![]() ![]() Professional photo colorizing ability - Machine Learning had been trained by over Millions of photos, both people and landscapes in different eras Easy and simple use - with just 1 click, photos will be converted into colorful Edit old photos - Crop, rotate your photos before using our AI technology ![]() Photo scanner - Scan your old photos and digitize them by taking a photo. At the same time, the system will automatically enhance the picture, the processed picture will be clearer, and the memory seems to return to the distant past. Use the powerful AI photo colorize function to turn old black and white photos into vibrant and colorful photos in just 1 click. 100% automatic and based on the advanced Automatic Machine Learning.Įxcept for Picture Colorizer, this app also supports more functions, like Old Photo Enhancer - which can enhance the quality of photos and turn blurred images into clear, Old Portrait Retouch - which will enhance facial contour and bring stereoscopic effect. Returns the Axes object with the plot drawn onto it.Colorize! is a photo colorization app used to colorize your old and black and white photos. Other keyword arguments are passed through to ax matplotlib Axes, optionalĪxes object to draw the plot onto, otherwise uses the current Axes. When hue nesting is used, whether elements should be shifted along theĬategorical axis. Often look better with slightly desaturated colors, but set this toġ if you want the plot colors to perfectly match the input color Proportion of the original saturation to draw colors at. Shouldīe something that can be interpreted by color_palette(), or aĭictionary mapping hue levels to matplotlib colors. palette palette name, list, or dictĬolors to use for the different levels of the hue variable. color matplotlib color, optionalĬolor for all of the elements, or seed for a gradient palette. To resolve ambiguity when both x and y are numeric or when Inferred based on the type of the input variables, but it can be used Orientation of the plot (vertical or horizontal). Order to plot the categorical levels in, otherwise the levels are ![]() order, hue_order lists of strings, optional Otherwise it is expected to be long-form. data DataFrame, array, or list of arrays, optionalĭataset for plotting. Parameters x, y, hue names of variables in data or vector data, optional When the data has a numeric or date type. This function always treats one of the variables as categorical andĭraws data at ordinal positions (0, 1, … n) on the relevant axis, even Grouping variables to control the order of plot elements. Additionally, you can use Categorical types for the Objects are preferable because the associated names will be used toĪnnotate the axes. In most cases, it is possible to use numpy or Python objects, but pandas Variables will determine how the data are plotted.Ī “wide-form” DataFrame, such that each numeric column will be plotted. Objects passed directly to the x, y, and/or hue parameters.Ī “long-form” DataFrame, in which case the x, y, and hue #Automatic picture colorizer series#Vectors of data represented as lists, numpy arrays, or pandas Series Input data can be passed in a variety of formats, including: The basic API and options are identical to thoseįor barplot(), so you can compare counts across nested variables. Show the counts of observations in each categorical bin using bars.Ī count plot can be thought of as a histogram across a categorical, instead countplot ( *, x = None, y = None, hue = None, data = None, order = None, hue_order = None, orient = None, color = None, palette = None, saturation = 0.75, dodge = True, ax = None, ** kwargs ) ¶ ![]()
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