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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())
2025-07-11 12:14:11.027581: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1752236051.052461 65659 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752236051.060050 65659 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1752236051.079263 65659 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752236051.079296 65659 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752236051.079299 65659 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752236051.079302 65659 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_hub/__init__.py:61: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. from pkg_resources import parse_version 2.19.0 The following GPU devices are available: 2025-07-11 12:14:14.772401: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
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.LANCZOS)
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.getbbox(ds)[3] 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]:
bbox = font.getbbox(display_str)
text_width, text_height = bbox[2], bbox[3]
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)
Image downloaded to /tmpfs/tmp/tmpql93_kgt.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)
W0000 00:00:1752236146.617189 65659 op_level_cost_estimator.cc:699] 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: -2484 } dim { size: -2485 } dim { size: -2486 } dim { size: 1088 } } } inputs { dtype: DT_FLOAT shape { dim { size: -105 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -105 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 17 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2299 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 47185920 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -105 } dim { size: 17 } dim { size: 17 } dim { size: 1088 } } }
Found 100 objects.
Inference time: 41.18216371536255
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])
Image downloaded to /tmpfs/tmp/tmp4m0u0ira.jpg. Found 100 objects. Inference time: 2.984281539916992 Inference time: 3.2401974201202393
detect_img(image_urls[1])
Image downloaded to /tmpfs/tmp/tmp91z__xy7.jpg. Found 100 objects. Inference time: 2.9305200576782227 Inference time: 3.188967227935791
detect_img(image_urls[2])
Image downloaded to /tmpfs/tmp/tmpg6uxzhcv.jpg. Found 100 objects. Inference time: 2.9831604957580566 Inference time: 3.358455181121826
