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ritvikmath
GitHub
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c7918ac389
commit
8751a07585
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"Steps:\n",
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" \n",
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"1. Visit https://console.cloud.google.com/\n",
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"\n",
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"2. Create a new project if none exist (otherwise you can use an existing one)\n",
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"\n",
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"3. Click \"Go to APIs Overview\" -> Click \"Enable APIs and Services\" -> Enable the \"Cloud Vision API\"\n",
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"\n",
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"4. Click the \"Credentials\" tab on the left. Click \"+ CREATE CREDENTIALS\" at the top and choose \"Service Account\". Give the service account a name and click \"Create\"\n",
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"\n",
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"5. Click on the newly created service account, ensure it is enabled, and click \"ADD KEY\" -> \"Create new key\". Pick \"JSON\" and download the json file and store it in the current working directory.\n",
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"\n",
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"6. Run the cells in this notebook."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install google-cloud-vision"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"from google.cloud import vision\n",
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"from google.cloud.vision import types"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#the JSON file you downloaded in step 5 above\n",
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"os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = ''"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiates a client\n",
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"client = vision.ImageAnnotatorClient()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#set this thumbnail as the url\n",
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"image = types.Image()\n",
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"image.source.image_uri = 'https://i.ytimg.com/vi/UQQHSbeIaB0/maxresdefault.jpg'"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#### LABEL DETECTION ######\n",
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"\n",
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"response_label = client.label_detection(image=image)\n",
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"\n",
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"for label in response_label.label_annotations:\n",
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" print({'label': label.description, 'score': label.score})"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#### FACE DETECTION ######\n",
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"\n",
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"response_face = client.face_detection(image=image)\n",
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"\n",
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"face_data = []\n",
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"\n",
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"for face_detection in response_face.face_annotations:\n",
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" d = {\n",
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" 'confidence': face_detection.detection_confidence,\n",
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" 'joy': face_detection.joy_likelihood,\n",
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" 'sorrow': face_detection.sorrow_likelihood,\n",
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" 'surprise': face_detection.surprise_likelihood,\n",
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" 'anger': face_detection.anger_likelihood\n",
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" }\n",
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" print(d)\n",
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#### IMAGE PROPERTIES ######\n",
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"\n",
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"response_image = client.image_properties(image=image)\n",
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"\n",
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"image_data = []\n",
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"\n",
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"for c in response_image.image_properties_annotation.dominant_colors.colors[:3]:\n",
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" d = {\n",
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" 'color': c.color,\n",
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" 'score': c.score,\n",
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" 'pixel_fraction': c.pixel_fraction\n",
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" }\n",
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" print(d)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#### TEXT DETECTION ######\n",
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"\n",
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"response_text = client.text_detection(image=image)\n",
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"\n",
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"for r in response_text.text_annotations:\n",
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" d = {\n",
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" 'text': r.description\n",
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" }\n",
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" print(d)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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