#Tensorflow official tutorial - beach image
#https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_object_detection.ipynb
# Clone the tensorflow models repository
!git clone --depth 1 https://github.com/tensorflow/models
%%bash
sudo apt install -y protobuf-compiler
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
import os
import pathlib
import matplotlib
import matplotlib.pyplot as plt
import io
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
from six.moves.urllib.request import urlopen
import tensorflow as tf
import tensorflow_hub as hub
tf.get_logger().setLevel('ERROR')
# @title Run this!!
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: the file path to the image
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
image = None
if(path.startswith('http')):
response = urlopen(path)
image_data = response.read()
image_data = BytesIO(image_data)
image = Image.open(image_data)
else:
image_data = tf.io.gfile.GFile(path, 'rb').read()
image = Image.open(BytesIO(image_data))
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(1, im_height, im_width, 3)).astype(np.uint8)
ALL_MODELS = {
'CenterNet HourGlass104 512x512' :
'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1',
'CenterNet HourGlass104 Keypoints 512x512' :
'https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1',
'CenterNet HourGlass104 1024x1024' :
'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024/1',
'CenterNet HourGlass104 Keypoints 1024x1024' :
'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024_kpts/1',
'CenterNet Resnet50 V1 FPN 512x512' :
'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1',
'CenterNet Resnet50 V1 FPN Keypoints 512x512' :
'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512_kpts/1',
'CenterNet Resnet101 V1 FPN 512x512' :
'https://tfhub.dev/tensorflow/centernet/resnet101v1_fpn_512x512/1',
'CenterNet Resnet50 V2 512x512' :
'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512/1',
'CenterNet Resnet50 V2 Keypoints 512x512' :
'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512_kpts/1',
'EfficientDet D0 512x512' : 'https://tfhub.dev/tensorflow/efficientdet/d0/1',
'EfficientDet D1 640x640' : 'https://tfhub.dev/tensorflow/efficientdet/d1/1',
'EfficientDet D2 768x768' : 'https://tfhub.dev/tensorflow/efficientdet/d2/1',
'EfficientDet D3 896x896' : 'https://tfhub.dev/tensorflow/efficientdet/d3/1',
'EfficientDet D4 1024x1024' : 'https://tfhub.dev/tensorflow/efficientdet/d4/1',
'EfficientDet D5 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d5/1',
'EfficientDet D6 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d6/1',
'EfficientDet D7 1536x1536' : 'https://tfhub.dev/tensorflow/efficientdet/d7/1',
'SSD MobileNet v2 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2',
'SSD MobileNet V1 FPN 640x640' :
'https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1',
'SSD MobileNet V2 FPNLite 320x320' :
'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1',
'SSD MobileNet V2 FPNLite 640x640' :
'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1',
'SSD ResNet50 V1 FPN 640x640 (RetinaNet50)' :
'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_640x640/1',
'SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)' :
'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_1024x1024/1',
'SSD ResNet101 V1 FPN 640x640 (RetinaNet101)' :
'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_640x640/1',
'SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)' :
'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_1024x1024/1',
'SSD ResNet152 V1 FPN 640x640 (RetinaNet152)' :
'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_640x640/1',
'SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)' :
'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_1024x1024/1',
'Faster R-CNN ResNet50 V1 640x640' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1',
'Faster R-CNN ResNet50 V1 1024x1024' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_1024x1024/1',
'Faster R-CNN ResNet50 V1 800x1333' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_800x1333/1',
'Faster R-CNN ResNet101 V1 640x640' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_640x640/1',
'Faster R-CNN ResNet101 V1 1024x1024' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_1024x1024/1',
'Faster R-CNN ResNet101 V1 800x1333' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_800x1333/1',
'Faster R-CNN ResNet152 V1 640x640' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_640x640/1',
'Faster R-CNN ResNet152 V1 1024x1024' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_1024x1024/1',
'Faster R-CNN ResNet152 V1 800x1333' :
'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_800x1333/1',
'Faster R-CNN Inception ResNet V2 640x640' :
'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1',
'Faster R-CNN Inception ResNet V2 1024x1024' :
'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_1024x1024/1',
'Mask R-CNN Inception ResNet V2 1024x1024' :
'https://tfhub.dev/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1'
}
IMAGES_FOR_TEST = {
'Beach' : 'models/research/object_detection/test_images/image2.jpg',
'Dogs' : 'models/research/object_detection/test_images/image1.jpg',
# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg
'Naxos Taverna' :
'https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg',
# Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_
(Plate_125)_(8592917784).jpg
'Beatles' :
'https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_
%28Plate_125%29_%288592917784%29.jpg',
# By Américo Toledano, Source: https://commons.wikimedia.org/wiki/File:Biblioteca_Maim
%C3%B3nides,_Campus_Universitario_de_Rabanales_007.jpg
'Phones' : 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Biblioteca_Maim
%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg/1024px-Biblioteca_Maim%C3%B3nides
%2C_Campus_Universitario_de_Rabanales_007.jpg',
# Source: https://commons.wikimedia.org/wiki/File:The_smaller_British_birds_
(8053836633).jpg
'Birds' :
'https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_
%288053836633%29.jpg',
}
COCO17_HUMAN_POSE_KEYPOINTS = [(0, 1),
(0, 2),
(1, 3),
(2, 4),
(0, 5),
(0, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(5, 6),
(5, 11),
(6, 12),
(11, 12),
(11, 13),
(13, 15),
(12, 14),
(14, 16)]
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import ops as utils_ops
%matplotlib inline
PATH_TO_LABELS = './models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
use_display_name=True)
#@title Model Selection { display-mode: "form", run: "auto" }
model_display_name = 'CenterNet HourGlass104 Keypoints 512x512' # @param ['CenterNet
HourGlass104 512x512','CenterNet HourGlass104 Keypoints 512x512','CenterNet HourGlass104
1024x1024','CenterNet HourGlass104 Keypoints 1024x1024','CenterNet Resnet50 V1 FPN
512x512','CenterNet Resnet50 V1 FPN Keypoints 512x512','CenterNet Resnet101 V1 FPN
512x512','CenterNet Resnet50 V2 512x512','CenterNet Resnet50 V2 Keypoints
512x512','EfficientDet D0 512x512','EfficientDet D1 640x640','EfficientDet D2
768x768','EfficientDet D3 896x896','EfficientDet D4 1024x1024','EfficientDet D5
1280x1280','EfficientDet D6 1280x1280','EfficientDet D7 1536x1536','SSD MobileNet v2
320x320','SSD MobileNet V1 FPN 640x640','SSD MobileNet V2 FPNLite 320x320','SSD MobileNet
V2 FPNLite 640x640','SSD ResNet50 V1 FPN 640x640 (RetinaNet50)','SSD ResNet50 V1 FPN
1024x1024 (RetinaNet50)','SSD ResNet101 V1 FPN 640x640 (RetinaNet101)','SSD ResNet101 V1
FPN 1024x1024 (RetinaNet101)','SSD ResNet152 V1 FPN 640x640 (RetinaNet152)','SSD ResNet152
V1 FPN 1024x1024 (RetinaNet152)','Faster R-CNN ResNet50 V1 640x640','Faster R-CNN ResNet50
V1 1024x1024','Faster R-CNN ResNet50 V1 800x1333','Faster R-CNN ResNet101 V1
640x640','Faster R-CNN ResNet101 V1 1024x1024','Faster R-CNN ResNet101 V1 800x1333','Faster
R-CNN ResNet152 V1 640x640','Faster R-CNN ResNet152 V1 1024x1024','Faster R-CNN ResNet152
V1 800x1333','Faster R-CNN Inception ResNet V2 640x640','Faster R-CNN Inception ResNet V2
1024x1024','Mask R-CNN Inception ResNet V2 1024x1024']
model_handle = ALL_MODELS[model_display_name]
print('Selected model:'+ model_display_name)
print('Model Handle at TensorFlow Hub: {}'.format(model_handle))
print('loading model...')
hub_model = hub.load(model_handle)
print('model loaded!')
#@title Image Selection (don't forget to execute the cell!) { display-mode: "form"}
selected_image = 'Beach' # @param ['Beach', 'Dogs', 'Naxos Taverna', 'Beatles', 'Phones',
'Birds']
flip_image_horizontally = False #@param {type:"boolean"}
convert_image_to_grayscale = False #@param {type:"boolean"}
image_path = IMAGES_FOR_TEST[selected_image]
image_np = load_image_into_numpy_array(image_path)
# Flip horizontally
if(flip_image_horizontally):
image_np[0] = np.fliplr(image_np[0]).copy()
# Convert image to grayscale
if(convert_image_to_grayscale):
image_np[0] = np.tile(
np.mean(image_np[0], 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
plt.figure(figsize=(24,32))
plt.imshow(image_np[0])
plt.show()
# running inference
results = hub_model(image_np)
# different object detection models have additional results
# all of them are explained in the documentation
result = {key:value.numpy() for key,value in results.items()}
print(result.keys())
label_id_offset = 0
image_np_with_detections = image_np.copy()
# Use keypoints if available in detections
keypoints, keypoint_scores = None, None
if 'detection_keypoints' in result:
keypoints = result['detection_keypoints'][0]
keypoint_scores = result['detection_keypoint_scores'][0]
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
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=COCO17_HUMAN_POSE_KEYPOINTS)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_detections[0])
plt.show()
# Handle models with masks:
image_np_with_mask = image_np.copy()
if 'detection_masks' in result:
# we need to convert np.arrays to tensors
detection_masks = tf.convert_to_tensor(result['detection_masks'][0])
detection_boxes = tf.convert_to_tensor(result['detection_boxes'][0])
# Reframe the the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes,
image_np.shape[1], image_np.shape[2])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
tf.uint8)
result['detection_masks_reframed'] = detection_masks_reframed.numpy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_mask[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False,
instance_masks=result.get('detection_masks_reframed', None),
line_thickness=8)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_mask[0])
plt.show()
x
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