<reponame>jbutle55/yolov3-tf2<filename>detect.py
import time
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import numpy as np
import tensorflow as tf
from yolov3_tf2.models import YoloV3, YoloV3Tiny
from yolov3_tf2.dataset import transform_images, load_tfrecord_dataset, transform_targets
from yolov3_tf2.utils import draw_outputs
import config as cfg
import argparse
# from tensorflow_core.python.keras.models import load_model


def main(args):
    class_path = args.classes  # Path to classes file
    weights = args.weights  # Path to weight file
    image_size = cfg.IMAGE_SIZE  # Resize images to size - 416 04 608
    image = ''  # Path to input image
    tfrecord = args.dataset  # tfrecord instead of image or None
    output = args.output  # Path to output image
    num_classes = args.num_classes  # Number of classes in model

    anchors = cfg.YOLO_ANCHORS
    anchor_masks = cfg.YOLO_ANCHOR_MASKS

    physical_devices = tf.config.experimental.list_physical_devices('GPU')
    for physical_device in physical_devices:
        tf.config.experimental.set_memory_growth(physical_device, True)

    yolo = YoloV3(image_size, training=False, classes=num_classes)
    yolo.load_weights(weights).expect_partial()
    print('weights loaded')

    class_names = [c.strip() for c in open(class_path).readlines()]
    print('classes loaded')

    if tfrecord:
        val_dataset = load_tfrecord_dataset(tfrecord, class_path, image_size)
        # val_dataset = val_dataset.shuffle(512)
        val_dataset = val_dataset.batch(1)
        val_dataset = val_dataset.map(lambda x, y: (
            transform_images(x, image_size),
            transform_targets(y, anchors, anchor_masks, image_size)))
        # img_raw, _label = next(iter(dataset.take(1)))
    else:
        img_raw = tf.image.decode_image(
            open(image, 'rb').read(), channels=3)

    index = 0
    for img_raw, _label in val_dataset.take(25):
        # img = tf.expand_dims(img_raw, 0)
        img = transform_images(img_raw, image_size)
        img = img * 255

        boxes, scores, classes, nums = yolo(img)

        output = '/Users/justinbutler/Desktop/test/test_images/test_{}.jpg'.format(index)
        output = '/home/justin/Models/yolov3-tf2/test_images/test_{}.jpg'.format(index)
        print('output saved to: {}'.format(output))

        img = cv2.cvtColor(img_raw[0].numpy(), cv2.COLOR_RGB2BGR)
        img = draw_outputs(img, (boxes, scores, classes, nums), class_names, thresh=0)
        img = img * 255
        cv2.imwrite(output, img)

        index = index + 1


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--num_classes', default=80, type=int)
    parser.add_argument('--classes', help='path to classes .names file')
    parser.add_argument('--weights', default='checkpoints/yolov3_608.tf')
    parser.add_argument('--dataset', help='path to tfrecord file')
    parser.add_argument('--output')

    arguments = parser.parse_args()
    main(arguments)


import time
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import numpy as np
import tensorflow as tf
from yolov3_tf2.models import YoloV3, YoloV3Tiny
from yolov3_tf2.dataset import transform_images, load_tfrecord_dataset, transform_targets
from yolov3_tf2.utils import draw_outputs
import config as cfg
import argparse
# from tensorflow_core.python.keras.models import load_model


def main(args):
    class_path = args.classes  # Path to classes file
    weights = args.weights  # Path to weight file
    image_size = cfg.IMAGE_SIZE  # Resize images to size - 416 04 608
    image = ''  # Path to input image
    tfrecord = args.dataset  # tfrecord instead of image or None
    output = args.output  # Path to output image
    num_classes = args.num_classes  # Number of classes in model

    anchors = cfg.YOLO_ANCHORS
    anchor_masks = cfg.YOLO_ANCHOR_MASKS

    physical_devices = tf.config.experimental.list_physical_devices('GPU')
    for physical_device in physical_devices:
        tf.config.experimental.set_memory_growth(physical_device, True)

    yolo = YoloV3(image_size, training=False, classes=num_classes)
    yolo.load_weights(weights).expect_partial()
    print('weights loaded')

    class_names = [c.strip() for c in open(class_path).readlines()]
    print('classes loaded')

    if tfrecord:
        val_dataset = load_tfrecord_dataset(tfrecord, class_path, image_size)
        # val_dataset = val_dataset.shuffle(512)
        val_dataset = val_dataset.batch(1)
        val_dataset = val_dataset.map(lambda x, y: (
            transform_images(x, image_size),
            transform_targets(y, anchors, anchor_masks, image_size)))
        # img_raw, _label = next(iter(dataset.take(1)))
    else:
        img_raw = tf.image.decode_image(
            open(image, 'rb').read(), channels=3)

    index = 0
    for img_raw, _label in val_dataset.take(25):
        # img = tf.expand_dims(img_raw, 0)
        img = transform_images(img_raw, image_size)
        img = img * 255

        boxes, scores, classes, nums = yolo(img)

        output = '/Users/justinbutler/Desktop/test/test_images/test_{}.jpg'.format(index)
        output = '/home/justin/Models/yolov3-tf2/test_images/test_{}.jpg'.format(index)
        print('output saved to: {}'.format(output))

        img = cv2.cvtColor(img_raw[0].numpy(), cv2.COLOR_RGB2BGR)
        img = draw_outputs(img, (boxes, scores, classes, nums), class_names, thresh=0)
        img = img * 255
        cv2.imwrite(output, img)

        index = index + 1


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--num_classes', default=80, type=int)
    parser.add_argument('--classes', help='path to classes .names file')
    parser.add_argument('--weights', default='checkpoints/yolov3_608.tf')
    parser.add_argument('--dataset', help='path to tfrecord file')
    parser.add_argument('--output')

    arguments = parser.parse_args()
    main(arguments)
# from tensorflow_core.python.keras.models import load_model # Path to classes file # Path to weight file # Resize images to size - 416 04 608 # Path to input image # tfrecord instead of image or None # Path to output image # Number of classes in model # val_dataset = val_dataset.shuffle(512) # img_raw, _label = next(iter(dataset.take(1))) # img = tf.expand_dims(img_raw, 0)