pingtile 0.0.1b10

Interface to tile sonar mosaics and maps.

PINGTile

Utility to tile sonar mosaics and maps.

UNDER CONSTRUCTION

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Installation

1. Install Miniforge.
2. Open the Miniforge prompt.
3. Install PINGInstaller:

   pip install pinginstaller
   

4. Install PINGTile.

   python -m pinginstaller pingtile
   

Usage

1. Copy the following script to some location on your computer:

'''
Copyright (c) 2025 Cameron S. Bodine
'''

#########
# Imports

import os, sys
from joblib import Parallel, delayed, cpu_count

# Debug
from imglbl2tile import doImgLbl2tile
from utils import mask_to_coco_json

# # For Package
# from pingtile.imglbl2tile import doImgLbl2tile
# from pingtile.utils import mask_to_coco_json

import rasterio as rio
import json

############
# Parameters

map = r'Z:\tmp\pingtile_test\map\Model_Training_Substrate_Polygons_Export.shp'
sonarDir = r'Z:\tmp\pingtile_test\mosaic'

outDirTop = r'Z:\tmp\pingtile_test'
outName = 'Hudson'

classCrossWalk = {
    '0':0,
    'U':1,
    'G':2,
    'B_C':3,
    'B':4
}

windowSize_m = [
                (12,12),
                (18,18),
                (24,24),
                ]

windowStride = 3
classFieldName = 'Substrate_'
minArea_percent = 0.5
target_size = (512, 512) #(1024, 1024)
threadCnt = 0.75
epsg_out = 32616
doPlot = True
lbl2COCO = True

if not os.path.exists(outDirTop):
    os.makedirs(outDirTop)


###############################################
# Specify multithreaded processing thread count
if threadCnt==0: # Use all threads
    threadCnt=cpu_count()
elif threadCnt<0: # Use all threads except threadCnt; i.e., (cpu_count + (-threadCnt))
    threadCnt=cpu_count()+threadCnt
    if threadCnt<0: # Make sure not negative
        threadCnt=1
elif threadCnt<1: # Use proportion of available threads
    threadCnt = int(cpu_count()*threadCnt)
    # Make even number
    if threadCnt % 2 == 1:
        threadCnt -= 1
else: # Use specified threadCnt if positive
    pass

if threadCnt>cpu_count(): # If more than total avail. threads, make cpu_count()
    threadCnt=cpu_count();
    print("\nWARNING: Specified more process threads then available, \nusing {} threads instead.".format(threadCnt))

print("\nUsing {} threads for processing.\n".format(threadCnt))


# Find all sonar files
sonarFiles = []
for root, dirs, files in os.walk(sonarDir):
    for file in files:
        if file.lower().endswith('.tif') or file.lower().endswith('.tiff'):
            sonarFiles.append(os.path.join(root, file))


for windowSize in windowSize_m:

    # windowStride_m = windowStride*windowSize[0]
    windowStride_m = windowStride
    # minArea = minArea_percent * windowSize[0]*windowSize[1]

    dirName = f"{windowSize[0]}_{windowSize[0]}"
    outDir = os.path.join(outDirTop, dirName)
    outSonDir = os.path.join(outDir, 'images')
    outMaskDir = os.path.join(outDir,'labels')
    pltDir = os.path.join(outDir,'plots')

    if not os.path.exists(outSonDir):
        os.makedirs(outSonDir)
        os.makedirs(outMaskDir)
        os.makedirs(pltDir)

    for sonarFile in sonarFiles:

        print(f"\nProcessing {os.path.basename(sonarFile)} with windowSize: {windowSize} and windowStride_m: {windowStride_m}...\n")

        doImgLbl2tile(inFileSonar=sonarFile,
                      inFileMask=map,
                      outDir=outDir,
                      outName=outName,
                      epsg_out=epsg_out,
                      classCrossWalk=classCrossWalk,
                      windowSize=windowSize,
                      windowStride_m=windowStride_m,
                      classFieldName=classFieldName,
                      minArea_percent=minArea_percent,
                      target_size=target_size,
                      threadCnt=threadCnt,
                      doPlot=doPlot
                      )

# Convert masks to COCO format
if lbl2COCO:
    

    for windowSize in windowSize_m:

        dirName = f"{windowSize[0]}_{windowSize[0]}"
        outDir = os.path.join(outDirTop, dirName)
        outSonDir = os.path.join(outDir, 'images')
        outMaskDir = os.path.join(outDir,'labels')
        pltDir = os.path.join(outDir,'plots')
        outJsonDir = os.path.join(outDir,'json')

        if not os.path.exists(outJsonDir):
            os.makedirs(outJsonDir)

        print(f"\nConverting to COCO format for windowSize: {windowSize}...\n")

        # Get the mask files
        maskFiles = []
        for root, dirs, files in os.walk(outMaskDir):
            for file in files:
                if file.lower().endswith(('.tif', '.tiff', '.png', '.jpg', '.jpeg')):
                    maskFiles.append(os.path.join(root, file))

        maskFiles=maskFiles[:10] # Debug limit to 10 files

        # Build categories list / lookup from classCrossWalk
        # categories_info passed to mask_to_coco_json should map id -> name
        categories_info = {v: str(k) for k, v in classCrossWalk.items()}
        # COCO categories (exclude background id 0 if present)
        categories = [{"id": v, "name": str(k)} for k, v in classCrossWalk.items() if v != 0]

        coco = {
            "info": {"description": outName or ""},
            "licenses": [],
            "images": [],
            "annotations": [],
            "categories": categories
        }

        annotation_id = 1
        image_id = 1

        for mask_path in maskFiles:
            base = os.path.splitext(os.path.basename(mask_path))[0]

            # try to find corresponding image filename in images folder (same base name)
            matched_image = None
            for ext in ('.png', '.jpg', '.jpeg', '.tif', '.tiff'):
                candidate = os.path.join(outSonDir, base + ext)
                if os.path.exists(candidate):
                    matched_image = os.path.basename(candidate)
                    break
            if matched_image is None:
                # fallback to mask basename (acceptable as file_name in COCO)
                matched_image = os.path.basename(mask_path)

            # read mask to get width/height
            try:
                with rio.open(mask_path) as src:
                    width, height = src.width, src.height
            except Exception as e:
                print(f"Skipping {mask_path}: cannot read ({e})")
                continue

            image_info = {
                "id": image_id,
                "file_name": matched_image,
                "width": width,
                "height": height
            }

            # mask_to_coco_json should return (annotations_list, next_annotation_id)
            anns, annotation_id = mask_to_coco_json(mask_path, image_info, categories_info, annotation_id)

            if anns:
                coco["images"].append(image_info)
                coco["annotations"].extend(anns)
                image_id += 1

        out_json = os.path.join(outJsonDir, f"_annotations.coco.json")
        with open(out_json, "w") as f:
            json.dump(coco, f)

2. Open the file with Visual Studio Code.
3. Update the Parameters as necessary:

############
# Parameters

map = r'Z:\tmp\pingtile_test\map\Model_Training_Substrate_Polygons_Export.shp'
sonarDir = r'Z:\tmp\pingtile_test\mosaic'

outDirTop = r'Z:\tmp\pingtile_test'
outName = 'Hudson'

classCrossWalk = {
    '0':0,
    'U':1,
    'G':2,
    'B_C':3,
    'B':4
}

windowSize_m = [
                (12,12),
                (18,18),
                (24,24),
                ]

windowStride = 3
classFieldName = 'Substrate_'
minArea_percent = 0.5
target_size = (512, 512) #(1024, 1024)
threadCnt = 0.75
epsg_out = 32616
doPlot = True
lbl2COCO = True

4. Ensure the pingtile environment is selected as the Interpreter see this.
5. Run the script in debug mode by pressing F5.

Upload Dataset to Roboflow

It is possible to upload your dataset to Roboflow with the following script:

import glob
from roboflow import Roboflow

# Initialize Roboflow client
rf = Roboflow(api_key="ADD_ROBOFLOW_API_KEY_HERE") #More info: https://docs.roboflow.com/developer/authentication/find-your-roboflow-api-key

# Directory path and file extension for images
dir_name = r"Z:\tmp\pingtile_test\12_12\json"
file_extension_type = ".png"

# Annotation file path and format (e.g., .coco.json)
annotation_filename = r"Z:\tmp\pingtile_test\12_12\json\_annotations.coco.json"

# Get the upload project from Roboflow workspace
project = rf.workspace().project("ADD_PROJECT_NAME_HERE")

# Upload images
image_glob = glob.glob(dir_name + '/*' + file_extension_type)
for image_path in image_glob:
    print(project.single_upload(
        image_path=image_path,
        annotation_path=annotation_filename,
        # -- optional parameters: --
        # annotation_labelmap=labelmap_path,
        # split='train',
        # num_retry_uploads=0,
        # batch_name='batch_name',
        # tag_names=['tag1', 'tag2'],
        # is_prediction=False,
    ))