 View on TensorFlow.org
|
 Run in Google Colab
|
 View on GitHub
|
 Download notebook
|
 See TF Hub models
|
This Colab demonstrates use of a TF-Hub module trained to perform object detection.
Setup
Imports and function definitions
# For running inference on the TF-Hub module.
import tensorflow as tf
import tensorflow_hub as hub
# For downloading the image.
import matplotlib.pyplot as plt
import tempfile
from six.moves.urllib.request import urlopen
from six import BytesIO
# For drawing onto the image.
import numpy as np
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
from PIL import ImageOps
# For measuring the inference time.
import time
# Print Tensorflow version
print(tf.__version__)
# Check available GPU devices.
print("The following GPU devices are available: %s" % tf.test.gpu_device_name())
2022-12-14 13:13:11.262549: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2022-12-14 13:13:11.262671: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2022-12-14 13:13:11.262682: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 2.11.0 The following GPU devices are available: /device:GPU:0
Example use
Helper functions for downloading images and for visualization.
Visualization code adapted from TF object detection API for the simplest required functionality.
def display_image(image):
fig = plt.figure(figsize=(20, 15))
plt.grid(False)
plt.imshow(image)
def download_and_resize_image(url, new_width=256, new_height=256,
display=False):
_, filename = tempfile.mkstemp(suffix=".jpg")
response = urlopen(url)
image_data = response.read()
image_data = BytesIO(image_data)
pil_image = Image.open(image_data)
pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS)
pil_image_rgb = pil_image.convert("RGB")
pil_image_rgb.save(filename, format="JPEG", quality=90)
print("Image downloaded to %s." % filename)
if display:
display_image(pil_image)
return filename
def draw_bounding_box_on_image(image,
ymin,
xmin,
ymax,
xmax,
color,
font,
thickness=4,
display_str_list=()):
"""Adds a bounding box to an image."""
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top),
(left, top)],
width=thickness,
fill=color)
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = top + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle([(left, text_bottom - text_height - 2 * margin),
(left + text_width, text_bottom)],
fill=color)
draw.text((left + margin, text_bottom - text_height - margin),
display_str,
fill="black",
font=font)
text_bottom -= text_height - 2 * margin
def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.1):
"""Overlay labeled boxes on an image with formatted scores and label names."""
colors = list(ImageColor.colormap.values())
try:
font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf",
25)
except IOError:
print("Font not found, using default font.")
font = ImageFont.load_default()
for i in range(min(boxes.shape[0], max_boxes)):
if scores[i] >= min_score:
ymin, xmin, ymax, xmax = tuple(boxes[i])
display_str = "{}: {}%".format(class_names[i].decode("ascii"),
int(100 * scores[i]))
color = colors[hash(class_names[i]) % len(colors)]
image_pil = Image.fromarray(np.uint8(image)).convert("RGB")
draw_bounding_box_on_image(
image_pil,
ymin,
xmin,
ymax,
xmax,
color,
font,
display_str_list=[display_str])
np.copyto(image, np.array(image_pil))
return image
Apply module
Load a public image from Open Images v4, save locally, and display.
# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg
image_url = "https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg"
downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True)
/tmpfs/tmp/ipykernel_79948/4241748128.py:14: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS) Image downloaded to /tmpfs/tmp/tmpurx5ocfv.jpg.
Pick an object detection module and apply on the downloaded image. Modules:
- FasterRCNN+InceptionResNet V2: high accuracy,
- ssd+mobilenet V2: small and fast.
module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"
detector = hub.load(module_handle).signatures['default']
INFO:tensorflow:Saver not created because there are no variables in the graph to restore INFO:tensorflow:Saver not created because there are no variables in the graph to restore
def load_img(path):
img = tf.io.read_file(path)
img = tf.image.decode_jpeg(img, channels=3)
return img
def run_detector(detector, path):
img = load_img(path)
converted_img = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]
start_time = time.time()
result = detector(converted_img)
end_time = time.time()
result = {key:value.numpy() for key,value in result.items()}
print("Found %d objects." % len(result["detection_scores"]))
print("Inference time: ", end_time-start_time)
image_with_boxes = draw_boxes(
img.numpy(), result["detection_boxes"],
result["detection_class_entities"], result["detection_scores"])
display_image(image_with_boxes)
run_detector(detector, downloaded_image_path)
2022-12-14 13:14:50.331745: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -2642 } dim { size: -2643 } dim { size: -2644 } dim { size: 1088 } } } inputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -22 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 17 } } device { type: "GPU" vendor: "NVIDIA" model: "Tesla P100-PCIE-16GB" frequency: 1328 num_cores: 56 environment { key: "architecture" value: "6.0" } environment { key: "cuda" value: "11020" } environment { key: "cudnn" value: "8100" } num_registers: 65536 l1_cache_size: 24576 l2_cache_size: 4194304 shared_memory_size_per_multiprocessor: 65536 memory_size: 16025321472 bandwidth: 732160000 } outputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: 17 } dim { size: 17 } dim { size: 1088 } } }
Found 100 objects.
Inference time: 44.401554584503174
/tmpfs/tmp/ipykernel_79948/4241748128.py:45: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead.
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
/tmpfs/tmp/ipykernel_79948/4241748128.py:55: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead.
text_width, text_height = font.getsize(display_str)
More images
Perform inference on some additional images with time tracking.
image_urls = [
# Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_(Plate_125)_(8592917784).jpg
"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
"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
"https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.jpg",
]
def detect_img(image_url):
start_time = time.time()
image_path = download_and_resize_image(image_url, 640, 480)
run_detector(detector, image_path)
end_time = time.time()
print("Inference time:",end_time-start_time)
detect_img(image_urls[0])
/tmpfs/tmp/ipykernel_79948/4241748128.py:14: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS) Image downloaded to /tmpfs/tmp/tmptzo7iumj.jpg. Found 100 objects. Inference time: 1.5392217636108398 Inference time: 1.7818574905395508 /tmpfs/tmp/ipykernel_79948/4241748128.py:45: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] /tmpfs/tmp/ipykernel_79948/4241748128.py:55: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. text_width, text_height = font.getsize(display_str)
detect_img(image_urls[1])
/tmpfs/tmp/ipykernel_79948/4241748128.py:14: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS) Image downloaded to /tmpfs/tmp/tmpxc7qj2da.jpg. Found 100 objects. Inference time: 0.9413907527923584 Inference time: 1.1664648056030273 /tmpfs/tmp/ipykernel_79948/4241748128.py:45: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] /tmpfs/tmp/ipykernel_79948/4241748128.py:55: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. text_width, text_height = font.getsize(display_str)
detect_img(image_urls[2])
/tmpfs/tmp/ipykernel_79948/4241748128.py:14: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS) Image downloaded to /tmpfs/tmp/tmpm1_7q7gq.jpg. Found 100 objects. Inference time: 0.9293255805969238 Inference time: 1.2488842010498047 /tmpfs/tmp/ipykernel_79948/4241748128.py:45: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] /tmpfs/tmp/ipykernel_79948/4241748128.py:55: DeprecationWarning: getsize is deprecated and will be removed in Pillow 10 (2023-07-01). Use getbbox or getlength instead. text_width, text_height = font.getsize(display_str)
