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import os
import coremltools
from crowdcount.models import paths as ccp
from import BaseCommand
from keras.engine import InputLayer
from keras.models import load_model
class Command(BaseCommand):
def add_arguments(self, parser):
parser.add_argument('--mlversion', default=2)
parser.add_argument("-o", "--output", required=False, default='tmp/CrowdPredictor.mlmodel', help="output coreml file")
def handle(self, *args, **kwargs):
os.makedirs("tmp", exist_ok=True)
version = kwargs['mlversion']
path = ccp.weights_for("density", version)
output = kwargs['output']
print("Converting {0} to {1}".format(path, output))
model = load_model(path)
# Create a new input layer to replace the (None,None,3) input layer
input_layer = InputLayer(input_shape=(675, 900, 3), name="input_1")
# Save
intermediary_path = "tmp/reshaped_model.h5"
model.layers[0] = input_layer
# Convert
coreml_model = coremltools.converters.keras.convert(
# Set model metadata = 'Dimitri Roche'
coreml_model.short_description = 'Generates a density map with the crowd estimate as the sum of pixels'
coreml_model.input_description['input_1'] = 'Image to calculate density map'
coreml_model.output_description['density_map'] = 'Density map where the sum of pixels is crowd count'