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Commit a281aaa2 authored by A. Unique TensorFlower's avatar A. Unique TensorFlower
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Change title of notebook. 

PiperOrigin-RevId: 447871803
parent 33a5eaff
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Showing with 33 additions and 33 deletions
official/projects/cots_detector/COTS_detection_inference_and_tracking.ipynb → official/projects/cots_detector/crown_of_thorns_starfish_detection_pipeline.ipynb
View file @ a281aaa2
......@@ -28,7 +28,7 @@
"id": "9CzbXNRovpbc"
},
"source": [
"# COTS Detection Inference and Tracking Pipeline"
"# Crown-of-Thorns Starfish Detection Pipeline"
]
},
{
......@@ -37,14 +37,14 @@
"id": "Lpb0yoNjiWhw"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/projects/cots_detector/COTS_detection_inference_and_tracking.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/projects/cots_detector/COTS_detection_inference_and_tracking.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n",
" </td>\n",
"</table>"
"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/projects/cots_detector/COTS_detection_inference_and_tracking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/projects/cots_detector/COTS_detection_inference_and_tracking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView on GitHub\u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/table\u003e"
]
},
{
......@@ -68,7 +68,7 @@
"\n",
"This notebook tutorial shows how to detect COTS using a pre-trained COTS detector implemented in TensorFlow. On top of just running the model on each frame of the video, the tracking code in this notebook aligns detections from frame to frame creating a consistent track for each COTS. Each track is given an id and frame count. Here is an example image from a video of a reef showing labeled COTS starfish.\n",
"\n",
"<img src=\"https://storage.googleapis.com/download.tensorflow.org/data/cots_detection/COTS_detected_sample.png\">"
"\u003cimg src=\"https://storage.googleapis.com/download.tensorflow.org/data/cots_detection/COTS_detected_sample.png\"\u003e"
]
},
{
......@@ -77,7 +77,7 @@
"id": "YxCF1t-Skag8"
},
"source": [
"It is recommended to enable GPU to accelerate the inference. On CPU, this runs for about 40 minutes, but on GPU it takes only 10 minutes. (from colab menu: *Runtime > Change runtime type > Hardware accelerator > select \"GPU\"*)."
"It is recommended to enable GPU to accelerate the inference. On CPU, this runs for about 40 minutes, but on GPU it takes only 10 minutes. (from colab menu: *Runtime \u003e Change runtime type \u003e Hardware accelerator \u003e select \"GPU\"*)."
]
},
{
......@@ -296,7 +296,7 @@
" overlap_y0 = max(self.y0, other.y0)\n",
" overlap_x1 = min(self.x1, other.x1)\n",
" overlap_y1 = min(self.y1, other.y1)\n",
" if overlap_x0 < overlap_x1 and overlap_y0 < overlap_y1:\n",
" if overlap_x0 \u003c overlap_x1 and overlap_y0 \u003c overlap_y1:\n",
" overlap_area = box_area(overlap_x0, overlap_y0, overlap_x1,\n",
" overlap_y1)\n",
" return overlap_area / (self.area() + other.area() - overlap_area)\n",
......@@ -387,8 +387,8 @@
" image_h = image.shape[0]\n",
"\n",
" # Assume tracker is invalid if too much time has passed!\n",
" if (self.prev_time > 0 and\n",
" timestamp - self.prev_time > self.time_threshold):\n",
" if (self.prev_time \u003e 0 and\n",
" timestamp - self.prev_time \u003e self.time_threshold):\n",
" logging.info(\n",
" 'Too much time since last update, resetting tracker.')\n",
" self.tracks = []\n",
......@@ -398,14 +398,14 @@
" # - Have existed for a long time without linking a real detection.\n",
" active_tracks = []\n",
" for track in self.tracks:\n",
" if (track.det.x0 < self.border or track.det.y0 < self.border or\n",
" track.det.x1 >= (image_w - self.border) or\n",
" track.det.y1 >= (image_h - self.border)):\n",
" if (track.det.x0 \u003c self.border or track.det.y0 \u003c self.border or\n",
" track.det.x1 \u003e= (image_w - self.border) or\n",
" track.det.y1 \u003e= (image_h - self.border)):\n",
" logging.info(f'Removing track {track.id} because it\\'s near the border')\n",
" continue\n",
"\n",
" time_since_last_detection = timestamp - track.linked_dets[-1].timestamp\n",
" if (time_since_last_detection > self.track_flow_time):\n",
" if (time_since_last_detection \u003e self.track_flow_time):\n",
" logging.info(f'Removing track {track.id} because it\\'s too old '\n",
" f'({time_since_last_detection:.02f}s)')\n",
" continue\n",
......@@ -415,7 +415,7 @@
" self.tracks = active_tracks\n",
"\n",
" # Run optical flow to update existing tracks.\n",
" if self.prev_time > 0:\n",
" if self.prev_time \u003e 0:\n",
" # print('Running optical flow propagation.')\n",
" of_params = {\n",
" 'winSize': self.of_size,\n",
......@@ -442,9 +442,9 @@
" detected_obj_track_ids = set()\n",
"\n",
" for detection in detections:\n",
" if (detection.x0 < self.border or detection.y0 < self.border or\n",
" detection.x1 >= image_w - self.border or\n",
" detection.y1 >= image_h - self.border):\n",
" if (detection.x0 \u003c self.border or detection.y0 \u003c self.border or\n",
" detection.x1 \u003e= image_w - self.border or\n",
" detection.y1 \u003e= image_h - self.border):\n",
" # print('Skipping detection because it\\'s close to the border.')\n",
" continue\n",
"\n",
......@@ -457,12 +457,12 @@
" if track.det.class_id != detection.class_id:\n",
" continue\n",
" overlap = detection.iou(track.det)\n",
" if overlap > overlap_max:\n",
" if overlap \u003e overlap_max:\n",
" overlap_index = track_index\n",
" overlap_max = overlap\n",
"\n",
" # Link to existing track with maximal IoU.\n",
" if overlap_max > self.overlap_threshold:\n",
" if overlap_max \u003e self.overlap_threshold:\n",
" track = self.tracks[overlap_index]\n",
" track.det = detection\n",
" track.linked_dets.append(Tracklet(timestamp, detection))\n",
......@@ -486,7 +486,7 @@
" self.prev_image = image\n",
" self.prev_time = timestamp\n",
"\n",
" if num_optical_flow_calls > 0:\n",
" if num_optical_flow_calls \u003e 0:\n",
" tracking_ms = int(1000 * (time.time() - start))\n",
" logging.info(f'Tracking took {tracking_ms}ms, '\n",
" f'{num_optical_flow_calls} optical flow calls')\n",
......@@ -511,7 +511,7 @@
},
"outputs": [],
"source": [
"def embed_video_file(path: os.PathLike) -> display.HTML:\n",
"def embed_video_file(path: os.PathLike) -\u003e display.HTML:\n",
" \"\"\"Embeds a file in the notebook as an html tag with a data-url.\"\"\"\n",
" path = pathlib.Path(path)\n",
" mime, unused_encoding = mimetypes.guess_type(str(path))\n",
......@@ -520,10 +520,10 @@
" b64 = base64.b64encode(data).decode()\n",
" return display.HTML(\n",
" textwrap.dedent(\"\"\"\n",
" <video width=\"640\" height=\"480\" controls>\n",
" <source src=\"data:{mime};base64,{b64}\" type=\"{mime}\">\n",
" \u003cvideo width=\"640\" height=\"480\" controls\u003e\n",
" \u003csource src=\"data:{mime};base64,{b64}\" type=\"{mime}\"\u003e\n",
" Your browser does not support the video tag.\n",
" </video>\n",
" \u003c/video\u003e\n",
" \"\"\").format(mime=mime, b64=b64))\n"
]
},
......@@ -668,7 +668,7 @@
" if str(track.id) not in seq_length_dict:\n",
" seq_length_dict[str(track.id)] = len(track.linked_dets)\n",
" else:\n",
" if len(track.linked_dets) > seq_length_dict[str(track.id)]:\n",
" if len(track.linked_dets) \u003e seq_length_dict[str(track.id)]:\n",
" seq_length_dict[str(track.id)] = len(track.linked_dets)\n",
" new_track_list.append({\"score\":str(round(track.det.score, 3)), \"seq_id\": str(track.id), \"seq_idx\": str(len(track.linked_dets)),\n",
" \"x0\": round(track.det.x0), \"y0\": round(track.det.y0), \"x1\": round(track.det.x1), \"y1\": round(track.det.y1)})\n",
......@@ -700,7 +700,7 @@
" batch_size, img_h, img_w = image.shape[0:3]\n",
"\n",
" for batch_index in range(batch_size):\n",
" valid_indices = detection_scores[batch_index, :] >= threshold\n",
" valid_indices = detection_scores[batch_index, :] \u003e= threshold\n",
" classes = detection_classes[batch_index, valid_indices]\n",
" scores = detection_scores[batch_index, valid_indices]\n",
" boxes = detection_boxes[batch_index, valid_indices, :]\n",
......@@ -841,7 +841,7 @@
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "No-Drive version of COTS Detection Inference + Tracking Pipeline.ipynb",
"name": "COTS Detection Inference and Tracking Pipeline.ipynb",
"provenance": []
},
"kernelspec": {
......@@ -854,4 +854,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
\ No newline at end of file
}
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