image type, color, and more.Google’s Visual Search: Use Google’s Visual Search feature to search for images by uploading an image or searching with a specific query.Additional Tips and TricksHere are some additional tips and tricks to help you find high-resolution images on Google:Use a reputable image search engine: While Google is a popular search engine, it’s not the only one. Use reputable image search engines like Bing, Flickr, or 500px to find high-resolution images.Check the image source: Always check the source of the image to ensure it’s royalty-free and high-resolution.Use image editing software: Use image editing software like Adobe Photoshop or GIMP to edit and enhance your high-resolution images.ConclusionFinding high-resolution images on Google can be a challenging task, but by using

You would process VueScan's output in an image adjustment program to make it look good; in particular, playing with curves will have a big effect on B&W tonality.Don't use Apple RGB as the output color space. Adobe RGB is a good, conservative choice.The screenshots show that your scanner supports multi-exposure. This is a really useful feature for extracting more detail from the dense parts of the negative; it's kind of like HDR for film scanners, scanning the image twice, first with normal exposure and second time overxposed. However, this isn't useful for all film frames. I judge whether to use it by looking at the raw histogram of the preview scan; if it's hitting the left edge hard then I do multi-exposure.VueScan has grain reduction in the Filter tab (you don't give us a screenshot of that). You may want to try it. I usually don't use it; if I wanted no grain I'd go for digital.Some of my B&W negative scans from VueScan, processed in Lightroom after scanning. The second two were shot at ISO 1600, so grain is no surprise: "}"> OP (unknown member) • New Member • Posts: 2 Re: Some thoughts In reply to sacundim • Jul 24, 2011 Yup, I have black and white slides! No idea how they made though, as they're not my own. I will be scanning color slides as well, though. Everything I have are positives, not negatives, if that makes any difference.I did try scanning using the 16bit greyscale setting,

Relative horizontal position. if (shape.getRelativeHorizontalPosition() == RelativeHorizontalPosition.DEFAULT) { // Setting the position binding to RightMargin. shape.setRelativeHorizontalPosition(RelativeHorizontalPosition.RIGHT_MARGIN); // The position relative value can be negative. shape.setLeftRelative(-260); } doc.save(getArtifactsDir() + "Shape.RelativeSizeAndPosition.docx"); Parameters:ParameterTypeDescriptionvaluefloatThe value that represents the percentage of shape’s relative width.setWrapSide(int value)public void setWrapSide(int value)Specifies how the text is wrapped around the shape.Remarks:The default value is WrapSide.BOTH.Has effect only for top level shapes.Examples:Shows how to replace all textbox shapes with image shapes. Document doc = new Document(getMyDir() + "Textboxes in drawing canvas.docx"); List shapeList = Arrays.stream(doc.getChildNodes(NodeType.SHAPE, true).toArray()) .filter(Shape.class::isInstance) .map(Shape.class::cast) .collect(Collectors.toList()); Assert.assertEquals(3, IterableUtils.countMatches(shapeList, s -> s.getShapeType() == ShapeType.TEXT_BOX)); Assert.assertEquals(1, IterableUtils.countMatches(shapeList, s -> s.getShapeType() == ShapeType.IMAGE)); for (Shape shape : shapeList) { if (((shape.getShapeType()) == (ShapeType.TEXT_BOX))) { Shape replacementShape = new Shape(doc, ShapeType.IMAGE); replacementShape.getImageData().setImage(getImageDir() + "Logo.jpg"); replacementShape.setLeft(shape.getLeft()); replacementShape.setTop(shape.getTop()); replacementShape.setWidth(shape.getWidth()); replacementShape.setHeight(shape.getHeight()); replacementShape.setRelativeHorizontalPosition(shape.getRelativeHorizontalPosition()); replacementShape.setRelativeVerticalPosition(shape.getRelativeVerticalPosition()); replacementShape.setHorizontalAlignment(shape.getHorizontalAlignment()); replacementShape.setVerticalAlignment(shape.getVerticalAlignment()); replacementShape.setWrapType(shape.getWrapType()); replacementShape.setWrapSide(shape.getWrapSide()); shape.getParentNode().insertAfter(replacementShape, shape); shape.remove(); } } shapeList = Arrays.stream(doc.getChildNodes(NodeType.SHAPE, true).toArray()) .filter(Shape.class::isInstance) .map(Shape.class::cast) .collect(Collectors.toList()); Assert.assertEquals(0, IterableUtils.countMatches(shapeList, s -> s.getShapeType() == ShapeType.TEXT_BOX)); Assert.assertEquals(4, IterableUtils.countMatches(shapeList, s -> s.getShapeType() == ShapeType.IMAGE)); doc.save(getArtifactsDir() + "Shape.ReplaceTextboxesWithImages.docx"); Parameters:ParameterTypeDescriptionvalueintThe corresponding int value. The value must be one of WrapSide constants.setWrapType(int value)public void setWrapType(int value)Defines whether the shape is inline or floating. For floating shapes defines the wrapping mode for text around the shape.Remarks:The default value is WrapType.NONE.Has effect only for top level shapes.Examples:Shows how to insert a floating image to the center of a page. Document doc = new Document(); DocumentBuilder builder = new DocumentBuilder(doc); // Insert a floating image that will appear behind the overlapping text and align it to the page's center. Shape shape =