Path: blob/master/site/en-snapshot/hub/tutorials/object_detection.ipynb
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Kernel: Python 3
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
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# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================
Object Detection
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Run in Google Colab
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Download notebook
See TF Hub modelsThis Colab demonstrates use of a TF-Hub module trained to perform object detection.
Setup
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#@title 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())
Example use
Helper functions for downloading images and for visualization.
Visualization code adapted from TF object detection API for the simplest required functionality.
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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.
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# 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" #@param downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True)
Pick an object detection module and apply on the downloaded image. Modules:
FasterRCNN+InceptionResNet V2: high accuracy,
ssd+mobilenet V2: small and fast.
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module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1" #@param ["https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1", "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"] detector = hub.load(module_handle).signatures['default']
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def load_img(path): img = tf.io.read_file(path) img = tf.image.decode_jpeg(img, channels=3) return img
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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)
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run_detector(detector, downloaded_image_path)
More images
Perform inference on some additional images with time tracking.
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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)
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detect_img(image_urls[0])
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detect_img(image_urls[1])
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detect_img(image_urls[2])