MiDaS-master/tf/run_pb.py

"""Compute depth maps for images in the input folder.
"""
import os
import glob
import utils
import cv2
import argparse

import tensorflow as tf

from transforms import Resize, NormalizeImage, PrepareForNet

def run(input_path, output_path, model_path, model_type="large"):
    """Run MonoDepthNN to compute depth maps.

    Args:
        input_path (str): path to input folder
        output_path (str): path to output folder
        model_path (str): path to saved model
    """
    print("initialize")

    # the runtime initialization will not allocate all memory on the device to avoid out of GPU memory
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
      try:
        for gpu in gpus:
          #tf.config.experimental.set_memory_growth(gpu, True)
          tf.config.experimental.set_virtual_device_configuration(gpu,
            [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4000)])
      except RuntimeError as e:
        print(e)

    # network resolution
    if model_type == "large":
        net_w, net_h = 384, 384
    elif model_type == "small":
        net_w, net_h = 256, 256
    else:
        print(f"model_type '{model_type}' not implemented, use: --model_type large")
        assert False

    # load network
    graph_def = tf.compat.v1.GraphDef()
    with tf.io.gfile.GFile(model_path, 'rb') as f:
        graph_def.ParseFromString(f.read())
        tf.import_graph_def(graph_def, name='')

    
    model_operations = tf.compat.v1.get_default_graph().get_operations()
    input_node = '0:0'
    output_layer = model_operations[len(model_operations) - 1].name + ':0'
    print("Last layer name: ", output_layer)

    resize_image = Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=False,
                ensure_multiple_of=32,
                resize_method="upper_bound",
                image_interpolation_method=cv2.INTER_CUBIC,
            )
    
    def compose2(f1, f2):
        return lambda x: f2(f1(x))

    transform = compose2(resize_image, PrepareForNet())

    # get input
    img_names = glob.glob(os.path.join(input_path, "*"))
    num_images = len(img_names)

    # create output folder
    os.makedirs(output_path, exist_ok=True)

    print("start processing")

    with tf.compat.v1.Session() as sess:
      try:
        # load images
        for ind, img_name in enumerate(img_names):

            print("  processing {} ({}/{})".format(img_name, ind + 1, num_images))

            # input
            img = utils.read_image(img_name)
            img_input = transform({"image": img})["image"]

            # compute
            prob_tensor = sess.graph.get_tensor_by_name(output_layer)
            prediction, = sess.run(prob_tensor, {input_node: [img_input] })
            prediction = prediction.reshape(net_h, net_w)
            prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
            
            # output
            filename = os.path.join(
                output_path, os.path.splitext(os.path.basename(img_name))[0]
            )
            utils.write_depth(filename, prediction, bits=2)

      except KeyError:
        print ("Couldn't find input node: ' + input_node + ' or output layer: " + output_layer + ".")
        exit(-1)

    print("finished")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument('-i', '--input_path', 
        default='input',
        help='folder with input images'
    )

    parser.add_argument('-o', '--output_path', 
        default='output',
        help='folder for output images'
    )

    parser.add_argument('-m', '--model_weights', 
        default='model-f6b98070.pb',
        help='path to the trained weights of model'
    )

    parser.add_argument('-t', '--model_type', 
        default='large',
        help='model type: large or small'
    )

    args = parser.parse_args()

    # compute depth maps
    run(args.input_path, args.output_path, args.model_weights, args.model_type)