Example #1
0
def main():
    """Use local data to train the neural net, probably made by bin/create_training_data.py."""
    parser = create_parser()
    args = parser.parse_args()
    with open(CACHE_PATH + 'raster_data_paths.pickle', 'r') as infile:
        raster_data_paths = pickle.load(infile)
    test_images, model = train_on_cached_data(raster_data_paths, args.neural_net, args.bands,
                                              args.tile_size, args.number_of_epochs)
    if not args.omit_findings:
        for path in raster_data_paths:
            print path
            labels, images = load_training_tiles(path)
            if len(labels) == 0 or len(images) == 0:
                print("WARNING, there is a borked naip image file")
                continue
            false_positives, false_negatives, fp_images, fn_images = list_findings(labels, images,
                                                                                   model)
            path_parts = path.split('/')
            filename = path_parts[len(path_parts) - 1]
            print("FINDINGS: {} false pos and {} false neg, of {} tiles, from {}".format(
                len(false_positives), len(false_negatives), len(images), filename))
            render_results_for_analysis([path], false_positives, fp_images, args.bands,
                                        args.tile_size)

    if args.render_results:
        predictions = predictions_for_tiles(test_images, model)
        render_results_for_analysis(raster_data_paths, predictions, test_images, args.bands,
                                    args.tile_size)
Example #2
0
def post_findings_to_s3(raster_data_paths, model, training_info, render_results):
    """Aggregate findings from all NAIPs into a pickled list, post to S3."""
    findings = []
    for path in raster_data_paths:
        labels, images = load_training_tiles(path)
        if len(labels) == 0 or len(images) == 0:
            print("WARNING, there is a borked naip image file")
            continue
        false_positives, fp_images = list_findings(labels, images, model)
        path_parts = path.split('/')
        filename = path_parts[len(path_parts) - 1]
        print("FINDINGS: {} false pos of {} tiles, from {}".format(
            len(false_positives), len(images), filename))
        if render_results:
            # render JPEGs showing findings
            render_results_for_analysis([path], false_positives, fp_images, training_info['bands'],
                                        training_info['tile_size'])

        # combine findings for all NAIP images analyzedfor the region
        [findings.append(f) for f in tag_with_locations(fp_images, false_positives,
                                                        training_info['tile_size'])]

    # dump combined findings to disk as a pickle
    try:
        os.mkdir(CACHE_PATH + training_info['naip_state'])
    except:
        pass
    naip_path_in_cache_dir = training_info['naip_state'] + '/' + 'findings.pickle'
    local_path = CACHE_PATH + naip_path_in_cache_dir
    with open(local_path, 'w') as outfile:
        pickle.dump(findings, outfile)

    # push pickle to S3
    s3_client = boto3.client('s3')
    s3_client.upload_file(local_path, FINDINGS_S3_BUCKET, naip_path_in_cache_dir)
Example #3
0
def render_errors(raster_data_paths, model, training_info, render_results):
    """Render JPEGs showing findings."""
    for path in raster_data_paths:
        labels, images = load_training_tiles(path)
        if len(labels) == 0 or len(images) == 0:
            print("WARNING, there is a borked naip image file")
            continue
        false_positives, fp_images = list_findings(labels, images, model)
        path_parts = path.split('/')
        filename = path_parts[len(path_parts) - 1]
        print("FINDINGS: {} false pos of {} tiles, from {}".format(
            len(false_positives), len(images), filename))
        render_results_for_analysis([path], false_positives, fp_images, training_info['bands'],
                                    training_info['tile_size'])
Example #4
0
def main():
    """Use local data to train the neural net, probably made by bin/create_training_data.py."""
    parser = create_parser()
    args = parser.parse_args()
    with open(CACHE_PATH + 'raster_data_paths.pickle', 'r') as infile:
        raster_data_paths = pickle.load(infile)
    test_images, model = train_on_cached_data(raster_data_paths,
                                              args.neural_net, args.bands,
                                              args.tile_size,
                                              args.number_of_epochs)
    if not args.omit_findings:
        findings = []
        for path in raster_data_paths:
            print path
            labels, images = load_training_tiles(path)
            if len(labels) == 0 or len(images) == 0:
                print("WARNING, there is a borked naip image file")
                continue
            false_positives, false_negatives, fp_images, fn_images = list_findings(
                labels, images, model)
            path_parts = path.split('/')
            filename = path_parts[len(path_parts) - 1]
            print(
                "FINDINGS: {} false pos and {} false neg, of {} tiles, from {}"
                .format(len(false_positives), len(false_negatives),
                        len(images), filename))
            # render JPEGs showing findings
            render_results_for_analysis([path], false_positives, fp_images,
                                        args.bands, args.tile_size)

            # combine findings for all NAIP images analyzed
            [
                findings.append(f) for f in tag_with_locations(
                    fp_images, false_positives, args.tile_size)
            ]

        # dump combined findings to disk as a pickle
        with open(CACHE_PATH + 'findings.pickle', 'w') as outfile:
            pickle.dump(findings, outfile)

        # push pickle to S3
        s3_client = boto3.client('s3')
        s3_client.upload_file(CACHE_PATH + 'findings.pickle', 'deeposm',
                              'findings.pickle')

    if args.render_results:
        predictions = predictions_for_tiles(test_images, model)
        render_results_for_analysis(raster_data_paths, predictions,
                                    test_images, args.bands, args.tile_size)
Example #5
0
def main():
    """Use local data to train the neural net, probably made by bin/create_training_data.py."""
    parser = create_parser()
    args = parser.parse_args()
    with open(CACHE_PATH + 'raster_data_paths.pickle', 'r') as infile:
        raster_data_paths = pickle.load(infile)
    test_images, model = train_on_cached_data(raster_data_paths, args.neural_net, args.bands,
                                              args.tile_size, args.number_of_epochs)
    if not args.omit_findings:
        findings = []
        for path in raster_data_paths:
            print path
            labels, images = load_training_tiles(path)
            if len(labels) == 0 or len(images) == 0:
                print("WARNING, there is a borked naip image file")
                continue
            false_positives, false_negatives, fp_images, fn_images = list_findings(labels, images,
                                                                                   model)
            path_parts = path.split('/')
            filename = path_parts[len(path_parts) - 1]
            print("FINDINGS: {} false pos and {} false neg, of {} tiles, from {}".format(
                len(false_positives), len(false_negatives), len(images), filename))
            # render JPEGs showing findings
            render_results_for_analysis([path], false_positives, fp_images, args.bands,
                                        args.tile_size)

            # combine findings for all NAIP images analyzed
            [findings.append(f) for f in tag_with_locations(fp_images, false_positives,
                                                            args.tile_size)]

        # dump combined findings to disk as a pickle
        with open(CACHE_PATH + 'findings.pickle', 'w') as outfile:
            pickle.dump(findings, outfile)

        # push pickle to S3
        s3_client = boto3.client('s3')
        s3_client.upload_file(CACHE_PATH + 'findings.pickle', 'deeposm', 'findings.pickle')

    if args.render_results:
        predictions = predictions_for_tiles(test_images, model)
        render_results_for_analysis(raster_data_paths, predictions, test_images, args.bands,
                                    args.tile_size)
Example #6
0
def post_findings_to_s3(raster_data_paths, model, training_info, bands,
                        render_results):
    """Aggregate findings from all NAIPs into a pickled list, post to S3."""
    findings = []
    for path in raster_data_paths:
        labels, images = load_all_training_tiles(path, bands)
        if len(labels) == 0 or len(images) == 0:
            print("WARNING, there is a borked naip image file")
            continue
        false_positives, fp_images = list_findings(labels, images, model)
        path_parts = path.split('/')
        filename = path_parts[len(path_parts) - 1]
        print("FINDINGS: {} false pos of {} tiles, from {}".format(
            len(false_positives), len(images), filename))
        if render_results:
            # render JPEGs showing findings
            render_results_for_analysis([path], false_positives, fp_images,
                                        training_info['bands'],
                                        training_info['tile_size'])

        # combine findings for all NAIP images analyzedfor the region
        [
            findings.append(f) for f in tag_with_locations(
                fp_images, false_positives, training_info['tile_size'],
                training_info['naip_state'])
        ]

    # dump combined findings to disk as a pickle
    try:
        os.mkdir(CACHE_PATH + training_info['naip_state'])
    except:
        pass
    naip_path_in_cache_dir = training_info[
        'naip_state'] + '/' + 'findings.pickle'
    local_path = CACHE_PATH + naip_path_in_cache_dir
    with open(local_path, 'w') as outfile:
        pickle.dump(findings, outfile)

    # push pickle to S3
    s3_client = boto3.client('s3')
    s3_client.upload_file(local_path, FINDINGS_S3_BUCKET,
                          naip_path_in_cache_dir)