def clarifai():
clarifai = ClarifaiCustomModel()
concept_name = 'piyali'
PIYALI_POSITIVES = [
'http://anshulkgupta.com/hackmit/piyali1.png',
'http://anshulkgupta.com/hackmit/piyali2.png',
'http://anshulkgupta.com/hackmit/piyali3.png',
'http://anshulkgupta.com/hackmit/piyali4.png'
]
for positive_example in PIYALI_POSITIVES:
clarifai.positive(positive_example, concept_name)
PIYALI_NEGATIVES = [
'http://anshulkgupta.com/hackmit/anshul1.png',
'http://anshulkgupta.com/hackmit/anshul2.png',
'http://anshulkgupta.com/hackmit/annie1.png',
'http://anshulkgupta.com/hackmit/annie2.png'
]
for negative_example in PIYALI_NEGATIVES:
clarifai.negative(negative_example, concept_name)
clarifai.train(concept_name)
PIYALI_TEST = [
'http://anshulkgupta.com/hackmit/piyali-test1.png'
]
NOT_PIYALI = [
'http://anshulkgupta.com/hackmit/annie-test1.png',
'http://anshulkgupta.com/hackmit/anshul-test1.png',
'http://anshulkgupta.com/hackmit/anshul-test2.png'
]
data = []
for test in PIYALI_TEST + NOT_PIYALI:
result = clarifai.predict(test, concept_name)
data.append([result['status']['message'],
result['urls'][0]['score'],
result['urls'][0]['url']])
return render_template('clarifai.html', data=data)
def trainTextbook(textbook, textbooks):
clarifai = ClarifaiCustomModel()
concept_name = encode.NumericToAlpha(textbook.isbn[1:])
PHISH_POSITIVES = [
textbook.imgURL
#pilexample.filterBlur(textbook.imgURL)
]
for positive_example in PHISH_POSITIVES:
clarifai.positive(positive_example, concept_name)
PHISH_NEGATIVES = []
for t in textbooks:
if t != textbook:
PHISH_NEGATIVES.append(t.imgURL)
for negative_example in PHISH_NEGATIVES:
clarifai.negative(negative_example, concept_name)
clarifai.train(concept_name)
result = clarifai.predict(textbook.imgURL, concept_name)
print result['status']['message'], "%0.3f" % result['urls'][0]['score'], result['urls'][0]['url']
print concept_name
print decode.alphaToNumeric(concept_name)
def build(self): # instantiate clarifai client clarifai = ClarifaiCustomModel(app_id, app_secret) concept_name = 'phish' # find some positive and negative examples PHISH_POSITIVES = [ 'http://clarifai-test.s3.amazonaws.com/phish/positive/3652848536_c72244dc88_o.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/4840976460_8463f9f319_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/4904257471_20c0ff714f_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/4904842036_6806f5fd25_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/4904845798_aaf3392666_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/6030148539_5d6da277c0_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/positive/9381652037_7e5e7665ab_k.jpg' ] # add the positive example images to the model for positive_example in PHISH_POSITIVES: clarifai.positive(positive_example, concept_name) # negatives are not required but will help if you want to discriminate between similar concepts PHISH_NEGATIVES = [ 'http://clarifai-test.s3.amazonaws.com/phish/negative/5587410471_cf932bf9fa_o.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/7367377586_f5e7c59ef8_k.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/8422034157_1fbe437d3a_b.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/8464327405_5eaf39e6e2_o.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/8804958484_9dcba3da19_k.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/8805067594_f2abc5c751_k.jpg', 'http://clarifai-test.s3.amazonaws.com/phish/negative/9583629691_a1594637a9_k.jpg' ] # add the negative example images to the model for negative_example in PHISH_NEGATIVES: clarifai.negative(negative_example, concept_name) # train the model clarifai.train(concept_name) PHISH_EXAMPLES = [ 'https://clarifai-test.s3.amazonaws.com/photo-1-11-e1342391144673.jpg', 'https://clarifai-test.s3.amazonaws.com/DSC01226-e1311293061704.jpg' ] NOT_PHISH = [ 'https://clarifai-test.s3.amazonaws.com/2141620332_2b741028b3.jpg', 'https://clarifai-test.s3.amazonaws.com/grateful_dead230582_15-52.jpg' ] # If everything works correctly, the confidence that true positive images are of Phish should be # significantly greater than 0.5, which is the same as choosing at random. The confidence that true # negative images are Phish should be significantly less than 0.5. # use the model to predict whether the test images are Phish or not for test in PHISH_EXAMPLES + NOT_PHISH: result = clarifai.predict(test, concept_name) print result['status']['message'], "%0.3f" % result['urls'][0]['score'], result['urls'][0]['url'] return Label(text='xxx')
class Trainer(object):
def __init__(self, jfp):
self.clarifai = ClarifaiCustomModel(app_id, app_secret)
self.jdata = getAllJSONData()
def train(self, negatives_threshold=1.0, dist=2):
for concept in self.jdata:
for url in self.jdata[concept]["resources"]:
print(concept + " positive: " + url)
self.clarifai.positive(url, concept)
filtered = getConceptsByGPS(
latitude=self.jdata[concept]["latitude"],
longitude=self.jdata[concept]["longitude"],
jdata=self.jdata,
distance=dist)
for cid, _ in filtered:
for url in self.jdata[cid]["resources"]:
if random() <= negatives_threshold:
print(concept + " negative: " + url)
self.clarifai.negative(url, cid)
self.clarifai.train(concept)
from clarifai_basic import ClarifaiCustomModel from json import dumps from classifications import language concept = ClarifaiCustomModel() for model in language.keys(): print "current model to train: " + model for url in language[model]: concept.positive(url, model) #print "training url:%s on model %s" % (url, model) for key, value in language.iteritems(): if key != model: for neg_url in value: print neg_url concept.negative(neg_url, model) concept.train(model) #print "making url:%s from model: %s a negative case." %(neg_url, key) ''' for url in language['letter_c']: concept.positive(url, "letter_c") for neg_url in language['letter_a']: concept.negative(neg_url, "letter_c") for neg_url in language['letter_b']: concept.negative(neg_url, "letter_c") for neg_url in language['applause']:
except urllib2.HTTPError, e:
POSITIVES.remove(i)
except urllib2.URLError, e:
POSITIVES.remove(i)
for i in NEGATIVES:
try:
urllib2.urlopen(i)
except urllib2.HTTPError, e:
NEGATIVES.remove(i)
except urllib2.URLError, e:
NEGATIVES.remove(i)
concept_name = 'car'
for positive_example in POSITIVES:
try:
clarifai.positive(positive_example, concept_name)
except socket.gaierror:
print 'ignoring failed address lookup for: ', positive_example
for negative_example in NEGATIVES:
try:
clarifai.negative(negative_example, concept_name)
except socket.gaierror:
print 'ignoring failed address lookup for: ', negative_example
clarifai.train(concept_name)
concept_name = 'automobile'
for positive_example in POSITIVES:
clarifai.positive(positive_example, concept_name)
clarifai.train(concept_name)
concept_name = 'auto'
for positive_example in POSITIVES:
Positive_File = str(sys.argv[2])
Negative_File = str(sys.argv[3])
print "Start"
# instantiate clarifai client
clarifai = ClarifaiCustomModel()
#[line.strip() for line in open("C:/name/MyDocuments/numbers", 'r')]
# add the positive example images to the model
print "Positive"
for positive_example in [ line.strip() for line in (open(Positive_File)).readlines() ]:
try:
clarifai.positive(positive_example, Model_name)
except:
print "oops"
print str(positive_example)
# negatives are not required but will help if you want to discriminate between similar concepts
print "Negative Examples"
# add the negative example images to the model
for negative_example in [ line.strip() for line in (open(Negative_File)).readlines() ]:
try:
clarifai.negative(negative_example, Model_name)
except:
print "oops"
print str(negative_example)
for concept in jdata:
allurls += jdata[concept]["resources"]
LEARN = False
# Percentage of negative pictures that are used for training
# set to 1 for 100%
NEGATIVEPERCENTAGE = 0.1
if LEARN:
for concept in jdata:
for url in allurls:
if url in jdata[concept]["resources"]:
print(concept + " positive: " + url)
clarifai.positive(url, concept)
else:
if random() <= NEGATIVEPERCENTAGE:
print(concept + " negative: " + url)
clarifai.negative(url, concept)
clarifai.train(concept)
print("Trained " + jdata[concept]["name"] + " as " + concept)
else:
testurls = [
"http://static.panoramio.com/photos/large/23208353.jpg",
"http://geschichtspuls.de/wp-content/uploads/2008/bmw-museum-p0044099.jpg",
"http://thumbs.dreamstime.com/z/towers-frauenkirche-cathedral-church-munich-6210116.jpg",
"http://static.theculturetrip.com/images/56-256156-munich-opera-festival.jpg"
]
for url in testurls:
#instantiate a new Clarifai object
from clarifai_basic import ClarifaiCustomModel
clarifai = ClarifaiCustomModel()
# Give a few positive examples and a name for the custom concept.
clarifai.positive('https://upload.wikimedia.org/wikipedia/commons/0/02/Durian.jpg', 'durian')
clarifai.positive('https://www.organicfacts.net/wp-content/uploads/2013/05/Banana21.jpg', 'banana')
clarifai.positive('http://thumbs.dreamstime.com/z/sweet-sop-2898990.jpg', 'sweetsop')
clarifai.positive('http://www.centraliowaminiclub.org/wp-content/uploads/2015/08/pie.jpg', 'pie')
clarifai.positive('http://media.mercola.com/assets/images/food-facts/lychee-nutrition-facts.jpg', 'lychee')
# Give a few negative examples and a name for the custom concept.
clarifai.negative('https://www.organicfacts.net/wp-content/uploads/2013/05/Banana21.jpg', 'durian')
clarifai.negative('https://upload.wikimedia.org/wikipedia/commons/0/02/Durian.jpg', 'banana')
clarifai.negative('http://dreamatico.com/data_images/apple/apple-7.jpg', 'sweetsop')
clarifai.negative('https://upload.wikimedia.org/wikipedia/commons/8/8f/NYC-Diner-Bacon-Cheeseburger.jpg', 'pie')
clarifai.negative('http://www.newhealthguide.org/images/10415496/image001.jpg', 'lychee')
# Train the platform to learn your custom concept.
clarifai.train('durian')
clarifai.train('pie')
clarifai.train('banana')
clarifai.train('sweetsop')
clarifai.train('lychee')
# BOOK TRAINER #
#import all the stuffs
from clarifai_basic import ClarifaiCustomModel
concept = ClarifaiCustomModel()
#assign the stuffs
image_url = ""
tag = ""
ISBN_DICT = {tag:image_url}
#Provide some positive example images for concept
concept.positive(image_url, tag );
__author__ = 'montanawong'
from clarifai.client import ClarifaiApi
from clarifai_basic import ClarifaiCustomModel
concept = ClarifaiCustomModel()
clarifai_api = ClarifaiApi() # assumes environment variables are set.
url = "http://cdn.hitfix.com/photos/5621843/Grumpy-Cat.jpg"
concept.positive(url, "nelly")
concept.train('nelly')
#result = #concept.predict('https://pbs.twimg.com/profile_images/616542814319415296/#McCTpH_E.jpg', 'nelly')
#confidence = result['urls'][0]['score']
#print confidence
#result = clarifai_api.tag_images(open('images/cat.jpg'))
result = clarifai_api.tag_image_urls(url)
print result
from clarifai_basic import ClarifaiCustomModel from json import dumps from classifications import language concept = ClarifaiCustomModel() image_url = 'http://i.imgur.com/m4yadmu.jpg' model = 'letter_o' concept.positive(image_url, model) concept.train(model) for word in language: if word != model: print word concept.negative(image_url, word) concept.train(word) print concept.predict(image_url, model)
from clarifai_basic import ClarifaiCustomModel
import os
os.environ["CLARIFAI_APP_ID"]="f_LGpdh9gta77vih9bOl-96qNU4Nbn5_x6j412N_"
os.environ["CLARIFAI_APP_SECRET"]="_daBl2t7eC9nAGb-IBOdYLfm1uqoCQH6MPlvAJR1"
concept = ClarifaiCustomModel()
concept.positive('https://upload.wikimedia.org/wikipedia/commons/thumb/5/59/BMW_E90_Kirrinsannassa_2.jpg/280px-BMW_E90_Kirrinsannassa_2.jpg', 'car')
concept.train('car')
# pprint.pprint(categories)
concept = ClarifaiCustomModel()
# train model for each category
for cat in categories:
print cat['alias']
if cat['alias'] in DONE:
continue
# get images to train with
images = bing.image_search(query=cat['title'])
for image in images:
print image
print images.index(image)
concept.positive(image, cat['alias'])
# choose random food
neg_index = random.randint(0, len(categories)-1)
while neg_index == categories.index(cat):
neg_index = random.randint(0, len(categories)-1)
# find negative examples
neg_images = bing.image_search(query=categories[neg_index]['title'])
for image in neg_images:
print image
print neg_images.index(image)
concept.negative(image, cat['alias'])
concept.train(cat['alias'])
import os
import sys
from clarifai_basic import ClarifaiCustomModel
concept = ClarifaiCustomModel()
filename = sys.argv[1]
tag = sys.argv[2]
tagArray = ['HeavyMetal','Rap', 'Alternative','Pop','Country','Classical','Electronic']
with open(filename) as f:
for line in f:
line = line.rstrip()
print line
for genre in tagArray:
try:
if genre == tag:
print 'Yes'
concept.positive(line,tag)
#else:
#concept.negative(line,tag)
#pass
except:
print 'Fail' , line
pass
concept.train(tag)
COLOGNE_POSITIVES = [
'http://www.pcgames.de/screenshots/original/2012/01/Koelner_Dom.jpg',
'http://www.stadt-touristen.de/wp-content/uploads/2014/05/K%C3%B6ln-K%C3%B6lner-Dom.jpg',
'https://thejarrells.files.wordpress.com/2011/06/img_2440_2.jpg',
'https://upload.wikimedia.org/wikipedia/commons/5/55/Dmthoth_Koelner_dom_001.JPG'
]
COLOGNE_NEGATIVES = [
'http://www.dresden.citysam.de/fotos-dresden-p/tagesausfluege/meissen-2.jpg',
'http://rotary1880.de/bayreuth_eremitage/bilder/Meissen5.JPG',
'http://sachsen-blogger.de/wp-content/uploads/2013/07/DSCF4292.jpg'
]
for pos in COLOGNE_POSITIVES:
clarifai.positive(pos, concept_name)
for neg in COLOGNE_NEGATIVES:
clarifai.negative(neg, concept_name)
clarifai.train(concept_name)
COLOGNE_EXAMPLES = [
'https://upload.wikimedia.org/wikipedia/commons/6/65/Hasak_-_Der_Dom_zu_K%C3%B6ln_-_Bild_02_Westseite.jpg',
'https://upload.wikimedia.org/wikipedia/commons/thumb/0/03/Cologne_cathedrale_vue_sud.jpg/1024px-Cologne_cathedrale_vue_sud.jpg',
'http://www.adventuresofagoodman.com/wp-content/uploads/2012/10/Me-in-front-of-Kolner-Dam-Cologne-Germany.jpg'
]
NOT_COLOGNE = [
'https://upload.wikimedia.org/wikipedia/commons/3/39/Meissner-dom1.jpg',
'http://2.bp.blogspot.com/_I_Lf6SbZKI4/S9lrcYiNBHI/AAAAAAAACKw/119YjFzwne4/s1600/meissen%2B3.JPG'
from clarifai_basic import ClarifaiCustomModel
# assumes environment variables are set.
clarifai_api = ClarifaiCustomModel(
"Qu6z2uKlfDgqa7Atn1HlOBa3pakRBQHflQicLNr_",
"8WgQ8D4Dp9IW1JilxEfjXjgg1geq1zpaeT3P7Rk3")
files = ["dent", "perfect"]
for fileName in files:
f = open("images/" + fileName + ".txt")
urls = [url.strip() for url in f.readlines()]
for url in urls:
print("Training positive " + fileName +": " + url)
clarifai_api.positive(url, fileName)
f.close()
for other in files:
if other != fileName:
f = open("images/" + fileName + ".txt")
urls = [url.strip() for url in f.readlines()]
for url in urls:
print("Training negative " + other +": " + url)
clarifai_api.positive(url, fileName)
f.close()
clarifai_api.train(fileName)
print("Trained: " + fileName)
startIndex = 4 * sI
searchUrl = (
"http://ajax.googleapis.com/ajax/services/search/images?v=1.0&q="
+ searchTerm
+ "+lol&start="
+ str(startIndex)
)
f = fetcher.open(searchUrl)
deserialized_output = json.load(f)
for i in xrange(min(len(deserialized_output["responseData"]["results"]), 10)):
imageUrl = deserialized_output["responseData"]["results"][i]["unescapedUrl"]
googleimages.append(imageUrl)
for images in googleimages:
try:
concept.positive(images, searchTerm)
except ApiError:
pass
concept.train(searchTerm)
print champion
"""
try:
result = concept.predict('https://www.panerabread.com/panerabread/menu/details/smoked-ham-and-swiss-sandwich-whole.jpg/_jcr_content/renditions/smoked-ham-and-swiss-sandwich-whole.desktop.jpeg', searchTerm)
except ApiError:
pass
print result
"""
if len(sys.argv) < 4:
print(
"syntax: python .\predict.py [URL|File] [Result State] [Good|Bad] [if Bad: expected State")
exit()
# assumes environment variables are set.
clarifai_api = ClarifaiCustomModel(
"Qu6z2uKlfDgqa7Atn1HlOBa3pakRBQHflQicLNr_",
"8WgQ8D4Dp9IW1JilxEfjXjgg1geq1zpaeT3P7Rk3")
possibleStates=["dent", "perfect"]
url = sys.argv[1]
state = sys.argv[2].lower
predictResult = sys.argv[3].lower()
if predictResult == "good":
clarifai_api.positive(url, state)
for s in possibleStates:
if s != state:
clarifai_api.negative(url, s)
clarifai_api.train(state)
if predictResult == "bad":
expectedState = sys.argv[4].lower()
clarifai_api.positive(url, expectedState)
for s in possibleStates:
if s != expectedState:
clarifai_api.negative(url, s)
clarifai_api.train(expectedState)