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code_gen_config.json
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{
"path": "/home/g/code/freelancing/pyramid_measurement/",
"messages": [],
"name": "",
"run_command": null,
"test_command": null,
"languages": [],
"tracked_root": {
"name": "/",
"files": [
"t1cv.py",
"segmenting.py",
"calculate.py",
"segmenting_helpers.py",
"grad_like.py",
"hist_check.py",
"guicontrol.py",
"pyramid.py",
"analysis.py"
],
"folders": {}
},
"summaries": {
"analysis.py": [
"b99f87915eafb306282e3b5c0c9616ef",
"The python file `analysis.py` is a script used for analyzing images. It includes a class named `ImageAnalysis` that facilitates numerous image processing tasks including reading image files, grayscaling, cross value landscaping, segmentation, determining leg lengths of image features and rendering images in order to visualize the analysis results.\n\nRelevant fields and their usage:\n- `fullimg` : used to store the entire image read from the file\n- `img` : stores image apart from the title bar\n- `titlebar` : stores title bar of the image\n- `gray` : stores the grayscaled image\n- `thresh_value` : used for thresholding\n- `region_contexts` : used to hold the region contexts\n- `rotation` : rotation factor of the image\n- `cross_value_landscape_stored` : stores the result of cross value landscape calculation\n- `claimed_territory_stored` : stores the claimed territory region of the image after processing\n- `laplacian_stored` : stores the Laplacian transformation of the image\n- `window` : specifies the window for image display using OpenCV \n- `capture` : captures the image after processing\n- `pyramids` & `pyramids_by_id` : structures to hold detected pyramid-like shapes\n- `prominence` : variable used in peak detection algorithm\n\nThe file includes numerous methods for image analysis like `save_clipped`, `show`, `cross_value_landscape`, `cross_claimed_territory_inverse_value`, `reset_canvas`, `draw_cross`, `render_display`, `consolidate_pyramids`, `reindex_pyramids`, `segment`, `get_leg_lengths`, `draw_pyramids`, and `to_json` among others. The `main()` function at the end uses a number of these methods to perform a sample image analysis task."
],
"darken_edges.py": [
"04bcf41578d780e5290cbd85f252f18c",
"The Python file `darken_edges.py` appears to be a script meant for processing and manipulating images. The main field at the end of the script runs the script if it is run as a standalone file. Within this `main` field, the script loads a .tif file of an image, applies a variety of image processing functions and finally writes the result to different .png files.\n\nKey Functions:\n1. `generate_and_save_darkened`: This function loads an image, darkens the edge points of the image under different configurations of iterations and decay parameters, and saves the combined result into a .png file.\n2. `circle_add`: This function seems to create a series of circles of varying radii and intensity around given points on an image.\n3. `normalize`: This function normalizes an image between the minimum and maximum value of its pixel intensities.\n4. `main`: The main function that runs the series of image processing operations, including darkening edge points, applying a Gaussian blur, and adding circles to the image. It saves the final processed image.\n\nRelevant Exposed Fields:\n1. `darkened`: Output of `darken_edge_points` function and is the darkened image.\n2. `original`: The original image.\n3. `points`: The coordinates of points where the circles will be added.\n4. `circles`: Output of the `circle_add` function and represents the image with added circles.\n5. `images`: A list containing all the images obtained from different iterations of edge point darkening."
],
"t1cv.py": [
"f83dbad45d9145b2aaabffb3780f6298",
"The provided Python file, `t1cv.py`, is designed for image manipulation and contour detection. It is based on the OpenCV, NumPy, MatPlotLib, Scipy, and skimage libraries for image processing. \n\nExposed functions and their purposes are noted below: \n- `dirs_to_point`: Compute vectors from a point to a set of points.\n- `dirs_to_line_orth`: Computes orthogonal projections of points on a line given the line's start point and direction. \n- `scalars_to_line_orth`: Scales and adjusts points to an orthogonal line. \n- `dirs_to_point_with_line`: Computes projections of points onto a line given by a start point and a direction vector. \n- `scalars_to_with_line`: Similar to `dirs_to_point_with_line`, but not an exact match.\n- `arrow`: Draws an arrow on an image.\n- `line`: Draws a line on an image.\n- `show`: Displays the processed image. \n- `combine_thresh_masks`: Combines various threshold masks into a binary or gradient mask.\n- `get_regions_and_add_function`: Returns a dictionary of regions and a function to add regions.\n- `get_contour_masks`: Finds the mask for each contour and groups them by the sum of their pixel values.\n- `remove_bottom_bar`: Crops the image to remove the bottom bar (probably for preprocessing).\n- `partition_contours`: Organizes a dictionary of contours into high, low, and mid groups based on pixel sum.\n- `parse_hierarchy`: Converts OpenCV's contour hierarchy to a Python object-oriented format.\n- `Contour`: A class that represents a contour through an object-oriented Python class.\n- `label_mask`: Labels each region of the mask with a unique color.\n- `all_imagestrs`: A generator that yields all files within the current directory having a '.tif' extension.\n\nThe script also contains extra commented-out code (e.g., code to load an image file, thresholding operations, contour detection) which is not currently being used."
],
"grad_detect.py": [
"feba4fca78ff0943bc40e1ab9081062a",
"The Python file `grad_detect.py` defines classes and functions for detecting image gradients and estimating 'leg lengths'. These calculations are used in image analysis to extract features and details from the images. \n\nThe `ImageGradDetector` class has the following attributes and methods.\n\nAttributes:\n- `gs`: An instance of the 'ImageGradStats' class.\n- `IA`: Instance of 'ImageAnalysis' class.\n\nMethods:\n- `__init__`: Initializes an instance of 'ImageGradDetector'.\n- `est_leg_len`: Estimates the 'leg length' from the startpoint along the direction vector.\n- `est_many_legs`: Estimates 'leg lengths' for multiple points and direction vectors.\n\nThe file also includes several helper functions like `dirs_to_point`, `dirs_to_line_orth`, `scalars_to_line_orth`, `dirs_to_point_with_line`, and `scalars_to_with_line` for different vector calculations.\n\nThe `main` function runs an example of how to use the `ImageGradDetector` class and `est_leg_len` function to estimate 'leg lengths' from an image. \n\nImportantly, the script relies on other modules like `grad_like`, `cv2`, `t1cv`, and `analysis`. These modules must be available in the run-time environment for this script to run correctly."
],
"grad_detect_parallel.py": [
"2a30d8d0b51bc46108205866b7f1e24b",
"The Python file `grad_detect_parallel.py` contains code for image segmentation and estimation of leg length in a particular direction for pyramids with given center points. The file imports necessary packages and initializes a random number generator. \n\nKey functions in the file include `dirs_to_point`, `dirs_to_line_orth`, `scalars_to_line_orth`, `dirs_to_point_with_line`, `scalars_to_with_line`, `arrow`, `calculate_leglen` and various image manipulation methods inside the `ImageGradDetector` class.\n\nAmong the class methods, `est_leg_len_parallel` estimates leg length in a direction for pyramids with centers at specified start points. The `est_many_legs` function estimates leg lengths for multiple start points. The `test_curved_vortex_lanscape` and `segment_with_hill_rolling` methods perform image segmentation using a hill rolling algorithm. The `Labeler` class is used to update label states of the image for image segmentation purposes.\n\nBoth the `ImageGradDetector` class and the `Labeler` class provide methods for image analysis and manipulation, including leg length estimation and image segmentation.\n\nThe main function demonstrates the usage of these classes and methods. \n\nThis script file comes with additional utility functions to perform the necessary operations for managing the application, including managing image arrays, calculations, and visualizations."
],
"segmenting.py": [
"6f91bd21be4bbb4849a9cfc529957628",
"The Python file `segmenting.py` is related to image segmentation and processing. It defines a class `ImageSegmenter` which takes an ImageAnalysis object and exposes an important method est_leg_len_parallel. It applies a segmentation algorithm to the image analysis object that involves using Gradient Statistics and Sobel Filters to process images in multiple iterations. \n\nThe function `segment` is used to perform image segmentation on given ImageAnalysis object where it takes in the density as an optional parameter (default is set to 40). It further carries out edge detection and gradient calculations on the sampled image with adjusting thresholds and iteratively refining the results.\n\nThe `main()` function initializes an ImageAnalysis object with the path of an image file and uses the 'segment' function to perform image segmentation.\n\nImportant exposed fields include `gs` for gradient statistics, `IA` for image analysis, and `startpoints` for indicating the points from where the algorithm starts to calculate leg lengths in parallel. The `segment()` function is the primary entry point for performing image segmentation which takes in an `ImageAnalysis` object and density as parameters. \n\nOverall, the file is geared towards analyzing and performing operations on grayscale images, leaning heavily on open-source libraries such as NumPy and OpenCV for mathematical and image processing operations respectively."
],
"calculate.py": [
"868b6b862c65cb5504531689ca514aeb",
"The file `calculate.py` is a python script that contains functions for calculation and visualization of pyramid heights. The following functions are contained within the file:\n\n1. `pixels_to_um`: This function converts pixels to micrometers (um) using a proportion factor.\n\n2. `H`: This function calculates three different heights (H1, H2 and H3), based on the lengths of pyramid sides (L) and a sine factor. It returns a tuple with these calculated heights and their formulas.\n\n3. `height_calc`: This function calculates the height of the pyramids using the function `H` and keeps records of the calculations.\n\n4. `make_hist`: This function plots a histogram of pyramid heights, and saves image and text files containing histogram and calculations, if required.\n\n5. `height_calc_hist_and_save`: This function integrates the calculations of pyramid heights and their visualization in a histogram.\n\nRelevant fields exposed are inputs and outputs to each function, essentially:\n\n- Input fields: `pixels`, `L`, `sin_degrees`, `self`, `pyramids`, `heights`, `height_measure`, `work`, `save`, `show`.\n- Output fields: a number of tuples and `None` (for the save and plot functions).\n\nIt must be noted that the file seems to be written within the object-oriented paradigm, with references to `self`, which presumes that these functions belong to a class yet to be defined (possibly `guicontrol` given the context clues). And also it imports many libraries including `numpy`, `pyramid`, and `matplotlib`.\n"
],
"segmenting_helpers.py": [
"0ebfc4fc5cd7ad4c8ff990ec3121123a",
"The file `segmenting_helpers.py` appears to be a helper script for segmenting objects from an image. It makes use of packages such as numpy, cv2, t1cv, and bottleneck. \n\nThe following functions are exposed in the script:\n\n- `get_weighted_mean(unique_points, unique_point_count, magnitudes, gray)`: This function calculates the weighted mean of the magnitudes of unique points in an image.\n\n- `get_weighted_median(unique_points, unique_point_count, magnitudes, gray)`: This function calculates the weighted median of the magnitudes of unique points in an image.\n\n- `calculate_withlen_and_orthlen(startpoint, direction_vector, points, pointints, canvas, gray, active, new_points, threshold_percentile = 50)`: This function calculates the width and length of the object along and orthogonal to a direction on an image or canvas. The function takes in startpoint, direction_vector that defines the direction of segmentation, the set of points to consider for segmentation(considered active), and their corresponding pointints on the object. It also takes the canvas on which the image exists, the grayscale image, and the active points for the operation. The function also accepts an optional percentile parameter used during brightness threshold calculation. It returns the length along the direction and orthogonal to it.\n\nIn addition, the script defines a random number generator (`rng`) using the numpy method `np.random.default_rng()`. The methods manipulate and segment the image by processing the image data in two-dimensional arrays to isolate distinct objects in the image."
],
"grad_like.py": [
"0eb6091e64491fa601743d29353537f2",
"The `grad_like.py` file appears to be a Python program that uses OpenCV library for image processing. It defines various methods for interpolating colors, generating steps, conducting image gradient statistics, and handling image manipulations. This includes functions for blurring the image, applying filters and detecting gradients. The program also contains classes like `ImageGradStats` which provides methods for multiple image analysis tasks such as Sobel filter, D2Gradx2y2, image blur etc. \n\nHere's a summary of the main classes and functions exposed in the file:\n\nClasses:\n- `ImageGradStats`: Provides several image processing features like Sobel edge detection, Image blurring, Image gradient functions etc.\n\nFunctions:\n- `darken_edge_points`: darkens and shows the edge points in an image.\n- `edge_points`: extracts edge points of an image.\n- `interpolate_color`: creates intermediate colors by interpolating two colors.\n- `stepf_generate`: generates steps between two given values.\n- `main`: demonstrates usage of the classes and functions in the file. \n\nThe module also includes a main execution block which processes an image by calling the `main` function. Generally, the program seems to be aimed at analyzing and manipulating images using various filtering and edge detection techniques."
],
"hist_check.py": [
"9b4bf9b20587e61b50d57d37659ec34f",
"The Python file 'hist_check.py' reads in grayscale images and manipulates them using multiple techniques to normalize the intensity values, apply a low pass filter and create image histograms. Here are the functions and significant lines of code:\n\n1. `hist_check.py` imports multiple libraries including numpy, matplotlib, and a custom library called 't1cv'. \n\n2. `log_normalize(gray)`: This function performs histogram equalization and normalization on the input grayscale image. It applies a Contrast Limited Adaptive Histogram Equalization (CLAHE), applies Gaussian blur, and uses morphological opening.\n\n3. `brighten_normalize(gray)`: This function prepares to adjust the brightness of the input image, although the current implementation only pads the grayscale image with a 1-pixel extension.\n\n4. `rm_lf(image, rad = 39)`: This function applies a low pass circular filter to the Fourier Transform of the input image, which is useful for smoothing or blurring the image.\n\n5. `apply_multi_low_pass(gray)`: This function applies multiple low-pass filters of varying radii to the grayscale image and averages the results, returning a smoothed version of the original image.\n\n6. `main()`: This is the main function to execute the script. Using the custom 'ImageAnalysis' class, it reads two .tif image files, converts them to grayscale, and applies the 'apply_multi_low_pass' function to the second image (visualized using the 'show' method from the 'ImageAnalysis' class)."
],
"guicontrol.py": [
"83b024dba4899e6fcd5378d6eac3408c",
"The provided Python file, `guicontrol.py`, contains the definition of a class `GuiControl`, which appears to control a GUI related to image analysis of pyramid objects. \n\nKey classes and methods are included: \n- `__init__`: initializes the GuiControl instance with various attributes related to image analysis like saved strings, legends, canvas, etc.\n- `mouse_event_callback`: A callback function invoked when there is a mouse event, typically a button click. \n- `control_loop`: Main event handling loop which seems to react to different keyboard inputs.\n- `update_display`: Updates the display with the provided legend and state.\n- `show_pyramids`: Displays identified pyramids on the provided canvas.\n- `set_guibar_text`: Updates the text on the GUI bar with provided legend and status.\n- `select_mode`: Selects the mode of operation based on a legend and a key press.\n- `default_mode`: The default mode of operation when nothing else is selected.\n- `pyramid_edit`: Allows the user to edit the identified pyramids.\n- `set_micrometer_scale`: Allows the user to set the micrometer scale length.\n- `set_direction`: Sets the direction vector of a pyramid.\n- `edit_pyramid_legs`: Provides functionality to edit the lengths and properties of the pyramid's legs.\n- `computations_menu`: Provides a menu to perform calculations like segmentation, length calculation, etc.\n- `make_histogram`: Creates a histogram based on the collected data of pyramid heights.\n\nThe file concludes with a `main()` function that runs the control loop on an instance of ImageAnalysis. The image files '437-1-03.tif' and '242316_01.tif' seem to be used for this analysis."
],
"reverse_grad_detect_parallel_segment.py": [
"41e317cce803e8b6eb5e519d3cddce20",
"The provided Python file is part of an image analysis library. It includes functions to estimate the length of legs that contribute to the formation of geometric structures in images. The library uses concepts from vector calculus and linear algebra to accomplish this. \n\nHere are the different classes and functions exposed in this file:\n\n- **`dirs_to_point()`**: Computes the vectorized directions from a collection of points to a specific point.\n- **`dirs_to_line_orth()`**: Evaluates orthogonal directions of a line from a collection of points to a starting point on the line.\n- **`scalars_to_line_orth()`**: Given a collection of points, it determines scalars orthogonal to a line.\n- **`dirs_to_point_with_line()`**: Calculates, in vectorized form, the direction of a line from a collection of points.\n- **`do_statistics_get_leglen()`**: Performs statistical analysis on a collection of points to estimate the length of a leg.\n- **`ImageGradDetector`**: This class encapsulates different methods to assist in the analysis and manipulation of an image.\n- **`est_leg_len_parallel()`**, **`est_many_legs()`**, **`est_leg()`**: These methods within the `ImageGradDetector` class calculate the potential leg lengths in an image utilizing the methods above.\n- **`test_curved_vortex_lanscape()`**: This method in `ImageGradDetector` for testing purpose which segments an image with the hill rolling algorithm.\n- **`segment_with_hill_rolling()`**: This method applies the hill-rolling algorithm to segment an image.\n- **`Labeler`**: A helper class that manages labels in an image.\n- **`main()`**: The main function which applies the hill-rolling algorithm and estimations to provided images.\n \nMany functions make use of numpy and CV2 (OpenCV) libraries for complex mathematical operations and direct image manipulations respectively."
],
"pyramid.py": [
"4d9569ee84698bd05320ba50ffe916ff",
"The provided file, `pyramid.py`, is a Python module that defines a class called `Pyramid`. The `Pyramid` class is used for graphical representation and analysis of images, specifically pertaining to analysis and manipulation of pyramid shaped structures.\n\nThe class has several key attributes or fields including:\n- `positions`: Used to store position coordinates.\n- `IA`: An instance of the `ImageAnalysis` class from the `analysis` module.\n- `cross_lengths`: A list used to store lengths of the cross lines.\n- `id`: An optional identifier for a Pyramid instance.\n- `cross_mask`: Used to store the graphical mask of the cross in the pyramid.\n- `mean_position`: The calculated mean position of all positions stored in the `positions` list.\n\nPublic methods of Pyramid class include:\n\n- `set_cross_lengths(lengths)`: Update the `cross_lengths` using provided lengths.\n- `get_center()`: Returns the mean position.\n- `absorb(other)`: Incorporates the positions of another `Pyramid` instance into itself, and recalculates the mean position.\n- `get_mask()`: Returns a graphical mask of the pyramid.\n- `get_line_endpoint()`: Calculates the endpoints of a line, given its length and rotation.\n- `get_cross_mask()`: Returns a graphical mask of the cross in the pyramid.\n- `is_inside_box(box)`: Checks if the pyramid is within a specified box (bounding box). \n\nThis module requires the `numpy` and `cv2` libraries for numerical computation and image processing, and `t1cv` module for line and arrow graphical representations respectively."
]
},
"outlines": {
"analysis.py": [
"b99f87915eafb306282e3b5c0c9616ef",
"`analysis.py` is a python script which utilizes computer vision libraries to analyze and manipulate images. It contains the `ImageAnalysis` class with several class methods to perform operations on images.\n\nThe `ImageAnalysis` class attributes include:\n1. `imgstr`: The image file path.\n2. `fullimg`, `img`, `titlebar`, `gray`: Variations of the full image.\n3. `imgwidth`: The width of the image.\n4. `guibar_original`, `guibar`: Other versions of the entire image.\n5. `render_canvas`: Canvas on which the image is rendered.\n6. `thresh_value`: The threshold value for making contour.\n7. `contour_masks`: Storage for contours of the image.\n8. `region_contexts`: Contain information about multiple pyramid sections in image.\n9. `rotation`: The degrees of rotation applied to the image.\n10. `cross_value_landscape_stored`, `claimed_territory_stored`: Temporarily stored values used in other methods to calculate pyramid shapes.\n11. `laplacian_stored`: Information about image edges.\n12. `window`: Creates a new openCV window for image.\n13. `capture`: Stores the frames for creating .gif files.\n14. `pyramids`: Stores the pyramid segments of the image.\n15. `pyramids_by_id`: Dictionary storing the pyramids with their ids.\n16. `last_show`: Stores the last version of the image.\n17. `prominence`: The prominence value used in peak detection during leg lengths calculation and segmentation.\n\nHighlighted Class methods include:\n1. `save_clipped`: Saves the clipped version of the image.\n2. `show`: Displays the image in an openCV window.\n3. `cross_value_landscape`: Helps in calculating prominence of pyramids.\n4. `cross_claimed_territory_inverse_value`: Helps in inverse value calculation of colors.\n5. `reset_canvas`: Resets the canvas to the original image.\n6. `draw_cross`: Draws a cross on the image.\n7. `consolidate_pyramids`: Absorbs overlapping pyramids.\n8. `segment`: Detects pyramid shapes in images.\n9. `get_leg_lengths`: Calculates the lengths of the four legs of each pyramid.\n10. `draw_pyramids`: Draws the pyramids on the image.\n11. `to_json`: Converts and saves instance attributes into a JSON file.\n12. `IA_from_json`: Recreates instance from JSON file.\n\nThere's also a `main` function at the bottom of the script which calls `ImageAnalysis` class and runs some of its methods for a test.\n"
],
"darken_edges.py": [
"04bcf41578d780e5290cbd85f252f18c",
"The file `darken_edges.py` is a Python script that applies various image processing operations to an input image file, specifically targeting the edges within the image. Three main functions are defined in this script:\n\n1. `generate_and_save_darkened()` applies a darkening process to the edges of an image file. After taking the input image, it employs various image processing techniques to enhance and darken the edges, then saves this processed image.\n\n2. `circle_add(img, points, rad_max = 60, intensity = 200, m = 1, x = 1.2)` is a function to add a circle with specific radius around provided points on an input image.\n\n3. `normalize(img)` is a helper function that normalizes the pixel values of an image.\n\n4. `main()` orchestrates the entire procedures, loads the image, calls the darken edge process, applies a Gaussian blur, inverses the pixel values, and saves the final processed image.\n\nThe file employs the OpenCV library for image loading and manipulation, as well as the numpy library for efficient numerical operations.\n\nThere's not exposed fields as the script is supposed to be run as a standalone program so the primary interface is through the command line. The script does not include any classes or publicly exposed methods for use by other programs. However, the defined functions like `generate_and_save_darkened()`, `circle_add()`, and `normalize()` could potentially be imported and used if the script were to be included as a module."
],
"t1cv.py": [
"f83dbad45d9145b2aaabffb3780f6298",
"The file `t1cv.py` imports several image processing libraries and defines multiple functions and methods for image manipulation, extraction of lines and shapes, presenting images, and performing operations with image contours. \n\nRelevant exposed fields include: \n\n- **dirs_to_point**: Subtracts a given point from all points in the given array.\n- **dirs_to_line_orth**: Projects the difference vectors between a startpoint and multiple other points onto the orthogonal of a direction vector.\n- **scalars_to_line_orth**: Projects the startpoint onto the orthogonal direction of a line defined by a direction vector and returns scalar results.\n- **dirs_to_point_with_line**: Projects voxels in 3D space onto a line within that space.\n- **scalars_to_with_line**: Calculates projections on a line in the form of scalars.\n- **arrow**: Draws an arrow on a given image from a start point to an endpoint.\n- **line**: Draws a line on an image from start point to endpoint.\n- **show**: Displays an image using OpenCV's `imshow` function or matplotlib if the image is grayscale.\n- **combine_thresh_masks**: Blends a list of thresholded binary images into a gradient and binary image.\n- **get_regions_and_add_function**: Returns a dict of region masks categorized by their white space sum.\n- **get_contour_masks**: Returns region masks and contour objects for each contour in an image.\n- **remove_bottom_bar**: Removes the bottom bar of an image (expected to be a color bar or similar).\n- **partition_contours**: Segments contours by sum of region white space into three categories - high, mid, low.\n- **Contour**: A class for managing contours and their hierarchical relations in an image.\n- **parse_hierarchy**: Converts OpenCV\u2019s contour hierarchy into an object-oriented tree of Contour objects.\n- **label_mask**: Applies a structure to a binary mask, labels it, and returns the label mask, number of labels, and a colored version of the label mask.\n- **all_imagestrs**: Returns a generator that yields every tif image filename in the current directory."
],
"grad_detect.py": [
"feba4fca78ff0943bc40e1ab9081062a",
"The provided file seems to be a script for detecting gradients in an image. Here is an outline of its functionality and the exposed fields:\n\n1. **Imports**: The script imports ImageGradStats, numpy, OpenCV, and ImageAnalysis from grad_like, numpy, cv2, t1cv, analysis respectively. \n\n2. **arrow(canvas, startpoint, endpoint, color = (0, 0, 255), thickness = 2)**: This function draws an arrow on an inputted canvas. \n\n3. **Class ImageGradDetector**: This class inherits and extends the function of ImageAnalysis that was imported from analysis. It contains several methods:\n - `__init__(self, IA: ImageAnalysis)`: Initializes instance of the ImageGradDetector class.\n - `est_leg_len(self, startpoint: np.ndarray, direction_vector: np.ndarray, leglen = 1, velocity_scale = 1)`: Estimates the length of a leg starting from a point with a specific direction vector.\n - `est_many_legs(self, startpoints: np.ndarray, direction_vectors: np.ndarray, velocity_scale: int)`: Estimates lengths of multiple legs at once from given start points and direction vectors. \n\n4. **dirs_to_point(point: np.ndarray, points: np.ndarray)**: It calculates vector directions from a set of points to a particular point.\n\n5. **dirs_to_line_orth(startpoint: np.ndarray, direction_vector: np.ndarray, points: np.ndarray)**: It calculates orth directory line.\n\n6. **scalars_to_with_line(startpoint: np.ndarray, direction_vector: np.ndarray, points: np.ndarray)**: This function calculates scalar quantities associated with a line.\n\n7. **dirs_to_point_with_line(startpoint: np.ndarray, direction_vector: np.ndarray, points: np.ndarray)**: Returns the distance from a set of points to a line defined by a starting point and direction vector.\n\n8. **est_leg_lens(IA, startpoints: np.ndarray, directions, velocity_scales: list = [], box_rad = 7., density = 50j, steps = 100)**: Estimate length of legs based on the ImageAnalysis, starting points, directions and other parameters.\n\n9. **main() function**: This is the main function in the script that uses the above classes, objects, and functions to run the calculations. It initializes an instance of ImageAnalysis with an image file, an instance of ImageGradDetector with the ImageAnalysis instance and then estimates the length of legs. \n\n10. **Invoking main() when script is directly run**: When the script is run directly (not imported as a module in another script), the __name__ variable is set to \"__main__\", and the main() function is invoked. If this script is imported as a module in another script, the main function will not be invoked."
],
"grad_detect_parallel.py": [
"2a30d8d0b51bc46108205866b7f1e24b",
"The outline of the file and relevant exposed fields are as follows:\n\n1. **Import statements**: Importing required libraries.\n\n2. **Functions**: Defines various functions including the following:\n - `dirs_to_point`: Calculates difference between a point and multiple points \n\n - `dirs_to_line_oth`: Returns the orthogonal component of points relative to a line\n\n - `scalars_to_line_orth`: Returns the scalar component of a line orthogonal to the points\n \n - `dirs_to_point_with_line`: Returns the directions of points to a line with startpoint and directional vector\n \n - `scalars_to_with_line`: Returns the scalar product between difference of points from starting point with the line direction\n\n - `arrow`: Draws an arrowed line on an image\n \n - `calculate_leglen`: Calculates the leg length from gathered points\n\n3. **Class ImageGradDetector**: Main class that includes functionality for image gradient detection and analysis with the following methods:\n\n - `__init__`: Preprocessing the image for gradient analysis.\n\n - `est_leg_len_parallel`: Estimates the parralel leg length from the pyramids at the startpoints.\n\n - `est_many_legs`: Estimates leg lengths of multiple lines.\n\n - `test_curved_vortex_landscape`: Tests a curved vortex landscape image segmentation.\n\n - `segment_with_hill_rolling`: Segments image using a hill rolling algorithm.\n \n4. **Class Labeler**: Includes functionality for labeling segments with the following methods:\n\n - `__init__`: Setups the initial state for labeling.\n\n - `merge_pairs`: Merges sector pairs.\n\n - `get_mergepairs`: Get the mergeable pairs.\n\n - `merge`: Merges labels.\n\n - `update_srcpoints`: Updates source points for labeling.\n\n - `update_destpoints`: Updates destination points for labeling.\n\n - `update_labelstates`: Updates the labeling and unlabeled states based on the source and destination points.\n \n - `label_singles`: Updates the necessary states for label update.\n \n - `label_single_srcs`: It labels the source points.\n\n - `label_single_dests`: It labels the destination points.\n\n - `add_necessary_labels`: Updates the necessary states for the label update.\n\n5. **Function est_leg_lens** : Estimates leg lengths of multiple grids of points.\n\n6. **main Function**: A function that tests the defined methods by displaying an image with visual navigation of the found gradients.\n\n Note that the methods and classes in this file are centered towards using a hill-climbing algorithm for an image's pixel intensity to detect and segment out regions of interest based on calculated gradients. \n \n This script file seems to be a part of larger package/module as it is importing analysis and grad_like from top level."
],
"segmenting.py": [
"6f91bd21be4bbb4849a9cfc529957628",
"The script provides the code for image segmentation in python. The primary class present in the code is `ImageSegmenter`, that takes input as an object of `ImageAnalysis` class and performs segmentation on the given image.\n\n### Class Outline:\n- `ImageSegmenter`: Class designed for segmenting an image.\n - `__init__`: Initializer, assigns an ImageAnalysis object and also instantiates object of ImageGradStats class from `grad_like.py`.\n - `est_leg_len_parallel`: Method to estimate leg length in a direction for pyramids with centers at startpoints[]. \n \n### Function Outline:\n\n- `segment`: Segments the input image by creating a grid of points with the help of a density and creating a velocity vector. Variable iterations define the iterations for adjusting the grid points. Parameters:\n - Gray :Grayscale form of the image.\n - IA : Object of analysis.ImageAnalysis.\n- `main` : Main function, which handles the execution flow starting from image analysis till its segmentation.\n \nThe script also has an instance of `cProfile` that is a python profiler to show time of execution statistics for the script's callables. The profiler has been commented out and not used anywhere in the script."
],
"calculate.py": [
"868b6b862c65cb5504531689ca514aeb",
"The `calculate.py` Python script includes a variety of functions related to image analysis, particularly towards pyramid objects in an image. The script includes importing of packages, and five key functions that manipulate and analyze pyramid objects. \n\nThe following are key function and their parameters:\n\n1. `pixels_to_um`: This function converts pixels to micrometers. Parameters:\n - `self`\n - `pixels`\n\n2. `H`: The function calculates measurements based on the provided list of pyramid parameters. Parameters:\n - `L`\n - `sin_degrees`\n\n3. `height_calc`: This function calculates the height of the pyramids in the image. Parameters:\n - `self`\n - `pyramids`\n\n4. `make_hist`: This function makes a histogram of height of the pyramids. Parameters:\n - `self`\n - `heights`\n - `height_measure`\n - `work`\n - `save` (default= True)\n - `show` (default= True)\n\n5. `height_calc_hist_and_save`: This function calculates height and saves histogram of the pyramids in an image. Parameters:\n - `self`\n - `pyramids`\n\nFields and data types in the file:\n\n- `self`: It is an instance of GuiControl class that controls and manages the GUI.\n- `pyramids`: It's a List of `Pyramid` objects.\n- `L`: A list containing heights of pyramid's sides.\n- `pixels`: A numpy ndarray of pixel-values that needs to be converted to micrometer\n- `sin_degrees`: A str representing sine of some angle.\n- `heights`: A List of floats representing the height(s) of the pyramids.\n- `height_measure`: A string that determine the type of height measure.\n- `work`: A List of strings that holds data for pyramids.\n- `save`: A boolean to determine whether to save or not the result.\n- `show`: A boolean to determine whether to show or not the result."
],
"segmenting_helpers.py": [
"0ebfc4fc5cd7ad4c8ff990ec3121123a",
"This is a Python file named `segmenting_helpers.py` and primarily deals with image processing tasks using libraries such as cv2 and numpy. \n\nExposed functions:\n\n1. `get_weighted_mean(unique_points, unique_point_count, magnitudes, gray)`: Computes the mean of the magnitudes of the unique points factoring in weights based on the brightness rank of the points in grayscale. It returns the weighted mean.\n\n2. `get_weighted_median(unique_points, unique_point_count, magnitudes, gray)`: Calculates the weighted median of a set of unique points, considering their brightness in grayscale as the weight. It returns the weighted median.\n\n3. `calculate_withlen_and_orthlen(startpoint, direction_vector, points, pointints, canvas, gray, active, new_points, threshold_percentile = 50)`: This function calculates 'withlen' and 'orthlen' using brightness, magnitudes, and point positions. It also allows weights to come in to play, ensuring a more nuanced handling of associated values. It returns the values of 'withlen' and 'orthlen'."
],
"grad_like.py": [
"0eb6091e64491fa601743d29353537f2",
"This file appears to be a utility for applying various gradient-based image processing operations, such as edge detection and gradient flow tracking on grayscale images. It incorporates functionalities provided by the `cv2` and `numpy` modules.\n\n**Class and its important attributes and methods**\n\nThe file defines a single class `ImageGradStats` with the following instance variables and methods:\n\n- Instance variables:\n - `IA: ImageAnalysis`: an object containing the original image and processed images.\n - `laplacian_stored, sobel_stored, filter2d_stored, filter2d_kernel_stored`: variables to store results of applying corresponding CV2 filters and transformations. \n - `gray`: a grayscale copy of the original image.\n - `default_iterations, default_stepsize, default_stepf, default_interval`: default parameters for iterative gradient operations.\n\n- Methods:\n - `reset_gray, blur_gray`: methods to manipulate the stored grayscale copy.\n - `sobel, dgrad, npdgrad, dgrad_positive_side, d2grad, d2gradx2y2, sobelxy`: methods that calculate different types of gradient or derivative features. \n - `draw_vector_field, do_rollfield, hillroll_line, filter2d, get_laplacian`: methods for displaying or calculating vector fields using gradients, applying 2D filters and getting Laplacian.\n - `hillroll_points_iterate, hillroll_points_iterate_velocity, active_points, hillroll_points`: methods for gradually altering set of points following the \"hill roll\" strategy, given a vector field.\n \n**Functions**\n\nThe file also contains the following standalone functions:\n\n- `interpolate_color, stepf_generate`: helper functions to interpolate between colors and create a sequence of steps, respectively.\n- `main`: a function demonstrating usage of the `ImageGradStats` class, particularly the hill roll method.\n- `darken_edge_points, edge_points`: functions for detecting edge points in an image using gradient descent iterations. \n\n**Constants**\n\nThe file contains definitions for `colorsUp` and `colorsDown`, which are to generate color variations for use in the hill roll operations."
],
"hist_check.py": [
"9b4bf9b20587e61b50d57d37659ec34f",
"The file `hist_check.py` is a Python script primarily used for image processing through several enhancement and normalization methods. Relevant functionality exposed includes:\n\n1. `log_normalize(gray)`: This function takes in a grayscale image and attempts to normalize it by applying a Gaussian blur, morphological operations, and histogram equalization. Although the function returns the morphological result early, there are kept, but unused, operations of logarithmic normalization and further blurring and equalizing of the image.\n\n2. `brighten_normalize(gray)`: This is an incomplete function that appears to be intended for brightening the provided grayscale image and achieving a uniform histogram, but it currently only does some padding operations.\n\n3. `rm_lf(image, rad = 39)`: This function performs a low-frequency removal operation on the input grayscale image utilizing DFT (Discrete Fourier Transform) and IDFT (Inverse Discrete Fourier Transform) operations.\n\n4. `apply_multi_low_pass(gray)`: This function applies a series of low-pass filter operations with the `rm_lf()` function across radii from 20 to 120 inclusive. Results are then blended with the original image and returned.\n\n5. `main()`: Entry point used when the script is run directly. This orchestrates applying the multi low-pass method on an image from an ImageAnalysis object and displays the result."
],
"guicontrol.py": [
"83b024dba4899e6fcd5378d6eac3408c",
"The `guicontrol.py` file defines the `GuiControl` class, which allows interaction with an image analysis program through a GUI. It contains multiple functions for loading and manipulating images, and defining and interacting with GUI components.\n\nThe main fields and methods of the file are:\n\n- **Constants for key bindings**: `K_ESC`, `K_ENTER`, `K_SPACE`, etc. Button bindings for interacting with the GUI.\n\n- **GuiControl class**: Contains several methods for GUI interactivity, such as control loops, mouse events, mode selection and display updates. \n\n - `__init__`: Initialization method for the GuiControl class. \n\n - `mouse_event_callback`: Method to deal with mouse events.\n\n - `control_loop`: Main loop where the GUI waits for events and processes them.\n\n - `update_display`: Updates the display with legend, state and pyramids.\n\n - `parse_state_key`: Creates a dictionary state from a list state.\n\n - `state_list_to_dict`: Converts the list state to dictionary state.\n\n - `show_pyramids`: Draws the pyramids on the image analyser's (IA) last_show and updates the GUI canvas.\n\n - `set_guibar_text`: Sets the text displayed on the GUI bar.\n\n - `select_mode`: Selects functioning mode according to the provided key.\n\n - Several methods to define and handle various modes, such as`default_mode`, `pyramid_count`, `selected_pyramid_count`, `pyramid_edit`, `add_pyramid`,`delete_pyramids`, `select_pyramid`,`set_micrometer_scale`, `set_micrometer_scale_length`,`um_per_pixel`,`set_direction`,`edit_pyramid_legs`,`change_selected_leg`,`computations_menu`,`edit_str`,and `make_histogram`.\n \n- **Module-level executable code**: When the module is run as a script, a `GuiControl` object is created and its `control_loop` method is run. The `main()` function encapsulates this behavior. It initializes an `ImageAnalysis` object with a specific image file and starts the GUI control loop for it."
],
"reverse_grad_detect_parallel_segment.py": [
"41e317cce803e8b6eb5e519d3cddce20",
"The file `reverse_grad_detect_parallel_segment.py` is a Python script used for image processing and analysis. It primarily focuses on feature extraction and segmentation based on gradient detection and hill rolling algorithm on images.\n\nRelevant exposed fields and classes, include:\n\n1. `class ImageGradDetector`: An abstraction class that wraps around image analysis object for image feature detection & analysis.\n * `est_leg_len_parallel(self, startpoints, leglens, direction_vector, velocity_scale, box_rad=7., density = 1j, steps = 100)`: Estimates the length of a leg in a direction for pyramids with centers at startpoints.\n * `est_many_legs(self, startpoints, direction_vectors, velocity_scale)`: Estimates the length of multiple legs given their starting points and direction vectors.\n * `test_curved_vortex_lanscape(self, density, **kwargs)`: Performs image segmentation with hill rolling algorithm.\n * `segment_with_hill_rolling(self, density, **kwargs)`: Segments an image using a hill rolling algorithm.\n\n2. `class Labeler`: A helper class used for image labeling during the hill rolling process. Its methods include `merge_pairs`, `merge`, `update_srcpoints`, `update_labelstates`, `label_single_srcs`, `label_single_dests` and others. \n\n3. `main()`: The main entry point function of the script. It demonstrates the usage of above classes and functions.\n\nAdditionally, the file contains several helper functions such as `dirs_to_point`, `scalars_to_with_line`, `arrow`, `do_statistics_get_leglen`, and others which are used for various image processing tasks.\n\nNote: The script requires several external libraries including `numpy`, `cv2`, `ImageGradStats`, and `ImageAnalysis` among others."
],
"pyramid.py": [
"4d9569ee84698bd05320ba50ffe916ff",
"The `pyramid.py` file provides a class `Pyramid` that is used to analyze and manipulate objects that symbolize image analyzing pyramids. Here are the fields in the `Pyramid` class:\n\n- `positions`: This is an array of numpy positions for the pyramid object.\n\n- `IA`: This is an instance of `ImageAnalysis` from the `analysis` module. This field represents the image to be analyzed.\n\n- `cross_lengths`: This is a list of lengths for the pyramid cross. Default values are `[5,5,5,5]`.\n\n- `id`: This is an optional identifier for a pyramid object. It's not required upon initialization.\n\n- `cross_mask`: This field represents a mask for a cross symbol of the pyramid. The initial value is None.\n\n- `mean_position`: This field represents the mean position of the pyramid object, calculated using provided `pos`.\n\nClass methods:\n\n- `set_cross_lengths(self, lengths)`: Sets the cross lengths for the pyramid.\n\n- `get_center(self)`: Returns the mean position of the pyramid.\n\n- `absorb(self, other)`: When another pyramid object is provided, it adds their positions to the current positions and re-calculates the mean position.\n\n- `get_mask(self, canvas = None, color=None, meanonly = False, lengths = None)`: This method is used to draw the entire pyramid on a given canvas using a specified color.\n\n- `get_line_endpoint(self, length, i, rotation = None, x = None, y = None)`: Gets the endpoint of a line given a length and an index (`i`).\n\n- `get_cross_mask(self, x = None, y = None, color=None, lengths = None, rotation = None, thickness = 1, canvas = None)`: Draws a cross shape on the pyramid using defined parameters or default ones.\n\n-`is_inside_box(self, box)`: Checks if the pyramid is inside a given box. \n\nThe `Pyramid` class is dependent on the numpy and cv2 packages, as well as the `analysis` module and `line` and `arrow` functions from the `t1cv` module."
]
}
}