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update apiRequest after update of PlantNet API
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‎README.md‎

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* Password
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* For the other APIs:
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* Access token
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* Filename of csv, where for each image the organ for each image is specified. Organ could be one of: leaf, flower, fruit, bark (For Pl@ntNet only)
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* The result file will have following result:
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* fileName of image, first prediction, score of first prediction, second prediction, score of second prediction, ...
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‎apiRequests.ipynb‎

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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copy of apiRequests.ipynb","provenance":[{"file_id":"1DeqHHIVPyq050iqMdRp6fApulrSw6-Pv","timestamp":1639148424775}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.4"}},"cells":[{"cell_type":"markdown","metadata":{"id":"aDrbTiS5DtVu"},"source":["# Requests to Computer Vision APIs (plant.id, PlantNet, iNaturalist, NIA)"]},{"cell_type":"code","metadata":{"id":"uJ39A3lpD_rL"},"source":["# import of needed packages\n","import base64\n","import requests\n","import getpass\n","import csv\n","import os\n","import json\n","from pprint import pprint\n","import time"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m0dq5HDmDo9Z"},"source":["# function to request the Plant.id API\n","def plantID (image, token, dir):\n"," image_data = readImage(dir + \"/\" + image) \n"," json_data = {\n"," \"images\": image_data,\n"," \"plant_details\": [\"common_names\", \"url\", \"taxonomy\"]\n"," }\n"," response = requests.post(\n"," \"https://api.plant.id/v2/identify\",\n"," json=json_data,\n"," headers={\n"," \"Content-Type\": \"application/json\",\n"," \"Api-Key\": token\n"," }).json()\n"," result= [image]\n"," for suggestion in response[\"suggestions\"]:\n"," result.append(suggestion[\"plant_name\"])\n"," result.append(str(suggestion[\"probability\"]))\n"," return result"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zguR5RkqMdS-"},"source":["# function to request the plantNet API\n","def plantNet (image, token, dir): \n"," image_data = open(dir + \"/\" + image[0], 'rb')\n"," api_endpoint = f\"https://my-api.plantnet.org/v2/identify/all?api-key={token}\"\n","\n","\n"," files = [\n"," ('images', (image[0], image_data, ), )\n"," ]\n"," \n"," data = {\n"," 'organs': [image[1].lstrip()]\n"," }\n","\n"," req = requests.Request('POST', url=api_endpoint, files=files, data=data)\n"," prepared = req.prepare()\n","\n"," s = requests.Session()\n"," response = s.send(prepared)\n"," json_result = json.loads(response.text)\n","\n"," pprint(response.status_code)\n"," pprint(json_result)\n"," result = [image[0]]\n"," if(response.status_code==404):\n"," result.append(\"not found\")\n"," elif(response.status_code==200): \n"," for suggestion in json_result[\"results\"]:\n"," result.append(suggestion[\"species\"][\"scientificNameWithoutAuthor\"]) \n"," result.append(str(suggestion[\"score\"]))\n"," return result"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m-i9gldEbbAe"},"source":["# function to request an access token for the NIA (Nature Identification API)\n","def NiaRetrieveAccess (clientId, email, password): \n"," data = {\n"," 'client_id': clientId,\n"," 'grant_type': 'password',\n"," 'email': email,\n"," 'password': password\n"," }\n","\n"," response = requests.post('https://waarneming.nl/api/v1/oauth2/token/', data=data)\n","\n"," response_text_json = json.loads(response.text) \n"," acces_token = response_text_json[\"access_token\"]\n","\n"," return acces_token"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HLrPrFVsbbAi"},"source":["# function to request the NIA API\n","def NIA(image, token, dir):\n"," image_dir = dir + \"/\" + image \n"," \n"," headers = {\n"," 'Authorization': 'Bearer ' + token,\n"," }\n","\n"," params = (\n"," ('app_name', 'uni-muenster'),\n"," )\n","\n"," files = {\n"," 'image': (image_dir, open(image_dir, 'rb')),\n"," }\n"," \n"," response = requests.post('https://waarneming.nl/api/identify-proxy/v1/', headers=headers, params=params, files=files)\n"," \n"," response_text_json = json.loads(response.text)\n"," \n"," result = [image]\n"," \n"," for prediction in response_text_json[\"predictions\"]:\n"," result.append(prediction[\"taxon\"][\"name\"])\n"," result.append(str(prediction[\"probability\"])) \n"," \n"," return result\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tRr2pNuDb5qL"},"source":["# function to request the iNaturalist API\n","def iNaturalist(image, token, dir):\n"," api_endpoint = \"https://visionapi.p.rapidapi.com/v1/rapidapi/score_image\"\n"," image_data = open(dir + \"/\" + image, 'rb')\n","\n"," headers = {\n"," 'x-rapidapi-host': \"visionapi.p.rapidapi.com\",\n"," 'x-rapidapi-key': token\n"," }\n","\n"," files = [\n"," ('image', (image, image_data))\n"," ]\n","\n"," req = requests.Request('POST', headers=headers, url=url, files=files)\n"," prepared = req.prepare()\n"," s = requests.Session()\n"," response = s.send(prepared)\n"," json_result = json.loads(response.text)\n"," result = [image]\n"," if(response.status_code==200): \n"," for suggestion in json_result[\"results\"]:\n"," result.append(suggestion[\"taxon\"][\"name\"]) \n"," result.append(\"n/a\")\n"," return result"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rJahqD-uFEkJ"},"source":["# encode image to base64\n","def readImage (image_name):\n"," with open(image_name, \"rb\") as file:\n"," image = [base64.b64encode(file.read()).decode(\"ascii\")]\n"," return image"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"e1uoIZGqIwTJ"},"source":["# function to write the results to csv\n","def writeDataToCSV (data, filename):\n"," with open(filename,'a+', encoding='UTF8', newline='') as file:\n"," writer = csv.writer(file)\n"," writer.writerow(data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oLordk4TFZU0","scrolled":true},"source":["# final excecution script\n","dir = input('Directory with test images: ') \n","filename = input('Filename of result CSV: ') \n","api = input('API (\"plantNet\",\"iNaturalist\", \"plantID\", \"NIA\"): ') \n","if (api == \"plantID\" or api == \"iNaturalist\" or api==\"NIA\"): \n"," if (api == \"NIA\"): \n"," clientId= getpass.getpass(prompt='Client Id: ', stream=None) \n"," email= input('Email: ') \n"," password= getpass.getpass(prompt='Password: ', stream=None) \n"," token = NiaRetrieveAccess(clientId, email, password)\n"," else:\n"," token= getpass.getpass(prompt='Api Key: ', stream=None) \n","\n","\n"," # get all images from the test directory \n"," images = os.listdir(dir)\n","\n"," # requesting the API for each image in the test directory\n"," for image in images:\n"," response = eval(api + \"(image, token, dir)\")\n"," writeDataToCSV(response, filename)\n"," if (api == \"iNaturalist\"):\n"," time.sleep(1) # limitation that just 60 requests per minute are allowed\n","\n","elif (api == \"plantNet\"): \n"," # plantNet requires an organ for each image. Organ could be one of: leaf, flower, fruit, bark\n"," species_list = input('Name of the CSV where corresponding organ is added to the imagefile') #csv should look like : imageName.jpg, organ \\n\n"," with open(species_list, mode ='r')as file:\n"," \n"," # reading the CSV file\n"," csv_file = csv.reader(file)\n","\n"," # requesting the API for each line of the CSV file\n"," for lines in csv_file:\n"," response= plantNet(lines, api_key, dir)\n"," writeDataToCSV(response, filename)\n","else:\n"," print('API must be one of \"plantNet\",\"iNaturalist\", \"plantID\", \"NIA\"')\n","\n"," \n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xnT0UdvIbbA3"},"source":[""],"execution_count":null,"outputs":[]}]}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "aDrbTiS5DtVu"
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},
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"source": [
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"# Requests to Computer Vision APIs (plant.id, PlantNet, iNaturalist, NIA)"
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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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"id": "uJ39A3lpD_rL"
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},
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"outputs": [],
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"source": [
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"# import of needed packages\n",
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"import base64\n",
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"import requests\n",
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"import getpass\n",
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"import csv\n",
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"import os\n",
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"import json\n",
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"from pprint import pprint\n",
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"import time"
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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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"id": "m0dq5HDmDo9Z"
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},
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"outputs": [],
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"source": [
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"# function to request the Plant.id API\n",
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"def plantID (image, token, dir):\n",
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" image_data = readImage(dir + \"/\" + image) \n",
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" json_data = {\n",
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" \"images\": image_data,\n",
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" \"plant_details\": [\"common_names\", \"url\", \"taxonomy\"]\n",
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" }\n",
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" response = requests.post(\n",
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" \"https://api.plant.id/v2/identify\",\n",
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" json=json_data,\n",
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" headers={\n",
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" \"Content-Type\": \"application/json\",\n",
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" \"Api-Key\": token\n",
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" }).json()\n",
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" result= [image]\n",
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" for suggestion in response[\"suggestions\"]:\n",
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" result.append(suggestion[\"plant_name\"])\n",
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" result.append(str(suggestion[\"probability\"]))\n",
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" return result"
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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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"id": "zguR5RkqMdS-"
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},
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"outputs": [],
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"source": [
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"# function to request the plantNet API\n",
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"def plantNet (image, token, dir): \n",
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" image_data = open(dir + \"/\" + image, 'rb')\n",
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" api_endpoint = f\"https://my-api.plantnet.org/v2/identify/the-plant-list?api-key={token}\"\n",
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"\n",
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"\n",
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" files = [\n",
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" ('images', (image, image_data, ) )\n",
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" ]\n",
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" \n",
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"\n",
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"\n",
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" req = requests.Request('POST', url=api_endpoint, files=files)\n",
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" prepared = req.prepare()\n",
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"\n",
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" s = requests.Session()\n",
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" response = s.send(prepared)\n",
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" json_result = json.loads(response.text)\n",
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"\n",
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" pprint(response.status_code)\n",
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" pprint(json_result)\n",
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" result = [image]\n",
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" if(response.status_code==404):\n",
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" result.append(\"not found\")\n",
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" elif(response.status_code==200): \n",
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" for suggestion in json_result[\"results\"]:\n",
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" result.append(suggestion[\"species\"][\"scientificNameWithoutAuthor\"]) \n",
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" result.append(str(suggestion[\"score\"]))\n",
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" return result"
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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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"id": "m-i9gldEbbAe"
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},
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"outputs": [],
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"source": [
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"# function to request an access token for the NIA (Nature Identification API)\n",
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"def NiaRetrieveAccess (clientId, email, password): \n",
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" data = {\n",
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" 'client_id': clientId,\n",
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" 'grant_type': 'password',\n",
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" 'email': email,\n",
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" 'password': password\n",
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" }\n",
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"\n",
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" response = requests.post('https://waarneming.nl/api/v1/oauth2/token/', data=data)\n",
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"\n",
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" response_text_json = json.loads(response.text) \n",
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" acces_token = response_text_json[\"access_token\"]\n",
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"\n",
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" return acces_token"
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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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"id": "HLrPrFVsbbAi"
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},
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"outputs": [],
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"source": [
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"# function to request the NIA API\n",
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"def NIA(image, token, dir):\n",
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" image_dir = dir + \"/\" + image \n",
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" \n",
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" headers = {\n",
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" 'Authorization': 'Bearer ' + token,\n",
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" }\n",
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"\n",
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" params = (\n",
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" ('app_name', 'uni-muenster'),\n",
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" )\n",
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"\n",
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" files = {\n",
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" 'image': (image_dir, open(image_dir, 'rb')),\n",
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" }\n",
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" \n",
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" response = requests.post('https://waarneming.nl/api/identify-proxy/v1/', headers=headers, params=params, files=files)\n",
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" \n",
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" response_text_json = json.loads(response.text)\n",
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" \n",
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" result = [image]\n",
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" \n",
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" for prediction in response_text_json[\"predictions\"]:\n",
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" result.append(prediction[\"taxon\"][\"name\"])\n",
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" result.append(str(prediction[\"probability\"])) \n",
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" \n",
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" return result\n"
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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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"id": "tRr2pNuDb5qL"
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},
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"outputs": [],
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"source": [
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"# function to request the iNaturalist API\n",
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"def iNaturalist(image, token, dir):\n",
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" api_endpoint = \"https://visionapi.p.rapidapi.com/v1/rapidapi/score_image\"\n",
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" image_data = open(dir + \"/\" + image, 'rb')\n",
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"\n",
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" headers = {\n",
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" 'x-rapidapi-host': \"visionapi.p.rapidapi.com\",\n",
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" 'x-rapidapi-key': token\n",
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" }\n",
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"\n",
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" files = [\n",
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" ('image', (image, image_data))\n",
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" ]\n",
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"\n",
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" req = requests.Request('POST', headers=headers, url=url, files=files)\n",
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" prepared = req.prepare()\n",
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" s = requests.Session()\n",
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" response = s.send(prepared)\n",
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" json_result = json.loads(response.text)\n",
188+
" result = [image]\n",
189+
" if(response.status_code==200): \n",
190+
" for suggestion in json_result[\"results\"]:\n",
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" result.append(suggestion[\"taxon\"][\"name\"]) \n",
192+
" result.append(\"n/a\")\n",
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" return result"
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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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"id": "rJahqD-uFEkJ"
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},
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"outputs": [],
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"source": [
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"# encode image to base64\n",
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"def readImage (image_name):\n",
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" with open(image_name, \"rb\") as file:\n",
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" image = [base64.b64encode(file.read()).decode(\"ascii\")]\n",
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" return image"
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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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"id": "e1uoIZGqIwTJ"
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},
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"outputs": [],
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"source": [
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"# function to write the results to csv\n",
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"def writeDataToCSV (data, filename):\n",
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" with open(filename,'a+', encoding='UTF8', newline='') as file:\n",
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" writer = csv.writer(file)\n",
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" writer.writerow(data)"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "oLordk4TFZU0",
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"outputId": "aa92fc39-244d-4dbe-ac48-78fd5d272dbf",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"## final excecution script\n",
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"dir = input('Directory with test images: ') \n",
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"filename = input('Filename of result CSV: ') \n",
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"api = input('API (\"plantNet\",\"iNaturalist\", \"plantID\", \"NIA\"): ') \n",
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"if (api == \"plantID\" or api == \"iNaturalist\" or api==\"NIA\" or api == \"plantNet\"): \n",
244+
" if (api == \"NIA\"): \n",
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" clientId= getpass.getpass(prompt='Client Id: ', stream=None) \n",
246+
" email= input('Email: ') \n",
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" password= getpass.getpass(prompt='Password: ', stream=None) \n",
248+
" token = NiaRetrieveAccess(clientId, email, password)\n",
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" else:\n",
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" token= getpass.getpass(prompt='Api Key: ', stream=None) \n",
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"\n",
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"\n",
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" # get all images from the test directory \n",
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" images = os.listdir(dir)\n",
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"\n",
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" # requesting the API for each image in the test directory\n",
257+
" for image in images:\n",
258+
" response = eval(api + \"(image, token, dir)\")\n",
259+
" writeDataToCSV(response, filename)\n",
260+
" if (api == \"iNaturalist\"):\n",
261+
" time.sleep(1) # limitation that just 60 requests per minute are allowed\n",
262+
"\n",
263+
"else:\n",
264+
" print('API must be one of \"plantNet\",\"iNaturalist\", \"plantID\", \"NIA\"')\n",
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"\n",
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" \n",
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" "
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]
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}
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],
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"metadata": {
272+
"colab": {
273+
"collapsed_sections": [],
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"name": "apiRequests.ipynb",
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"provenance": []
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},
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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",
288+
"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
291+
"pygments_lexer": "ipython3",
292+
"version": "3.7.9"
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}
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},
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"nbformat": 4,
296+
"nbformat_minor": 1
297+
}

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