From 73a968d515d0570dbbfa525466825be07cc426f7 Mon Sep 17 00:00:00 2001 From: Feng Xiaoqi Date: Thu, 25 Oct 2018 10:35:22 +0800 Subject: [PATCH 1/2] Mask RCNN: update script & png files --- Custom_Mask_RCNN/annotations/masks/toy13.png | Bin 1902 -> 1140 bytes Custom_Mask_RCNN/annotations/masks/toy16.png | Bin 1929 -> 1226 bytes Custom_Mask_RCNN/annotations/masks/toy19.png | Bin 1617 -> 1154 bytes Custom_Mask_RCNN/annotations/masks/toy20.png | Bin 2874 -> 1090 bytes Custom_Mask_RCNN/annotations/masks/toy21.png | Bin 1957 -> 1241 bytes Custom_Mask_RCNN/annotations/masks/toy22.png | Bin 3683 -> 1287 bytes Custom_Mask_RCNN/annotations/masks/toy23.png | Bin 1699 -> 1305 bytes Custom_Mask_RCNN/annotations/masks/toy25.png | Bin 1646 -> 1267 bytes Custom_Mask_RCNN/annotations/masks/toy26.png | Bin 1964 -> 1254 bytes Custom_Mask_RCNN/annotations/masks/toy27.png | Bin 1514 -> 1204 bytes Custom_Mask_RCNN/annotations/masks/toy28.png | Bin 4776 -> 1498 bytes Custom_Mask_RCNN/annotations/masks/toy29.png | Bin 1843 -> 1421 bytes Custom_Mask_RCNN/annotations/masks/toy30.png | Bin 2126 -> 1440 bytes Custom_Mask_RCNN/annotations/masks/toy31.png | Bin 2069 -> 1431 bytes Custom_Mask_RCNN/annotations/masks/toy32.png | Bin 4513 -> 1520 bytes Custom_Mask_RCNN/annotations/masks/toy33.png | Bin 2011 -> 1333 bytes Custom_Mask_RCNN/annotations/masks/toy34.png | Bin 1644 -> 1194 bytes Custom_Mask_RCNN/annotations/masks/toy35.png | Bin 1628 -> 1293 bytes Custom_Mask_RCNN/annotations/masks/toy4.png | Bin 5011 -> 2806 bytes Custom_Mask_RCNN/annotations/masks/toy5.png | Bin 1951 -> 1520 bytes Custom_Mask_RCNN/annotations/masks/toy6.png | Bin 2132 -> 1433 bytes Custom_Mask_RCNN/annotations/masks/toy7.png | Bin 1897 -> 1156 bytes Custom_Mask_RCNN/annotations/masks/toy8.png | Bin 2157 -> 1420 bytes Custom_Mask_RCNN/create_pet_tf_record.py | 24 +++++++++---------- 24 files changed, 11 insertions(+), 13 deletions(-) diff --git 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z7*jh(^E?@BO7X@9Wj^N+HD&m?X9gQJB1)jt0g!&Tq(u?c$)|I;auaGY>v0SVO+KAu z^aIgjglkGg@n7BFONQ(Lm?32_DK%EC`qp-Tuw~ON4>v(s$MZ9zpA=d0>kX#bnOWIW zMt3=~aO@ zD)3c%Q4i%WFq2{qE^v_qA0yQ$0;PJNNX`%{QX{=s&tAf%w4 z=9+9dk~cc7i1I;=(G5}t(b+`56?KAf+929j{|xRk3wZ>r`KkGKlzsC~v6jL41oMB~ z@c?U_g@{j>=X`3obUBrKw>30N)LpIMKcyH@(_So4L_j+eQXKwMBogmt Date: Wed, 31 Oct 2018 16:15:40 +0800 Subject: [PATCH 2/2] Mask RCNN: add test.py & label_map.pbtxt Signed-off-by: Feng Xiaoqi --- Custom_Mask_RCNN/create_pet_tf_record.py | 2 +- Custom_Mask_RCNN/label_map.pbtxt | 4 + Custom_Mask_RCNN/test.py | 156 +++++++++++++++++++++++ 3 files changed, 161 insertions(+), 1 deletion(-) create mode 100644 Custom_Mask_RCNN/label_map.pbtxt create mode 100644 Custom_Mask_RCNN/test.py diff --git a/Custom_Mask_RCNN/create_pet_tf_record.py b/Custom_Mask_RCNN/create_pet_tf_record.py index 91a2a224..1dbd062e 100644 --- a/Custom_Mask_RCNN/create_pet_tf_record.py +++ b/Custom_Mask_RCNN/create_pet_tf_record.py @@ -257,7 +257,7 @@ def create_tf_record(output_filename, def main(_): data_dir = FLAGS.data_dir #label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) - label_map_dict = label_map_util.get_label_map_dict('/tmp/label_map.pbtxt') + label_map_dict = label_map_util.get_label_map_dict('./label_map.pbtxt') logging.info('Reading from Pet dataset.') image_dir = os.path.join(data_dir, 'images') diff --git a/Custom_Mask_RCNN/label_map.pbtxt b/Custom_Mask_RCNN/label_map.pbtxt new file mode 100644 index 00000000..b0f86ba1 --- /dev/null +++ b/Custom_Mask_RCNN/label_map.pbtxt @@ -0,0 +1,4 @@ +item { + id: 1 + name: 'toy' +} diff --git a/Custom_Mask_RCNN/test.py b/Custom_Mask_RCNN/test.py new file mode 100644 index 00000000..cea0771e --- /dev/null +++ b/Custom_Mask_RCNN/test.py @@ -0,0 +1,156 @@ +import numpy as np +import os +import six.moves.urllib as urllib +import sys +import tarfile +import tensorflow as tf +import zipfile +import datetime +#import cv2 + +from collections import defaultdict +from io import StringIO + +import matplotlib +matplotlib.use('Agg') + +from matplotlib import pyplot as plt +from PIL import Image + + +# This is needed since the notebook is stored in the object_detection folder. +sys.path.append("..") +from object_detection.utils import ops as utils_ops + +#if tf.__version__ < '1.4.0': +# raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!') + +# This is needed to display the images. +#%matplotlib inline + +from utils import label_map_util + +from utils import visualization_utils as vis_util + +# What model to download. +MODEL_NAME = 'out4' +#MODEL_NAME = 'mask_rcnn_inception_v2_coco_2018_01_28' +#MODEL_FILE = MODEL_NAME + '.tar.gz' + +# Path to frozen detection graph. This is the actual model that is used for the object detection. +PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' + +# List of the strings that is used to add correct label for each box. +#PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') +PATH_TO_LABELS = 'label_map.pbtxt' +NUM_CLASSES = 1 + +detection_graph = tf.Graph() +with detection_graph.as_default(): + od_graph_def = tf.GraphDef() + with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: + serialized_graph = fid.read() + od_graph_def.ParseFromString(serialized_graph) + tf.import_graph_def(od_graph_def, name='') + +label_map = label_map_util.load_labelmap(PATH_TO_LABELS) +categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) +category_index = label_map_util.create_category_index(categories) + +def load_image_into_numpy_array(image): + (im_width, im_height) = image.size + return np.array(image.getdata()).reshape( + (im_height, im_width, 3)).astype(np.uint8) + +# For the sake of simplicity we will use only 2 images: +# image1.jpg +# image2.jpg +# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. +#PATH_TO_TEST_IMAGES_DIR = 'test_images' +#TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 7) ] +img_path='images' +TEST_IMAGE_PATHS = os.listdir(img_path) +os.chdir(img_path) + +# Size, in inches, of the output images. +IMAGE_SIZE = (12, 8) + +def run_inference_for_single_image(image, graph): + with graph.as_default(): + with tf.Session() as sess: + # Get handles to input and output tensors + ops = tf.get_default_graph().get_operations() + all_tensor_names = {output.name for op in ops for output in op.outputs} + tensor_dict = {} + for key in [ + 'num_detections', 'detection_boxes', 'detection_scores', + 'detection_classes', 'detection_masks' + ]: + tensor_name = key + ':0' + if tensor_name in all_tensor_names: + tensor_dict[key] = tf.get_default_graph().get_tensor_by_name( + tensor_name) + if 'detection_masks' in tensor_dict: + # The following processing is only for single image + detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0]) + detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0]) + # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size. + real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32) + detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1]) + detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1]) + detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks( + detection_masks, detection_boxes, image.shape[0], image.shape[1]) + detection_masks_reframed = tf.cast( + tf.greater(detection_masks_reframed, 0.1), tf.uint8) + # Follow the convention by adding back the batch dimension + tensor_dict['detection_masks'] = tf.expand_dims( + detection_masks_reframed, 0) + image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0') + + # Run inference + print("start") + b = datetime.datetime.now() + output_dict = sess.run(tensor_dict, + feed_dict={image_tensor: np.expand_dims(image, 0)}) + + e = datetime.datetime.now() + k = e - b + print("%f ms" % (k.total_seconds()*1000)) + # all outputs are float32 numpy arrays, so convert types as appropriate + output_dict['num_detections'] = int(output_dict['num_detections'][0]) + output_dict['detection_classes'] = output_dict[ + 'detection_classes'][0].astype(np.uint8) + output_dict['detection_boxes'] = output_dict['detection_boxes'][0] + output_dict['detection_scores'] = output_dict['detection_scores'][0] + if 'detection_masks' in output_dict: + output_dict['detection_masks'] = output_dict['detection_masks'][0] + return output_dict + +#for image_path in TEST_IMAGE_PATHS: +#image = Image.open(image_path) +image_path='/home/xiaoqife/models/models-master/research/object_detection/images/toy6.jpg' +image = Image.open(image_path) + +print(image_path) +# the array based representation of the image will be used later in order to prepare the +# result image with boxes and labels on it. +image_np = load_image_into_numpy_array(image) +# Expand dimensions since the model expects images to have shape: [1, None, None, 3] +image_np_expanded = np.expand_dims(image_np, axis=0) +# Actual detection. +output_dict = run_inference_for_single_image(image_np, detection_graph) +# Visualization of the results of a detection. +vis_util.visualize_boxes_and_labels_on_image_array( + image_np, + output_dict['detection_boxes'], + output_dict['detection_classes'], + output_dict['detection_scores'], + category_index, + instance_masks=output_dict.get('detection_masks'), + use_normalized_coordinates=True, + line_thickness=8) +plt.figure(figsize=IMAGE_SIZE) +filename = os.path.split(image_path)[1] +plt.imshow(image_np) +plt.show() +plt.savefig(filename + '.jpg')