def IsPizza(image1):
app = ClarifaiApp(api_key='f53aa080f78d418fb75051b03f80d3c4')
model = app.models.get('food-items-v1.0')
# image = ClImage(url='https://s3.amazonaws.com/clarifai-api/img2/prod/small/b8ea54acf15c4642a5ba985bda8b9633/de65940cc1524bf3a01563a7bf8ddac7')
if validators.url(image1):
image = ClImage(image1)
else:
image = ClImage(open(image1, 'rb'))
response = model.predict([image])
elements = response['outputs'][0]['data']['concepts']
isPizzaThreshold = 0.85
isPineappleThreshold = 0.3
isPizza = False
isPineapple = False
for element in elements:
if (element['name']
== 'pizza') and (element['value'] > isPizzaThreshold):
isPizza = True
if (element['name']
== 'pineapple') and (element['value'] > isPineappleThreshold):
isPineapple = True
if isPineapple and isPizza:
print('Get the hell out of here')
elif isPizza:
print(
'Your pizza passes the standards of the anti pineapple pizza community.'
)
def uploadTrain():
apparel_Model = APP.models.get('apparel')
imgAry = []
import mongo
data = mongo.getTrain()
print(len(data))
for item in data[401:]:
#print(item['Unique_No'])
try:
obj = open('./img/' + str(item['Unique_No']) + '.jpg', "rb")
# print(obj)
conceptObj = apparel_Model.predict([ClImage(file_obj=obj)])
mainConcept = ''
for key in conceptObj['outputs'][0]['data']['concepts']:
mainConcept = key['name']
break
imgAry.append(
ClImage(file_obj=obj,
image_id=str(item['Unique_No']) + '.jpg',
concepts=[mainConcept],
metadata={'key': mainConcept}))
except Exception as e:
print(e)
pass
#print(imgAry[:10])
APP.inputs.bulk_create_images(imgAry)
# bulkInput()
# search()
# delAll()
# uploadTrain()
# searchAndUpload()
def whichOneIsCuter(image_url1, image_url2):
img1 = ClImage(url = image_url1, allow_dup_url=True)
img2 = ClImage(url = image_url2, allow_dup_url=True)
app.inputs.bulk_create_images([img1, img2])
cute = app.inputs.search_by_predicted_concepts(concept='cute')
for i in range(len(cute)): print (cute[i].url)
def bulkInsert(start, end, path, filesAry):
apparel_Model = APP.models.get('apparel')
imgAry = []
print(filesAry)
try:
for filename in filesAry[start:end]:
obj = open(os.path.join(path, filename), "rb")
#print(os.path.join(path, filename))
conceptObj = apparel_Model.predict([ClImage(file_obj=obj)])
#print("enter")
mainConcept = ''
for key in conceptObj['outputs'][0]['data']['concepts']:
mainConcept = key['name']
break
imgAry.append(
ClImage(file_obj=obj,
image_id=filename,
concepts=[mainConcept],
metadata={'key': mainConcept}))
# break
except:
pass
#print(imgAry[:10])
APP.inputs.bulk_create_images(imgAry)
def main():
app = ClarifaiApp()
# get image height and width
img = Image.open('./img/face1.jpeg')
width, height = img.size
# detect boundaries of face
face_model = app.models.get('face-v1.3')
image = ClImage(file_obj=open('./img/face1.jpeg', 'rb'))
face_result = face_model.predict([image])
#print(face_result)
face = face_result['outputs'][0]['data']
# get boundaries for face
for i in face.values():
for j in i:
top = i[0]['region_info']['bounding_box']['top_row']
left = i[0]['region_info']['bounding_box']['left_col']
right = i[0]['region_info']['bounding_box']['right_col']
bottom = i[0]['region_info']['bounding_box']['bottom_row']
# calculate height and width
top = int(top * height)
left = int(left * width)
right = int(right * width)
bottom = int(bottom * height)
# crop image
img2 = img.crop((left, top, right, bottom))
img2 = img2.resize((width, height)).save('./img/new.jpeg')
# detect color in cropped image
image2 = ClImage(file_obj=open('./img/new.jpeg', 'rb'))
color_model = app.models.get('color')
color_result = color_model.predict([image2])
#print(result)
color = color_result['outputs'][0]['data']
#print(color)
a = 0
mval = 0
for i in color.values():
for j in i:
hex_dec = i[a]['w3c']['hex']
name = i[a]['w3c']['name']
value = i[a]['value']
a = a + 1
if hex_dec != '#ffffff' or hex_dec != '#000000':
if value > mval:
mval = value
skin = name
dec = hex_dec
print(mval, skin, dec)
def add_images(self, images, class_type):
for chunk in chunks(images, 128):
if class_type == 'positive':
cl_images = [
ClImage(filename=path, concepts=[self.concept_name])
for path in chunk
]
else:
cl_images = [
ClImage(filename=path, not_concepts=[self.concept_name])
for path in chunk
]
self.app.inputs.bulk_create_images(cl_images)
def __init__(self, source):
url_regex = re.compile(
r'^(?:http|ftp)s?://' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain...
r'localhost|' #localhost...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$', re.IGNORECASE)
if re.match(url_regex, source) is not None:
self.image = ClImage(url=source)
else:
self.image = ClImage(filename=source)
def check_friend(self, frame):
img_name = 'tmp.png'
cv2.imwrite(img_name, frame)
print("{} written!".format(img_name))
differences = []
for friend in self.friends:
test_img = ClImage(filename=img_name)
test_img_pred = self.model.predict([test_img])
while True:
try:
pic2 = test_img_pred['outputs'][0]['data']['regions'][0][
'data']['embeddings'][0]['vector']
break
except KeyError:
print("Oops! That was no valid number. Try again...")
# differences.append(L2(pic2, friend[1]))
differences.append(neural_network(pic2, friend[1]))
if len(differences) > 0:
if min(differences) < self.limit:
self.friend_name = self.friends[differences.index(
min(differences))][0]
self.show_message = 1
else:
self.show_message = 2
print('Difference is: {}'.format(min(differences)))
else:
self.show_message = 2
def run(myApi):
#Import statements + constructors
import os
from clarifai.rest import ClarifaiApp
from clarifai.rest import Image as ClImage
#Creates Clarifai App
app = ClarifaiApp(api_key=myApi)
#Resets the server to 0 images, avoids duplicates
app.inputs.delete_all()
#URL
URL_FILE_NAME = 'vacations_url.txt'
URL_FILE_PATH = os.path.join(os.path.curdir, URL_FILE_NAME)
#Reads the urls of images from txt file
with open(URL_FILE_PATH) as data_file:
images = [url.strip() for url in data_file]
row_count = len(images)
#Making images into a list that convers into being uploadable to the server
imageList = []
for i in range(row_count):
imageList.append(ClImage(url=images[i], allow_dup_url=False))
app.inputs.bulk_create_images(imageList)
#NAMES
NAMES_FILE_NAME = 'vacations_names.txt'
NAMES_FILE_PATH = os.path.join(os.path.curdir, NAMES_FILE_NAME)
return
def determine(image):
# get the general model
#model = app.models.get("general-v1.3")
model = app.models.get("Ocean Cleanup")
image = ClImage(file_obj=open(image, 'rb'))
output = str(model.predict([image]))
#find concepts related to image
index = output.find("u'name'") + 1
conc = []
for i in range(1, output.count("u'name'")):
index = output.find("u'name'", index + 1)
conc.append(output[index + 11:output.find("'", index + 13)])
#obj = "not found"
#for i in conc:
# if (i == "bottle" or i == "can" or i == "balloon" or i == "cigarettes" or i == "plastic bag"):
# obj = conc[i]
# break
jsonTags = model.predict([image])
#print (jsonTags['outputs'][0]['data']['concepts'][0]['name'])
names = []
for tag in jsonTags['outputs'][0]['data']['concepts']:
names.append(tag['name'])
obj = "not found"
for i in names:
if (i == "bottle" or i == "can" or i == "balloon" or i == "cigarettes"
or i == "plastic bag"):
obj = i
return obj
def clarifai_prediction(image):
app = ClarifaiApp(api_key='f789eaa60ab04869a2b80c752daa3484')
model = app.models.get('Mood Recognition')
climage = ClImage(filename=image)
hello = model.predict([climage])
test = hello['outputs'][0]['data']['concepts'][0]['id']
return test
def add_category(post):
app = ClarifaiApp(api_key=API_KEY)
model = app.models.get("general-v1.3")
image = ClImage(file_obj=open(post.image.path, 'rb'))
response = model.predict([image])
if response["status"]["code"] == 10000:
if response["outputs"]:
if response["outputs"][0]["data"]:
if response["outputs"][0]["data"]["concepts"]:
for index in range(
0,
len(response["outputs"][0]["data"]["concepts"])):
category = CategoryModel(
post=post,
category_text=response["outputs"][0]["data"]
["concepts"][index]["name"])
category.save()
else:
print("No Concepts List Error")
else:
print("No Data List Error")
else:
print("No Outputs List Error")
else:
print("Response Code Error")
def ethnicity_guesser(image_url):
model = app.models.get("demographics")
image = ClImage(url = image_url)
data = model.predict([image])
ethnicity = data['outputs'][0]['data']['regions'][0]['data']['face']['multicultural_appearance']['concepts'][0]['name']
# concept_names = [concept['name'] for concept in concepts]
return ethnicity
def identify():
"""!identify <image>
Queries clarif.ai to identify the top 10 concepts within an image.
Usage: !identify <image>"""
msg = Message(flask.request.form)
fileName = msg.fileName
if fileName == 'noFile':
return 'Please supply a file.'
elif fileName == 'fileError':
return 'There was an error selecting the last file transfer.'
else:
init_clarifai()
model = clarifai.models.get('general-v1.3')
image = ClImage(file_obj=open(fileName, 'rb'))
imageData = model.predict([image])
try:
imageData = imageData['outputs'][0]['data']['concepts'][:10]
imageData = [x['name'] for x in imageData]
return reduce_output(imageData, delimiter=', ')
except:
return 'Error'
def check_image(browser, clarifai_api_key, img_tags, img_tags_skip_if_contain, logger, full_match=False, logging=False, picture_url=None):
"""Uses the link to the image to check for invalid content in the image"""
clarifai_api = ClarifaiApp(api_key=clarifai_api_key)
# set req image to given one or get it from current page
if picture_url is None:
img_link = get_imagelink(browser,logger,logging)
else:
img_link = picture_url
# Uses Clarifai's v2 API
model = clarifai_api.models.get('general-v1.3')
image = ClImage(url=img_link)
result = model.predict([image])
clarifai_tags = [concept.get('name').lower() for concept in result[
'outputs'][0]['data']['concepts']]
for (tags, should_comment, comments) in img_tags:
if should_comment:
if given_tags_in_result(tags, clarifai_tags, full_match):
return True, comments
else:
if given_tags_in_result(tags, clarifai_tags, full_match):
if not given_tags_in_result(img_tags_skip_if_contain, clarifai_tags, full_match):
logger.info('Not Commenting, image reco contains: "{}".'.format(', '.join(list(set(clarifai_tags)&set(tags)))))
return False, []
return True, []
def label_images(input_dir, filetype, season):
"""
Label images to be fed to Clarifai Model
"""
# TODO: Figure out how to add concepts.
# Assign them IDs and specify concepts and non concepts.
# figure out the bulk_create_images response error.
seasons = ["spring","summer","fall", "winter"]
concept = season
seasons.remove(str(season))
nonconcepts = seasons
print(concept)
print(nonconcepts)
app = ClarifaiApp(api_key='f7e11a3064f8468087ca656dce9e7abc')
images = [os.path.join(input_dir,i) for i in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir,i)) and i.endswith(filetype)]
#print(app.inputs.get_all())
for image in app.inputs.get_all():
print(image.input_id)
#app.concepts.bulk_create(['id1', 'id2','id3','id4'], ['spring', 'summer','fall', 'winter'])
#df = pd.DataFrame(columns=['Image_Name','Colors','Hex_Numbers'])
img_list = []
#for count, picture in enumerate(images,1):
image = ClImage(url='https://samples.clarifai.com/metro-north.jpg', concepts=[concept], not_concepts=[nonconcepts])
def output_prediction(word):
app = ClarifaiApp(api_key='b93c9746c9cc4a79baec13d0a83739cb')
model = app.models.get('food-items-v1.0')
image = ClImage(url=word)
data = model.predict([image])
food_list = [data['outputs'][0]['data']['concepts'][0]['name']]
return (food_list[0], get_recipe(food_list, [])[0])
def isThisFood(image_url):
model = app.models.get('food-items-v1.0')
image = ClImage(url= image_url)
response_data = model.predict([image])
concepts = response_data['outputs'][0]['data']['concepts']
concept_names = [concept['name'] for concept in concepts]
return concept_names
def my_callback(channel):
GPIO.output(pir_led,True) #Turn on LED
time.sleep(0.5) # leave LED on for 0.5 seconds, just for test
camera = picamera.PiCamera()
camera.vflip = True
GPIO.output(camera_led, GPIO.HIGH)
camera.capture('slika.jpg')
GPIO.output(camera_led, GPIO.LOW)
camera.close()
GPIO.output(pir_led,False) #turn off LED
danger = False
app = ClarifaiApp(api_key='------------key-----------')
model = app.models.get('general-v1.3')
image = ClImage(file_obj=open('slika.jpg', 'rb'))
response=model.predict([image])
concepts = response['outputs'][0]['data']['concepts']
for concept in concepts:
print(concept['name'], concept['value'])
if any(x in concept['name'] for x in ["bird"]): # can be more filters: ["danger","man","people","adult","weapon","safety"]):
danger = True
#print('bird on camera')
if danger:
print('sviraj')
#zvuk
song = "countdown.mp3"
pygame.init()
pygame.mixer.init()
pygame.mixer.music.load(song)
pygame.mixer.music.play()
while pygame.mixer.music.get_busy():
pygame.time.Clock().tick(10)
def array_to_byte(inputs):
fig = np.around((inputs + 0.5) * 255)
fig = fig.astype(np.uint8).squeeze()
img = Image.fromarray(fig)
imgByteArr = io.BytesIO()
img.save(imgByteArr, format="PNG")
return ClImage(file_obj=imgByteArr)
def lewd():
"""!lewd <image>
Queries clarif.ai to detect if an image is 'lewd'.
Usage: !lewd <image>"""
msg = Message(flask.request.form)
fileName = msg.fileName
if fileName == 'noFile':
return 'Please supply a file.'
elif fileName == 'fileError':
return 'There was an error selecting the last file transfer.'
else:
init_clarifai()
model = clarifai.models.get('nsfw-v1.0')
if not model:
return 'Model no longer exists.'
image = ClImage(file_obj=open(fileName, 'rb'))
responses = []
imageData = model.predict([image])
result = imageData['outputs'][0]['data']['concepts'][0]
if result['name'] == u'nsfw':
responses.append('LEEEWWWDDD!!!!!')
responses.append('accuracy: %f' % result['value'])
return responses
else:
return 'not lewd'
def extract_demography(app, url):
if app is None:
app = ClarifaiApp(api_key='9a2117f4967a4f2d92e84adb221c5cc1')
model = app.models.get("demographics")
if isinstance(url, str):
prediction = model.predict_by_url(url=url)
else:
image = ClImage(file_obj=url)
prediction = model.predict([image])
try:
regions = prediction['outputs'][0]['data']['regions']
for region in regions[:1]: ## analyse only first guy on the picture
concepts = region['data']['face']
age = sorted(concepts['age_appearance']['concepts'], key=lambda j: -j['value'])[0]['name']
gender = sorted(concepts['gender_appearance']['concepts'], key=lambda j: -j['value'])[0]['name']
multiculture = sorted(concepts['multicultural_appearance']['concepts'], key=lambda j: -j['value'])[0]['name']
result = {
"age": age,
"gender": gender,
"multiculture": multiculture
}
return result
except KeyError as e:
return {}
def IsPizza(url):
image = ClImage(url=url)
response = model.predict([image])
elements = response['outputs'][0]['data']['concepts']
isPizzaThreshold = 0.85
isPineappleThreshold = 0.3
isPizza = False
isPineapple = False
for element in elements:
if (element['name'] == 'pizza') and (element['value'] > isPizzaThreshold):
isPizza = True
if (element['name'] == 'pineapple') and (element['value'] > isPineappleThreshold):
isPineapple = True
if isPineapple and isPizza:
response = 'FATAL ERROR! pineapple detected.'
elif isPizza:
response = 'Your pizza passes the standards of the anti pineapple pizza community.'
else:
response = 'That is no pizza, man.'
return response
def call_vision_api(image_filename, api_keys):
clarifai_key = api_keys['clarifai']
app = ClarifaiApp(api_key=clarifai_key)
model = app.models.get('general-v1.3')
image = ClImage(file_obj=open(image_filename, 'rb'))
result = model.predict([image])
return result
def predict_color(app, url, num_top=1):
if app is None:
app = ClarifaiApp(api_key='9a2117f4967a4f2d92e84adb221c5cc1')
model = app.models.get("color")
# predict with the model
if isinstance(url, str):
prediction = model.predict_by_url(url=url)
else:
image = ClImage(file_obj=url)
prediction = model.predict([image])
colors = prediction['outputs'][0]['data']['colors']
#example : [{'raw_hex': '#d4ced6', 'w3c': {'hex': '#d3d3d3', 'name': 'LightGray'}, 'value': 0.59625}, {'raw_hex': '#e42123', 'w3c': {'hex': '#dc143c', 'name': 'Crimson'}, 'value': 0.09675}, {'raw_hex': '#24242a', 'w3c': {'hex': '#000000', 'name': 'Black'}, 'value': 0.10125}, {'raw_hex': '#928f76', 'w3c': {'hex': '#808080', 'name': 'Gray'}, 'value': 0.16375}, {'raw_hex': '#f08b85', 'w3c': {'hex': '#f08080', 'name': 'LightCoral'}, 'value': 0.042}]
colors = list(
map(lambda c: {
"embedding": c['w3c']['hex'],
"prob": c['value']
}, colors))
colors.sort(key=lambda x: -x['prob'])
colors = colors[:min(num_top, len(colors))]
logger.info("predict_color. colors are: {}".format(colors))
return colors
def clarifai(runningCount, user_name):
#need img_link
clarifai_api = ClarifaiApp(api_key='a88172a02f3e4036b5b13bdee391e7f3')
img_link = browser.find_element_by_xpath('//img[@class = "_2di5p"]') \
.get_attribute('src')
#print(img_link)
#general model
#model = clarifai_api.models.get('general-v1.3')
#nsfw model
model = clarifai_api.models.get('e9576d86d2004ed1a38ba0cf39ecb4b1')
image = ClImage(url=img_link)
#only predicts image, not videos
result = model.predict([image])
#print(result)
sfwValue = float(result['outputs'][0]['data']['concepts'][0]['value'])
print(sfwValue)
if sfwValue >= 0.80:
print("passed clarifai")
#finding hearts when on picture - ready to use.
#hearts = browser.find_elements_by_xpath("//a[@role='button']/span[@class='_8scx2']")
hearts = browser.find_elements_by_class_name('coreSpriteHeartOpen')
#/html/body/div[4]/div/div[2]/div/article/div[2]/section[1]/a[1]/span
print(len(hearts))
sleep(5)
ActionChains(browser).move_to_element(hearts[0]).click().perform()
print("liked photo")
sleep(5)
runningCount += 1
print("Total likes this round: ", runningCount)
#INSERT USERNAME AND TIME TO DATABASE using sqlite3
#timeNow = time.ctime(int(time.time()))
#insert_user(user_name, timeNow)
#query all to make list
#allUsers = query_users()
#print(allUsers)
#delete all usernames in db and check to make sure they cleared
#delete_table()
#anyLeft = query_users()
#print(anyLeft)
#INSERT USERNAME AND TIME TO DATABASE using postgreSQL
timeNow = time.ctime(int(time.time()))
insert_user(user_name, timeNow)
else:
print("failed clarifai")
def upload():
if request.method == 'POST':
file = request.files['file']
if file and allowed_file(file.filename):
now = datetime.now()
username = request.cookies.get('userID')
print(username)
filename = os.path.join(app.config['UPLOAD_FOLDER'], username)
filename = os.path.join(app.config['UPLOAD_FOLDER']+username, "%s.%s" % (now.strftime("%Y-%m-%d-%H-%M-%S-%f"), file.filename.rsplit('.', 1)[1]))
print(app.config['UPLOAD_FOLDER']+username)
file.save(filename)
#image = ClImage(url='https://samples.clarifai.com/metro-north.jpg')
image = ClImage(file_obj=open(filename, 'rb'))
pred = model.predict([image])
user_images.insert_one(
{
"username": username,
"filepath": filename,
"clarifai_data": pred
}
)
print(pred)
return jsonify({"success":True})
elif(request.method == 'GET'):
return render_template("upload.html")
def handler():
app = ClarifaiApp(api_key=env_vars.clarifai_api_key)
model = app.models.get('food-items-v1.0')
img = ClImage(file_obj=open('pics/fridge2.jpeg', 'rb'))
pred = model.predict([img])
items = ""
for con in pred['outputs'][0]['data']['concepts']:
items += json.dumps(con['name'])[1:-1] + ", "
items = items[:-2]
params = {"ingredients": items}
response = unirest.get(
"https://spoonacular-recipe-food-nutrition-v1.p.mashape.com/recipes/findByIngredients",
params=params,
headers={
"X-Mashape-Key":
"IwLJqO6bAtmshh9PXpr3EhpVvpJWp1smLbBjsnwCNvi8KTvV4u",
"X-Mashape-Host":
"spoonacular-recipe-food-nutrition-v1.p.mashape.com"
})
title_video_id = {}
for res in response.body:
title = json.dumps(res['title'])[1:-1]
video_id = youtube.youtube_search(title)
if video_id:
title_video_id[title] = str(video_id)
return title_video_id
def extractImageFeatures(imgs_files):
classes = ['BH','BR','O','P','SH']
fs = FileSystemStorage(location=settings.FEAT_ROOT)
app = ClarifaiApp('jiBNMPUJ4QR7GT-VRDL6ZdrvfgbYRzK6jX7DQKXp','jlwJ1AKBNSN1B8205X_3oqaZLYrxOa2kL3DUKX77')
app.auth.get_token()
batch = 90
sent_count = 0
model = app.models.get('general-v1.3')
concepts = []
while sent_count < len(imgs_files):
imgs = map(lambda x: ClImage(file_obj=open(x, 'rb'), image_id=x),
imgs_files[sent_count:min(len(imgs_files), sent_count + batch)])
resp = model.predict(imgs)
sent_count += min(len(imgs), sent_count + batch)
filenames = map(lambda x: x['input']['id'], resp['outputs'])
concepts += getConcepts(resp)
file_paths = []
vocabs = os.listdir(settings.VOCAB_ROOT)
vocabs = map(lambda x: os.path.join(settings.VOCAB_ROOT,x),vocabs)
for j in range(len(vocabs)):
vocab = vocabs[j]
with open(vocab, 'r') as f:
vocab = f.readlines()
for i in range(len(concepts)):
fv = np.array([map(lambda x: concepts[i].get(x[:-1],0.0), vocab)])
f_path = fs.path(imgs_files[i].split('/')[-1])+"_%s.feat"%classes[j]
file_paths.append(f_path)
np.savetxt(f_path, fv, fmt='%s')
return file_paths
def clarify(urlstr):
if 'http:' in urlstr:
x = ClImage(url = urlstr)
else:
x = app.inputs.create_image_from_filename(urlstr)
predictions = str(model.predict([x]))
#Outputing the response string to a file and formatting it
fil = open('output.txt', 'w')
for i in predictions:
if i == '}':
fil.write('}\n')
else:
fil.write(i)
#Takes the highest confidence line
fil = open('output.txt','r')
for line in fil:
if (('\'id\': \'ai' in line) == True):
result = line
print(line)
break
#Takes only the name
result1 = result[(result.find('\'name\':')+7):]
result2 = result1[:result1.find(',')]
return result2
