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')
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 classify(url):
concept = ClarifaiCustomModel()
highscore = 0
classification = None
for d in data:
score = concept.predict(url, d["alias"])["urls"][0]["score"]
if score > 0.9:
return d
if score > highscore and score > 0.5:
classification = d
highscore = score
print classification["alias"]
print score
return classification
def predict(url): concept = ClarifaiCustomModel() max_confidence = 0.0 classification = None for word in language.keys(): #print word result = concept.predict(url, word) confidence = result['urls'][0]['score'] print word, confidence if confidence > max_confidence: max_confidence = confidence classification = word if classification == None: return None else: return (classification, max_confidence)
def goToBookScouterURL(url_in):
clarifai = ClarifaiCustomModel()
probMax = 0
maxScoreISBN = ""
print "Searching textbooks...."
for isbn in isbnList.getISBNList():
result = clarifai.predict(url_in, encode.NumericToAlpha(isbn))
data = json.dumps(result)
jdata = json.loads(data)
jresults = jdata['urls'][0]['score']
#print str(jresults) + ":" + encode.NumericToAlpha(isbn)
if result['urls'][0]['score'] > probMax:
probMax = result['urls'][0]['score']
maxScoreISBN = isbn
isbn = maxScoreISBN
home = "http://bookscouter.com"
ext1 = "/prices.php?isbn="
ext2 = "&searchbutton=Sell"
url = home + ext1 + isbn + ext2
print "Found Textbook"
print "ISBN-10 "+isbn+" with probablity: "+"%0.3f" % probMax
webbrowser.open_new(url) #go to bookscouter.com
import sys
from clarifai_basic import ClarifaiCustomModel
concept = ClarifaiCustomModel()
url = sys.argv[1]
max = 0
prediction = ''
tagArray = ['HeavyMetal','Rap', 'Alternative','Pop','Country','Classical','Electronic']
for genre in tagArray:
#print genre
result = concept.predict(url,genre)
confidence = float(result['urls'][0]['score'])
#if confidence > max:
#max = confidence
#prediction = genre
print genre, 'Confidence rate: ', confidence
#print prediction
from clarifai_basic import ClarifaiCustomModel
import sys
if len(sys.argv) < 2:
print("please provide a valid URL or File Path")
print("syntax: python .\predict.py [URL|File]")
exit()
# assumes environment variables are set.
clarifai_api = ClarifaiCustomModel("Qu6z2uKlfDgqa7Atn1HlOBa3pakRBQHflQicLNr_",
"8WgQ8D4Dp9IW1JilxEfjXjgg1geq1zpaeT3P7Rk3")
url = sys.argv[1]
result = clarifai_api.predict(url, 'perfect')
perfect = result["urls"][0]["score"]
result = clarifai_api.predict(url, 'dent')
dent = result["urls"][0]["score"]
output = {"url": url, "state": "undef", "score": 0}
if dent > perfect:
output["state"] = "dent"
output["score"] = dent
else:
output["state"] = "perfect"
output["score"] = perfect
from clarifai_basic import ClarifaiCustomModel
import sys
clarifai = ClarifaiCustomModel()
result = clarifai.predict('http://comps.canstockphoto.com/can-stock-photo_csp10316699.jpg', 'test5')
print result
"http://pad2.whstatic.com/images/thumb/b/bb/Get-Rid-of-Skin-Moles-Step-2-Version-3.jpg/670px-Get-Rid-of-Skin-Moles-Step-2-Version-3.jpg"
result = clarifai.predict("http://pad2.whstatic.com/images/thumb/b/bb/Get-Rid-of-Skin-Moles-Step-2-Version-3.jpg/670px-Get-Rid-of-Skin-Moles-Step-2-Version-3.jpg", 'test5')
print result
result = clarifai.predict("http://cancerousmolepictures.com/large/6/Cancerous-Mole-Pictures-1.jpg" , 'test5')
print result
try:
urllib2.urlopen(i)
except urllib2.HTTPError, e:
NEGATIVES.remove(i)
except urllib2.URLError, e:
NEGATIVES.remove(i)
concept_name = 'people'
for positive_example in POSITIVES:
clarifai.positive(positive_example, concept_name)
for negative_example in NEGATIVES:
clarifai.negative(negative_example, concept_name)
clarifai.train(concept_name)
#XXXXXXXXXXXXXXXXXXX USAGE XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
EXAMPLES = [
'https://blog-blogmediainc.netdna-ssl.com/SportsBlogcom/filewarehouse/37676/4b8c8d0728f0fb6e78b1071445770fab.jpg',
'http://www.africacradle.com/wp-content/uploads/2015/08/article-2441512-02650200000005DC-411_634x380.jpg'
]
NOT = [
'https://clarifai-test.s3.amazonaws.com/2141620332_2b741028b3.jpg',
'https://clarifai-test.s3.amazonaws.com/grateful_dead230582_15-52.jpg'
]
for test in EXAMPLES + NOT:
result = clarifai.predict(test, 'car')
ans = 'ACCEPTED' if (result['urls'][0]['score']>0.7) else 'REJECTED'
print result['status']['message'], "%0.3f" % result['urls'][0]['score'], ans, result['urls'][0]['url']
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:
print(url)
results = []
for concept in jdata:
result = clarifai.predict(url, concept)
results.append([concept, result["urls"][0]["score"]])
results = sorted(results, key=lambda pair: pair[1], reverse=True)
pprint(results)
import sys
from clarifai_basic import ClarifaiCustomModel
good = False
concept = ClarifaiCustomModel()
filename = sys.argv[1]
g = sys.argv[2]
tagArray = ['HeavyMetal','Rap', 'Alternative','Pop','Country','Classical','Electronic']
file = open('ElecExcel.txt','w')
file.write('{url, genre, HeavyMetal, Rap, Alternative, Pop, Country, Classical, Electronic}\n')
with open(filename) as f:
for line in f:
good = False
line = line.rstrip()
try:
for genre in tagArray:
result = concept.predict(line,genre)
good = True
except:
good = False
if(good is True):
print line
file.write('{')
file.write(line)
file.write(', ')
file.write(g)
for genre in tagArray:
file.write(', ')
result = concept.predict(line,genre)
confidence = float(result['urls'][0]['score'])
file.write(repr(confidence))
file.write('}\n')
]
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'
]
for test in COLOGNE_EXAMPLES + NOT_COLOGNE:
result = clarifai.predict(test, concept_name)
print result['status']['message'], "%0.3f" % result['urls'][0]['score'], result['urls'][0]['url']
import sys
if len(sys.argv) < 2:
print("please provide a valid URL or File Path")
print("syntax: python .\predict.py [URL|File]")
exit()
# assumes environment variables are set.
clarifai_api = ClarifaiCustomModel(
"Qu6z2uKlfDgqa7Atn1HlOBa3pakRBQHflQicLNr_",
"8WgQ8D4Dp9IW1JilxEfjXjgg1geq1zpaeT3P7Rk3")
url = sys.argv[1]
result = clarifai_api.predict(url, 'perfect')
perfect = result["urls"][0]["score"]
result = clarifai_api.predict(url, 'dent')
dent = result["urls"][0]["score"]
output = {
"url": url,
"state": "undef",
"score": 0
}
if dent > perfect:
output["state"] = "dent"
output["score"] = dent
from clarifai_basic import ClarifaiCustomModel
# assumes environment variables are set.
clarifai_api = ClarifaiCustomModel(
"Qu6z2uKlfDgqa7Atn1HlOBa3pakRBQHflQicLNr_",
"8WgQ8D4Dp9IW1JilxEfjXjgg1geq1zpaeT3P7Rk3")
result = clarifai_api.predict('http://www.bodyshopzone.com/archives/collision/side_impact_1/cxeqia01apr_Equinox_tbone.jpg', 'dent')
print("Dent: ")
print(result)
#TGhikOhhzTI/AAAAAAAAAPo/nYmF9hULrCU/s1600/IMG_1437.JPG', 'scratch')
#print("Scratch: ")
#print(result)
result = clarifai_api.predict('http://www.bodyshopzone.com/archives/collision/side_impact_1/cxeqia01apr_Equinox_tbone.jpg', 'perfect')
print("Perfect: ")
print(result)
testConcept.negative('https://absolutelymindboggling.files.wordpress.com/2012/04/pepsi-bottle.jpg', TEST_TAG)
testConcept.negative('http://static.caloriecount.about.com/images/medium/pepsi-diet-183174.jpg', TEST_TAG)
testConcept.negative('http://couponing4you.net/wp-content/uploads/2015/07/pepsi.jpg', TEST_TAG)
testConcept.negative('https://stephanieolivieri.files.wordpress.com/2014/02/pepsi-cola_1940s.png', TEST_TAG)
testConcept.train(TEST_TAG)
if __name__ == "__main__":
conceptTrainer() #Run the custom concept trainer.
done = False
while not done:
result = None
inputURL = raw_input("Enter an image URL to test: ")
if inputURL != None:
result = testConcept.predict(inputURL, TEST_TAG)
if result['status']['message'] == 'Success':
confidenceScore = result['urls'][0]['score']
print "Your confidence score is = " + str(confidenceScore) + " Hope this makes sense!"
else:
print "We encountered some weird error!"
else:
print "ERROR IN URL!@#$%^"
doneVal = raw_input("Are we done here? (y/n): ")
if doneVal == 'y' or doneVal == 'Y' or doneVal == 'Yes' or doneVal == 'yes':
done = True
elif doneVal == 'n' or doneVal == 'N' or doneVal == 'No' or doneVal == 'no':
print "All right then, let's go again!"
else:
print "That's not a valid input, so I'm just going to do this all over again."
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)
class Client(object):
def __init__(self):
self.clarifai = ClarifaiCustomModel(app_id, app_secret)
self.jdata = getAllJSONData()
def processImage(self, latitude, longitude, url, homelat, homelong):
concept = self.__processImage(latitude, longitude, url)
if concept is None:
return None
return self.__getInfos(concept, homelat, homelong)
def __processImage(self, latitude, longitude, url):
filtered = getConceptsByGPS(latitude, longitude, self.jdata, 1)
results = []
for concept, _ in filtered:
result = self.clarifai.predict(url, concept)
results.append([concept, result["urls"][0]["score"]])
results = sorted(results, key=lambda pair: pair[1], reverse=True)
if len(results) == 0:
return None
result = results[0]
if result[1] < 0.5:
return None
return result[0]
def processImageDebug(self, url):
print("Start recognizing...")
filtered = getConceptsByGPS(0, 0, self.jdata, 40000)
results = []
count = len(filtered)
for concept, _ in filtered:
result = self.clarifai.predict(url, concept)
results.append([concept, result["urls"][0]["score"]])
results = sorted(results, key=lambda pair: pair[1], reverse=True)
if len(results) == 0:
return None
result = results[0]
if result[1] < 0.5:
return None
return result[0]
def getInfos(self, concept):
target = self.jdata[concept]
name = target["name"]
city = target["city"]
return (name, city)
def __getInfos(self, concept, homelat, homelong):
target = self.jdata[concept]
name = target["name"]
city = "NiHier" # target["city"]
tlong = target["longitude"]
tlat = target["latitude"]
distance = distance_on_unit_sphere(homelat, homelong, tlat, tlong)
return (name, distance, city)
# 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']) # Our output is the following. Your results will vary as there are some non-deterministic elements # of the algorithms used. # Success 0.797 http://phishthoughts.com/wp-content/uploads/2012/07/photo-1-11-e1342391144673.jpg # Success 0.706 http://bobmarley.cdn.junip.com/wp-content/uploads/2014/10/DSC01226-e1311293061704.jpg # Success 0.356 http://farm3.static.flickr.com/2161/2141620332_2b741028b3.jpg # Success 0.273 http://www.mediaspin.com/joel/grateful_dead230582_15-52.jpg
