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Update notebooks
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‎README.md

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# deepstack-python
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Unofficial python API for [DeepStack](https://python.deepstack.cc/). Provides class for making requests to the object detection endpoint, and functions for processing the result. See the Jupyter notebooks for usage.
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## Services
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Face and object detection endpoints return bounding boxes of faces and objects respectively.
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TODO: add face registration and recognition.
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Run deepstack (CPU, noAVX mode):
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```
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docker run -e VISION-DETECTION=True -e VISION-FACE=True -e MODE=High -d \
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-v localstorage:/datastore -p 5000:5000 \
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-e API-KEY="Mysecretkey" \
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--name deepstack deepquestai/deepstack:noavx
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```
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## Development
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* Use `venv` -> `source venv/bin/activate`

‎deepstack/core.py

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@@ -161,7 +161,7 @@ def register_face(self, name: str, image_bytes: bytes):
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)
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if response.status_code == 200 and response.json()["success"] == True:
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print("Taught face {} using file {}".format(name, file_path))
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return
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elif response.status_code == 200 and response.json()["success"] == False:
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error = response.json()["error"]
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print("Error: {}".format(error))
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raise DeepstackException(f"Error from Deepstack: {error}")

‎usage-face-recognition.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This package provides convenience classes and functions for working with deepstack face API. \n",
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"\n",
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"Run deepstack with:\n",
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"```\n",
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"docker run -e VISION-FACE=True -e MODE=High -d \\\n",
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" -v localstorage:/datastore -p 5000:5000 \\\n",
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" -e API-KEY=\"Mysecretkey\" \\\n",
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" --name deepstack deepquestai/deepstack:noavx\n",
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"```"
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"Work with the deepstack face API. "
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]
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},
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{
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"IP_ADDRESS = 'localhost'\n",
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"PORT = '5000'\n",
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"API_KEY = \"Mysecretkey\"\n",
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"TIMEOUT = 8"
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"TIMEOUT = 10 # Default is 10"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'confidence': 0.9999846, 'y_min': 162, 'x_min': 1620, 'y_max': 680, 'x_max': 1982}, {'confidence': 0.99997175, 'y_min': 230, 'x_min': 867, 'y_max': 729, 'x_max': 1199}]\n"
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"CPU times: user 8.38 ms, sys: 9.7 ms, total: 18.1 ms\n",
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"Wall time: 2.72 s\n"
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]
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}
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],
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"source": [
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"#%%time\n",
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"%%time\n",
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"try:\n",
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" dsface.process_file(image_path)\n",
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" print(dsface.predictions)\n",
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" with open(image_path, \"rb\") as image_bytes:\n",
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" dsface.detect(image_bytes)\n",
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"except ds.DeepstackException as exc:\n",
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" print(exc)"
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]
@@ -174,41 +167,33 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Taught face idris using file tests/images/couple.jpg\n"
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"CPU times: user 4.46 ms, sys: 3.62 ms, total: 8.09 ms\n",
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"Wall time: 2.78 s\n"
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]
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}
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],
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"source": [
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"dsface.register_face(name=\"idris\", file_path = 'tests/images/couple.jpg')"
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"%%time\n",
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"try:\n",
185+
" with open(image_path, \"rb\") as image_bytes:\n",
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" dsface.register_face(\"idris\", image_bytes)\n",
187+
"except ds.DeepstackException as exc:\n",
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" print(exc)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'tests/images/couplesdf.jpg'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-10-ab14dae0eb4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_face\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"idris\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'tests/images/couplesdf.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m~/Github/deepstack-python/deepstack/core.py\u001b[0m in \u001b[0;36mregister_face\u001b[0;34m(self, name, file_path)\u001b[0m\n\u001b[1;32m 155\u001b[0m \"\"\"\n\u001b[1;32m 156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 158\u001b[0m response = post_image(\n\u001b[1;32m 159\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mURL_FACE_REGISTRATION\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ip_address\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tests/images/couplesdf.jpg'"
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]
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}
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],
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"source": [
211-
"dsface.register_face(name=\"idris\", file_path = 'tests/images/couplesdf.jpg')"
195+
"## Face recognition\n",
196+
"Recoginition will match any faces that have been taught. This is slower than face detection"
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]
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},
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{
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219204
"The package provides helper functions for extracting info out of deepstack predictions"
220205
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
228-
"name=\"idris\"\n",
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"file_path=\"foo.jpg\"\n",
230-
"print(\"Taught face {} using file {}\".format(name, file_path))"
231-
]
232-
},
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{
234208
"cell_type": "code",
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"execution_count": null,

‎usage-object-detection.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This package provides convenience classes and functions for working with deepstack object detection API.\n",
8-
"\n",
9-
"Run deepstack with:\n",
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"```\n",
11-
"docker run -e VISION-DETECTION=True -e MODE=High -d \\\n",
12-
" -v localstorage:/datastore -p 5000:5000 \\\n",
13-
" -e API-KEY=\"Mysecretkey\" \\\n",
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" --name deepstack deepquestai/deepstack:noavx\n",
15-
"```\n",
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"\n",
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"Note that by default, the minimum confidence for detected objects is 0.45"
7+
"Work with the deepstack object detection API (Yolo-v3). Note that by default, the minimum confidence for detected objects is 0.45"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"IP_ADDRESS = 'localhost'\n",
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"PORT = '5000'\n",
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"API_KEY = \"Mysecretkey\"\n",
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"TIMEOUT = 8"
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"TIMEOUT = 10 # Default is 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Perform object detection"
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"Perform object detection - can be slow"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'confidence': 0.9998661, 'label': 'person', 'y_min': 0, 'x_min': 258, 'y_max': 676, 'x_max': 485}, {'confidence': 0.9996547, 'label': 'person', 'y_min': 0, 'x_min': 405, 'y_max': 652, 'x_max': 639}, {'confidence': 0.99745613, 'label': 'dog', 'y_min': 311, 'x_min': 624, 'y_max': 591, 'x_max': 825}]\n"
91+
"Timeout connecting to Deepstack, current timeout is 8 seconds\n",
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"CPU times: user 3.88 ms, sys: 2.42 ms, total: 6.3 ms\n",
93+
"Wall time: 8.02 s\n"
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]
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}
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],
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"source": [
106-
"#%%time\n",
98+
"%%time\n",
10799
"try:\n",
108-
" dsobject.process_file(image_path)\n",
100+
" with open(image_path, 'rb') as image_bytes:\n",
101+
" dsobject.detect(image_bytes)\n",
109102
"except ds.DeepstackException as exc:\n",
110103
" print(exc)"
111104
]
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156149
},
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{
158151
"cell_type": "code",
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"execution_count": 14,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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165158
"3"
166159
]
167160
},
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"execution_count": 14,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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186179
"cell_type": "markdown",
187180
"metadata": {},
188181
"source": [
189-
"Get the set of unique labels"
182+
"Get the set objects"
190183
]
191184
},
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{
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206199
}
207200
],
208201
"source": [
209-
"ds.get_object_labels(dsobject.predictions)"
202+
"ds.get_objects(dsobject.predictions)"
210203
]
211204
},
212205
{
213206
"cell_type": "markdown",
214207
"metadata": {},
215208
"source": [
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"Get a summary of the number of occurances of labels"
209+
"Get a summary of the number of occurances of objects"
217210
]
218211
},
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{
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237230
"summary"
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]
239232
},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['dog', 'person']"
249-
]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
254-
}
255-
],
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"source": [
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"list(summary.keys())"
258-
]
259-
},
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{
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"cell_type": "markdown",
262235
"metadata": {},
263236
"source": [
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"Get a list of confidences for a single label type, e.g. `person`"
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"Get a list of confidences for a single object type, e.g. `person`"
265238
]
266239
},
267240
{
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281254
}
282255
],
283256
"source": [
284-
"confidences = ds.get_label_confidences(dsobject.predictions, 'person')\n",
285-
"confidences"
257+
"person_confidences = ds.get_object_confidences(dsobject.predictions, 'person')\n",
258+
"person_confidences"
286259
]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"1"
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]
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},
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"execution_count": 16,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
311284
"source": [
312285
"CONFIDENCE_THRESHOLD = 0.9997\n",
313-
"len(ds.get_confidences_above_threshold(confidences, CONFIDENCE_THRESHOLD))"
286+
"len(ds.get_confidences_above_threshold(person_confidences, CONFIDENCE_THRESHOLD))"
314287
]
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},
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{

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