def image_recognition(request):
query = request.GET.get("q")
args = {}
if query:
instance = vr(api_key='119030bac8e38e1721e61e0f6a295e18e5d9ecdf',
version='2016-05-20')
img = instance.classify(images_url=query)
args = {'images': img['images'][0]['classifiers'][0]['classes']}
return render(request, 'watson/image_recognition.html', args)
def url_recognizer():
wvr = vr(
version="2018-03-19",
iam_apikey="F_-o_C5ie1HKGU750akv3SsbDwy5dI2yigLVl0TSZVOi",
)
img = wvr.classify(
url=
"https://s2.glbimg.com/3auOxS3cG2mc_H5jFXDpxC7ol-w=/e.glbimg.com/og/ed/f/original/2016/09/12/dr-alan-turing-2956483.jpg"
)
return print(img)
# Visual Recognition with IBM Watson - Image Classification # Requirements 1: IBM Bluemix Account (free) - https://console.ng.bluemix.net/ # Requirements 2: watson_developer_cloud module python module # Once registered, login to your account, go to Services -> Watson and create a Visual Recognition instance # Once you have your VR instance created, you will use its API credentials in your python code from watson_developer_cloud import VisualRecognitionV3 as vr # creating a VR instance instance = vr(api_key='paste your api _key here', version='2016-05-20') # select an image (local or url). Copy its location (path or url): img = instance.classify(images_url='url-path-to-img.jpg') # you can run this code in the interpreter. If you request >>> img it will output a json formatted result # getting down the json tree with the following input will display what Watson sees in the image, and the confidence level # >>> img['images'][0]['classifiers'][0]['classes'] # for a better view of the results, you can use pprint import pprint pprint.pprint(img['images'][0]['classifiers'][0]['classes']) # I posted a demo of this here: http://bit.ly/2gZg4D9 # If you need help with Watson and Visual Recognition, send me a message.
# Visual Recognition with IBM Watson - Image Classification
# Requirements 1: IBM Bluemix Account (free) - https://console.ng.bluemix.net/
# Requirements 2: watson_developer_cloud module python module
# Once registered, login to your account, go to Services -> Watson and create a Visual Recognition instance
# Once you have your VR instance created, you will use its API credentials in your python code
from watson_developer_cloud import VisualRecognitionV3 as vr
# creating a VR instance
instance = vr(api_key='143a027a9be556e0c04d17cc87df0e84a6f838b4',
version='2016-05-20')
# select an image (local or url). Copy its location (path or url):
img = instance.classify(
images_url=
'http://ichef.bbci.co.uk/wwfeatures/wm/live/624_351/images/live/p0/3d/tk/p03dtkw2.jpg'
)
# you can run this code in the interpreter. If you request >>> img it will output a json formatted result
# getting down the json tree with the following input will display what Watson sees in the image, and the confidence level
# >>> img['images'][0]['classifiers'][0]['classes']
# for a better view of the results, you can use pprint
import pprint
pprint.pprint(img['images'][0]['classifiers'][0]['classes'])
def __init__(self, name=None, device=None, context=None):
App.__init__(self, name, device, context)
self.engine = vr('2018-07-10',
iam_apikey=self.device.get_encrypted_field('key'))
self.jString = ''
# -*- coding: utf-8 -*-
import os
from os.path import join
from watson_developer_cloud import VisualRecognitionV3 as vr
import numpy as np
vr_instance = vr(version='2016-05-20',
api_key='ca62a5844926baf007e5558a1d4c236dbccee838')
def find_images(walk_dir):
'''
Find images of different labels (corresponding to different subfolders) according to parent folder 'walk_dir'.
'''
names = []
fnames = []
str_labels = []
for root, subdirs, files in os.walk(walk_dir):
for filename in files:
if filename.endswith(('.jpg', '.png', '.JPEG')):
full_fname = join(root, filename)
names.append(filename)
fnames.append(full_fname)
str_labels.append(root)
[u, labels] = np.unique(str_labels, return_inverse=True)
return np.array(fnames), np.array(labels)
#!/usr/bin/env python3
from watson_developer_cloud import VisualRecognitionV3 as vr
import json
import os
import sys
try:
devKey = os.environ['IBM_DEV_KEY']
except:
exit("IBM_DEV_KEY environment variable needs to be set")
instance = vr(api_key=devKey, version='2017-01-24')
elvisTestDir = '/Users/janae/data/elvisPMs_last100'
#testPMs = ['Tony_Blair', 'Gordon_Brown', 'David_Cameron']
testPMs = ['David_Cameron']
# PM found, PM not found, no face found, wrong PM found
pmRes = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
nameUnknown = 'unknown'
for i in range(len(testPMs)):
testperson = testPMs[i]
print(os.path.join(elvisTestDir, testperson))
allImg = [
os.path.join(elvisTestDir, testperson, f)
for f in os.listdir(os.path.join(elvisTestDir, testperson))
if os.path.isfile(os.path.join(elvisTestDir, testperson, f))
and f.endswith(".jpg")
]
for imgFile in allImg:
print 'metadata: ', folder_metadata
#download first image file
print " "
filelocation = raw_input("Enter file location: (example: /Home/13-15-00.jpg) ")
f, metadata = client.get_file_and_metadata(filelocation)
print metadata
im = Image.open(f)
im.show()
print client.share(filelocation, short_url = False)
fileurl = client.share(filelocation, short_url = False)
print fileurl.get('url')
lasturl = fileurl.get('url')
#examine first image file
instance = vr(api_key='put your watson ibm api key here', version='2016-05-20')
img = instance.classify(images_url = lasturl)
a = 0
for things in img['images'][0]['classifiers'][0]['classes']:
if((things['score']*100) > a):
a = things['score']*100
first = things['class']
print('\n There is a ' + str(things['score']*100) + ' percent chance the image contains: '+ things['class'])
print first
#second image file retrieved
filelocation2 = raw_input("Enter file location to compare to: (example: /Home/13-15-00.jpg) ")
c, metadata = client.get_file_and_metadata(filelocation2)
from watson_developer_cloud import VisualRecognitionV3 as vr
import webbrowser
import time
# creating a VR instance
instance = vr(api_key='ff644e192cbf2fcd509165d85a7b053f360ca6a1', version='2016-05-20')
# select an image (local or url). Copy its location (path or url):
img = instance.classify(images_url='http://i.dailymail.co.uk/i/pix/2011/12/23/article-2077964-0CAA9E3A000005DC-263_468x319.jpg')
# you can run this code in the interpreter. If you request >>> img it will output a json formatted result
# getting down the json tree with the following input will display what Watson sees in the image, and the confidence level
# >>> img['images'][0]['classifiers'][0]['classes']
# for a better view of the results, you can use pprint
print "The output given by Watson \n"
print(img)
print("--------------------------------------------------------------------------------------------------------------------------------------------------------- \n")
num_of_results= len(img['images'][0]['classifiers'][0]['classes'])
print "The output in a readable user-friendly way \n"
print("This image contains the following:")
for x in range(0, num_of_results):
print img['images'][0]['classifiers'][0]['classes'][x]['class'],'with confidence level:' , img['images'][0]['classifiers'][0]['classes'][x]['score']
print("--------------------------------------------------------------------------------------------------------------------------------------------------------- \n")
def __init__(self, name, device, context):
App.__init__(self, name, device, context)
self.engine = vr('2016-05-20',
api_key=self.device.get_encrypted_field('key'))
from watson_developer_cloud import VisualRecognitionV3 as vr
import pprint
api_key = input("Enter you api key : ")
instance = vr(api_key=api_key, version='2016-05-20')
def visualRecog(self):
images_url = input("Enter the url of the image :\n")
img = self.classify(images_url=images_url)
data = img['images'][0]['classifiers'][0]['classes']
pprint.pprint(data)
def textRecog(self):
images_url = input("Enter the url of the image :\n")
img = self.recognize_text(images_url=images_url)
print(img['images'][0]['text'])
def facialRecog(self):
images_url = input("Enter the url of the image :\n")
img = self.detect_faces(images_url=images_url)
for identity in img['images'][0]['faces']:
if ('identity' in identity):
print(
"Name : " + identity['identity']['name'] + "\n",
"Gender : " + identity['gender']['gender'] + "\n",
''' THE MAIN PYTHON CODE TO DRIVE THE RASPBERRY PI- IBM BLUEMIX DUO'''
#importing necessary modules
import json
import sys
from os.path import join, dirname
from os import environ
from watson_developer_cloud import VisualRecognitionV3 as vr
from pprint import pprint
#instantiating
visual_recognition = vr(vr.latest_version,
api_key='0234986d7c042310503a1e2477f1f8579a190040')
with open(
'C:\Users\Pritesh J Shah\Desktop\VisualRecognition\leaf_negative_examples\leaf69.jpg',
'rb') as image_file:
a = json.dumps(visual_recognition.classify(
images_file=image_file,
threshold=0,
classifier_ids=['PestControl_834594187']),
indent=2) #retrieving the JSON object
js = json.loads(a) #parsing
a = js['images'][0]['classifiers'][0]['classes'][0]['score'] #parsing
b = js['images'][0]['classifiers'][0]['classes'][1]['score'] #parsing
if (a > b):
print(js['images'][0]['classifiers'][0]['classes'][0]['class'])
else: #checking for pest/not pest
print(js['images'][0]['classifiers'][0]['classes'][1]['class'])
#at this point we get whether the image contains pests or not.
def __init__(self, name=None, device=None):
App.__init__(self, name, device)
self.engine = vr('2016-05-20',
api_key=self.get_device().get_password())
def initiliazeWatson():
ibmWatson = vr(iam_apikey='qqBbMGQ4qmRaPBLbGENUrJMtt-Xy3PvxQk_sptgYDCzJ',version='2016-05-20')
return ibmWatson
