MAGNETRON ™ TECHNOLOGY_ ARTIFICIAL INTELLIGENCE 2.0 ™ - INSTINCTIVE MIND PROXIA (OBJECT DETECTION IMAGES).ipynb

{"cells":[{"cell_type":"markdown","source":["# THE ABC 123 GROUP ™:\n","\n","- GENERAL CONSULTING ABC 123 BY OSAROPRIME ™.\n","- ABC 123 USA ™\n","- ABC 123 DESYGN ™\n","- ABC 123 FILMS ™\n","\n","=============================================================\n","\n","\n","> **ARTIFICIAL INTELLIGENCE 2.0 ™ : OBJECT DETECTION B**\n","\n","\n",">>>> *️⃣📶🤖\n","\n","\n","=============================================================\n","\n","This Google Colab **NOTEBOOK** will guide you on one possible scheme to create an **OBJECT DETECTION PROXIA** in the **ARTIFICIAL INTELLIGENCE 2.0**™ FRAMEWORK/DOCUMENTATION. This notebook will guide you on using **OBJECT DETECTION** on **IMAGES** for making **PROXIA** to be used on **MINDCLOUDS**. **OBJECT DETECTION** on IMAGES will be reffered to as **OBJECT DETECTION B**. This is useful in **ARTIFICIAL INTELLIGENCE 2.0 ™** as far as helping robots in understanding IMAGES encountered on the internet. This will be on the **INSTINCTIVE MIND MINDCLOUD** and **INFORMATION** from this **PROXIA** must go to other **PROXIA** for processing e.g **LOGIC/ASTRAL MIND MINDCLOUD PROXIA**. \n","\n"," \n","\n","NOTE:  **ARTIFICIAL INTELLIGENCE 2.0**™ IS PART OF  **MAGNETRON**™  **TECHNOLOGY**.\n","\n","NOTE : REMEMBER A **PROXIA** IS THE EQUIVALENT OF AN APP IN iOS APP DEVELOPMENT (THE iPHONE ITSELD WOULD BE A **MINDCLOUD** with the PROXIA stored on it (refer to **ARTIFICIAL INTELLIGENCE 2.0 ™** DOCUMENTATION ONLINE). \n","CLICK HERE FOR **ARTIFICIAL INTELLIGENCE 2.0 ™** DOCUMENTATION ON FACEBOOK: \n","\n","[ARTIFICIAL INTELLIGENCE PRIMER ™ DOCUMENTATION](https://www.facebook.com/artificialintelligenceprimer)\n","\n","[ARTIFICIAL INTELLIGENCE 2.0 ™ DOCUMENTATION](https://www.facebook.com/aibyabc123/)\n","\n"],"metadata":{"id":"KqB6HUCs-5pj"}},{"cell_type":"markdown","metadata":{"id":"N6ZDpd9XzFeN"},"source":["##### Copyright 2018 The TensorFlow Hub Authors.\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"both","id":"KUu4vOt5zI9d"},"outputs":[],"source":["# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\n","#\n","# Licensed under the Apache License, Version 2.0 (the \"License\");\n","# you may not use this file except in compliance with the License.\n","# You may obtain a copy of the License at\n","#\n","#     http://www.apache.org/licenses/LICENSE-2.0\n","#\n","# Unless required by applicable law or agreed to in writing, software\n","# distributed under the License is distributed on an \"AS IS\" BASIS,\n","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","# See the License for the specific language governing permissions and\n","# limitations under the License.\n","# =============================================================================="]},{"cell_type":"markdown","metadata":{"id":"Sy553YSVmYiK"},"source":["This Colab demonstrates use of a TF-Hub module trained to perform object detection."]},{"cell_type":"markdown","metadata":{"id":"v4XGxDrCkeip"},"source":["## Setup\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6cPY9Ou4sWs_"},"outputs":[],"source":["#@title Imports and function definitions\n","\n","# For running inference on the TF-Hub module.\n","import tensorflow as tf\n","\n","import tensorflow_hub as hub\n","\n","# For downloading the image.\n","import matplotlib.pyplot as plt\n","import tempfile\n","from six.moves.urllib.request import urlopen\n","from six import BytesIO\n","\n","# For drawing onto the image.\n","import numpy as np\n","from PIL import Image\n","from PIL import ImageColor\n","from PIL import ImageDraw\n","from PIL import ImageFont\n","from PIL import ImageOps\n","\n","# For measuring the inference time.\n","import time\n","\n","# Print Tensorflow version\n","print(tf.__version__)\n","\n","# Check available GPU devices.\n","print(\"The following GPU devices are available: %s\" % tf.test.gpu_device_name())"]},{"cell_type":"markdown","metadata":{"id":"ZGkrXGy62409"},"source":["## Example use"]},{"cell_type":"markdown","metadata":{"id":"vlA3CftFpRiW"},"source":["### Helper functions for downloading images and for visualization.\n","\n","Visualization code adapted from [TF object detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/utils/visualization_utils.py) for the simplest required functionality."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D9IwDpOtpIHW"},"outputs":[],"source":["def display_image(image):\n","  fig = plt.figure(figsize=(20, 15))\n","  plt.grid(False)\n","  plt.imshow(image)\n","\n","\n","def download_and_resize_image(url, new_width=256, new_height=256,\n","                              display=False):\n","  _, filename = tempfile.mkstemp(suffix=\".jpg\")\n","  response = urlopen(url)\n","  image_data = response.read()\n","  image_data = BytesIO(image_data)\n","  pil_image = Image.open(image_data)\n","  pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS)\n","  pil_image_rgb = pil_image.convert(\"RGB\")\n","  pil_image_rgb.save(filename, format=\"JPEG\", quality=90)\n","  print(\"Image downloaded to %s.\" % filename)\n","  if display:\n","    display_image(pil_image)\n","  return filename\n","\n","\n","def draw_bounding_box_on_image(image,\n","                               ymin,\n","                               xmin,\n","                               ymax,\n","                               xmax,\n","                               color,\n","                               font,\n","                               thickness=4,\n","                               display_str_list=()):\n","  \"\"\"Adds a bounding box to an image.\"\"\"\n","  draw = ImageDraw.Draw(image)\n","  im_width, im_height = image.size\n","  (left, right, top, bottom) = (xmin * im_width, xmax * im_width,\n","                                ymin * im_height, ymax * im_height)\n","  draw.line([(left, top), (left, bottom), (right, bottom), (right, top),\n","             (left, top)],\n","            width=thickness,\n","            fill=color)\n","\n","  # If the total height of the display strings added to the top of the bounding\n","  # box exceeds the top of the image, stack the strings below the bounding box\n","  # instead of above.\n","  display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]\n","  # Each display_str has a top and bottom margin of 0.05x.\n","  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)\n","\n","  if top > total_display_str_height:\n","    text_bottom = top\n","  else:\n","    text_bottom = top + total_display_str_height\n","  # Reverse list and print from bottom to top.\n","  for display_str in display_str_list[::-1]:\n","    text_width, text_height = font.getsize(display_str)\n","    margin = np.ceil(0.05 * text_height)\n","    draw.rectangle([(left, text_bottom - text_height - 2 * margin),\n","                    (left + text_width, text_bottom)],\n","                   fill=color)\n","    draw.text((left + margin, text_bottom - text_height - margin),\n","              display_str,\n","              fill=\"black\",\n","              font=font)\n","    text_bottom -= text_height - 2 * margin\n","\n","\n","def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.1):\n","  \"\"\"Overlay labeled boxes on an image with formatted scores and label names.\"\"\"\n","  colors = list(ImageColor.colormap.values())\n","\n","  try:\n","    font = ImageFont.truetype(\"/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf\",\n","                              25)\n","  except IOError:\n","    print(\"Font not found, using default font.\")\n","    font = ImageFont.load_default()\n","\n","  for i in range(min(boxes.shape[0], max_boxes)):\n","    if scores[i] >= min_score:\n","      ymin, xmin, ymax, xmax = tuple(boxes[i])\n","      display_str = \"{}: {}%\".format(class_names[i].decode(\"ascii\"),\n","                                     int(100 * scores[i]))\n","      color = colors[hash(class_names[i]) % len(colors)]\n","      image_pil = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n","      draw_bounding_box_on_image(\n","          image_pil,\n","          ymin,\n","          xmin,\n","          ymax,\n","          xmax,\n","          color,\n","          font,\n","          display_str_list=[display_str])\n","      np.copyto(image, np.array(image_pil))\n","  return image"]},{"cell_type":"markdown","metadata":{"id":"D19UCu9Q2-_8"},"source":["## Apply module\n","\n","Load a public image from Open Images v4, save locally, and display."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YLWNhjUY1mhg"},"outputs":[],"source":["# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg\n","image_url = \"https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg\"  #@param\n","downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True)"]},{"cell_type":"markdown","metadata":{"id":"t-VdfLbC1w51"},"source":["Pick an object detection module and apply on the downloaded image. Modules:\n","* **FasterRCNN+InceptionResNet V2**: high accuracy,\n","* **ssd+mobilenet V2**: small and fast."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uazJ5ASc2_QE"},"outputs":[],"source":["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\"]\n","\n","detector = hub.load(module_handle).signatures['default']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"znW8Fq1EC0x7"},"outputs":[],"source":["def load_img(path):\n","  img = tf.io.read_file(path)\n","  img = tf.image.decode_jpeg(img, channels=3)\n","  return img"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kwGJV96WWBLH"},"outputs":[],"source":["def run_detector(detector, path):\n","  img = load_img(path)\n","\n","  converted_img  = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]\n","  start_time = time.time()\n","  result = detector(converted_img)\n","  end_time = time.time()\n","\n","  result = {key:value.numpy() for key,value in result.items()}\n","\n","  print(\"Found %d objects.\" % len(result[\"detection_scores\"]))\n","  print(\"Inference time: \", end_time-start_time)\n","\n","  image_with_boxes = draw_boxes(\n","      img.numpy(), result[\"detection_boxes\"],\n","      result[\"detection_class_entities\"], result[\"detection_scores\"])\n","\n","  display_image(image_with_boxes)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vchaUW1XDodD"},"outputs":[],"source":["run_detector(detector, downloaded_image_path)"]},{"cell_type":"markdown","metadata":{"id":"WUUY3nfRX7VF"},"source":["### More images\n","Perform inference on some additional images with time tracking.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rubdr2JXfsa1"},"outputs":[],"source":["image_urls = [\n","  # Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_(Plate_125)_(8592917784).jpg\n","  \"https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_%28Plate_125%29_%288592917784%29.jpg\",\n","  # By Américo Toledano, Source: https://commons.wikimedia.org/wiki/File:Biblioteca_Maim%C3%B3nides,_Campus_Universitario_de_Rabanales_007.jpg\n","  \"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\",\n","  # Source: https://commons.wikimedia.org/wiki/File:The_smaller_British_birds_(8053836633).jpg\n","  \"https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.jpg\",\n","  ]\n","\n","def detect_img(image_url):\n","  start_time = time.time()\n","  image_path = download_and_resize_image(image_url, 640, 480)\n","  run_detector(detector, image_path)\n","  end_time = time.time()\n","  print(\"Inference time:\",end_time-start_time)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"otPnrxMKIrj5"},"outputs":[],"source":["detect_img(image_urls[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H5F7DkD5NtOx"},"outputs":[],"source":["detect_img(image_urls[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DZ18R7dWNyoU"},"outputs":[],"source":["detect_img(image_urls[2])"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"private_outputs":true,"provenance":[{"file_id":"https://github.com/tensorflow/hub/blob/master/examples/colab/object_detection.ipynb","timestamp":1662855305576}],"toc_visible":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}