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ऑब्जेक्ट डिटेक्शन

TensorFlow.org पर देखें Google Colab में चलाएं गिटहब पर देखें नोटबुक डाउनलोड करें TF हब मॉडल देखें

यह कोलाब एक टीएफ-हब मॉड्यूल के उपयोग का प्रदर्शन करता है जिसे ऑब्जेक्ट डिटेक्शन करने के लिए प्रशिक्षित किया जाता है।

सेट अप

आयात और कार्य की परिभाषा

# 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())
2.3.1
The following GPU devices are available: /device:GPU:0

उदाहरण का उपयोग करें

चित्र डाउनलोड करने और विज़ुअलाइज़ेशन के लिए सहायक कार्य करता है।

विज़ुअलाइज़ेशन कोड को सरलतम आवश्यक कार्यक्षमता के लिए TF ऑब्जेक्ट डिटेक्शन एपीआई से अनुकूलित किया गया है।

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

मॉड्यूल लागू करें

ओपन इमेज v4 से एक सार्वजनिक छवि लोड करें, स्थानीय रूप से सहेजें और प्रदर्शित करें।

# 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 /tmp/tmp490wdgdc.jpg.

पींग

ऑब्जेक्ट डिटेक्शन मॉड्यूल चुनें और डाउनलोड की गई छवि पर लागू करें। मॉड्यूल:

  • FasterRCNN + InceptionResNet V2 : उच्च सटीकता,
  • ssd + mobilenet V2 : छोटा और तेज।
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)
Found 100 objects.
Inference time:  32.809327602386475

पींग

अधिक छवियां

समय पर नज़र रखने के साथ कुछ अतिरिक्त छवियों पर आक्षेप करें।

03bbb2220
detect_img(image_urls[0])
Image downloaded to /tmp/tmpo6r5d7cz.jpg.
Found 100 objects.
Inference time:  1.0424823760986328
Inference time: 1.6992628574371338

पींग

detect_img(image_urls[1])
Image downloaded to /tmp/tmp3qiy5or2.jpg.
Found 100 objects.
Inference time:  0.7707986831665039
Inference time: 1.1541223526000977

पींग

detect_img(image_urls[2])
Image downloaded to /tmp/tmp7cxe1qu9.jpg.
Found 100 objects.
Inference time:  0.7675492763519287
Inference time: 1.313662052154541

पींग