TensorFlow Object Detection API simplified sample code for inference

tensorflow_object_detection_api_test.py

from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import os

from object_detection.utils import label_map_util
from object_detection.utils import config_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder


center_net_path = './centernet_resnet50_v1_fpn_512x512_coco17_tpu-8/'
pipeline_config = center_net_path + 'pipeline.config'
model_path = center_net_path + 'checkpoint/'
label_map_path = './mscoco_label_map.pbtxt'
image_path = './test.jpg'

# Load pipeline config and build a detection model
configs = config_util.get_configs_from_pipeline_file(pipeline_config)
model_config = configs['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)

# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(model_path, 'ckpt-0')).expect_partial()

def get_model_detection_function(model):
    @tf.function
    def detect_fn(image):
        image, shapes = model.preprocess(image)
        prediction_dict = model.predict(image, shapes)
        detections = model.postprocess(prediction_dict, shapes)

        return detections, prediction_dict, tf.reshape(shapes, [-1])
    return detect_fn

detect_fn = get_model_detection_function(detection_model)



label_map_path = label_map_path
label_map = label_map_util.load_labelmap(label_map_path)
categories = label_map_util.convert_label_map_to_categories(
    label_map,
    max_num_classes=label_map_util.get_max_label_map_index(label_map),
    use_display_name=True)
category_index = label_map_util.create_category_index(categories)
label_map_dict = label_map_util.get_label_map_dict(label_map, use_display_name=True)


image = np.array(Image.open(image_path))
input_tensor = tf.convert_to_tensor(np.expand_dims(image, 0), dtype=tf.float32)
detections, predictions_dict, shapes = detect_fn(input_tensor)

label_id_offset = 1
image_np_with_detections = image.copy()

# Use keypoints if available in detections
keypoints, keypoint_scores = None, None
if 'detection_keypoints' in detections:
    keypoints = detections['detection_keypoints'][0].numpy()
    keypoint_scores = detections['detection_keypoint_scores'][0].numpy()

def get_keypoint_tuples(eval_config):
    tuple_list = []
    kp_list = eval_config.keypoint_edge
    for edge in kp_list:
        tuple_list.append((edge.start, edge.end))
    return tuple_list

viz_utils.visualize_boxes_and_labels_on_image_array(
    image_np_with_detections,
    detections['detection_boxes'][0].numpy(),
    (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
    detections['detection_scores'][0].numpy(),
    category_index,
    use_normalized_coordinates=True,
    max_boxes_to_draw=200,
    min_score_thresh=.30,
    agnostic_mode=False,
    keypoints=keypoints,
    keypoint_scores=keypoint_scores,
    keypoint_edges=get_keypoint_tuples(configs['eval_config']))

plt.figure(figsize=(12,16))
plt.imshow(image_np_with_detections)
plt.savefig('./output.png')
plt.show()

Have you fixed it? I can't find your comment on GitHub. This gist is part of a tutorial I wrote on Medium, here is a link to it https://towardsdatascience.com/how-to-install-tensorflow-2-object-detection-api-on-windows-2eef9b7ae869?sk=a323d2d0aa65b73ff61a00aeb0d69952 From the error message, I assume you didn't download the centernet model. Let me know if you need any help

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On Tue, Dec 1, 2020 at 3:52 PM Ivan ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ why when i try to run it appear the following message: `---> 23 detection_model = model_builder.build(model_config=model_config, is_training=False) 24 25 # Restore checkpoint 4 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/lib/io/file_io.py in _preread_check(self) 77 "File isn't open for reading") 78 self._read_buf = _pywrap_file_io.BufferedInputStream( ---> 79 self.__name, 1024 * 512) 80 81 def _prewrite_check(self): NotFoundError: PATH_TO_BE_CONFIGURED; No such file or directory` — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/ee3dc19242915278e95cb75014e29083#gistcomment-3546486>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AA7S5DWCNKGAR24VJVNBEGTSSU3OTANCNFSM4UJMOU2A> .

I'm trying to get the object predictions before inference the class of every region. I think that with your code i can get my goal but i'm not sure

What do you mean before inference? Do you mean the raw algorithm output, without any post-processing?

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On Tue, Dec 1, 2020 at 7:00 PM Ivan ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ I'm trying to get the object predictions before inference the class of every region. I think that with your code i can get my goal but i'm not sure — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/ee3dc19242915278e95cb75014e29083#gistcomment-3546657>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AA7S5DWBYXE4ZMSJUBSPBUTSSVRO5ANCNFSM4UJMOU2A> .

I need to get the elements or regions before the class inference, i mean all the regions proposed by the model before to the class detection in every one of them. I need to access to al regions proposed to make some changes or insert new regions and then get the class of every one of them.

I see, you need the raw, unfiltered results. For that, I recommend you checking the official docs. In the sample notebook, there is a section called "Digging into the model's intermediate predictions", which cover exactly what you want. You can find it here: https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/inference_tf2_colab.ipynb In particular, this snippet: class_index = label_map_dict[class_name] class_center_logits = predictions_dict['object_center'][0] class_center_logits = class_center_logits[0][ :, :, class_index - label_id_offset] heatmap = tf.exp(class_center_logits) / (tf.exp(class_center_logits) + 1) return heatmap

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On Tue, Dec 1, 2020 at 7:21 PM Ivan ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ I need to get the elements or regions before the class inference, i mean all the regions proposed by the model before to the class detection in every one of them. I need to access to al regions proposed to make some changes or insert new regions and then get the class of every one of them. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/ee3dc19242915278e95cb75014e29083#gistcomment-3546684>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AA7S5DRNZIUE23MR3S7WBATSSVT73ANCNFSM4UJMOU2A> .

I will check it. I think that could work.

Thanks for help me nuzrub ; )

I warn you, however, that the "inspection code" of the example is specific to centernet models and is likely to fail for others, such as RCNNs and SSDs. Object detection models are quite messy, there is hardly much code reuse across implementations. This code certainly might be useful for rolling out the needed implementation for other detector families, but I wouldn't expect it to be a silver bullet by any means.

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On Tue, Dec 1, 2020 at 7:30 PM Ivan ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ I will check it. I think that could work. Thanks for help me nuzrub ; ) — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/ee3dc19242915278e95cb75014e29083#gistcomment-3546694>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AA7S5DQHIG4EQHW3VHF2B4DSSVVA3ANCNFSM4UJMOU2A> .

in this case I am working with centernet but I will take it into account in case I need other alternative models.

All good then, good work o/

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On Tue, Dec 1, 2020 at 7:35 PM Ivan ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ in this case I am working with centernet but I will take it into account in case I need other alternative models. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/ee3dc19242915278e95cb75014e29083#gistcomment-3546698>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AA7S5DQJ3EFHELFDVT7J3W3SSVVUBANCNFSM4UJMOU2A> .

First, I would like to say thank you for the easiest to follow tutorial on Medium/TowardsDataScience.

In Line 18 label_map_path = './coco_labelmap.pbtxt' should be '...mscoco_label_map.pbtxt'. The label map file must have been updated.

First, I would like to say thank you for the easiest to follow tutorial on Medium/TowardsDataScience.

In Line 18 label_map_path = './coco_labelmap.pbtxt' should be '...mscoco_label_map.pbtxt'. The label map file must have been updated.

Thanks for enjoying it. I fixed the label map path, thanks :)