## 5 EBCA Testbed  
This section describes a testbed which was released under an open-source license in order to make it possible for other researches to develop new and better algorithms using considered camera arrays. The application is available at ([[https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)]([https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)) accessed on 9 August 2021). The testbed was released as a part of work on this paper. The testbed is suitable for evaluating a usage of all camera sets consisting of a central camera and up to four side cameras equally distant from a central one. In particular, algorithms designed for trinocular vision systems can be evaluated. The testbed can be used for both testing an array consisting of three cameras placed in a row and an L-shape trinocular vision system [29; 31]. Moreover, test data presented in this paper include images necessary for testing arrays with four and five cameras. There are currently no openly available testbeds which makes this possible. The testbed consists of six data sets and the software necessary for running experiments. Data sets used in the testbed contain images of plants. There are two data sets for every considered species including strawberries (sets marked with \(ST_{1}\) and \(ST_{2}\)), redcurants (\(CU_{1}\) and \(CU_{2}\)) and cherries (\(CH_{1}\) and \(CH_{2}\)). Every data set was created on the basis of five images taken by EBCA. Figure 5 presents a sample data set which is \(ST_{1}\) containing images of a strawberry plant. In the testbed calibration, the methods described in Sections 2.4 and 4 were used. Calibration was based on taking images of an exemplary image pattern which was a chessboard with \(10\times 7\) intersections of black and white fields. Each field has a side equal to 24 mm. The set of images used for calibrating cameras consisted of 10 series of five chessboard images taken by EBCA from different angles. The OpenCV library was used for analyzing images and calculating transformations matrices used for refining images. The process of preparing images for a testbed also consisted of cutting out parts of images taken by EBCA. Cameras made images with the resolution of \(1280\times 720\). The entire content of images was not used. Experiments were performed on parts of these images containing leaves and at least one fruit. In every data set, the selected parts of images were those which were located at the same coordinates in side images and a central one. The sizes of image parts which were used for making test sets are presented in Table 1.  
The content of a central image can be distinguished between a matching area (\(MA\)) and a margin (\(MR\)). \(MA\) is a part of an image for which a disparity map is acquired. \(MR\) is the area placed around \(MA\). \(MR\) contains auxiliary data used for calculating disparities in \(MA\). Locations of objects in \(MA\) of the central image are shifted in side images with regard to positions of side cameras. Without a margin some objects located near borders of \(MA\)of a central image would not be visible from points of view of side cameras. Thus, both \(MA\) and \(MR\) need to be included in calculations in order to provide data for calculating disparities in \(MA\). The size of a margin needs to be greater than the maximum disparity that occurs in \(MA\). After cutting out significant parts of raw images and calibrating them, the resulting images can be then processed by a stereo matching algorithm in order to retrieve a disparity map. Maximum disparities in different data sets are presented in the last column of Table 1. In all cases the maximum disparity is lower than 100. In previous experiments described in previous papers sizes of margins were not equal to each other [2; 3]. Margins of images included in the testbed presented in this paper were unified and in every data set the margin covers all points located within the range of 100 points from borders of \(MA\).