def test_is_relevant(self):
url = 'http://i.huffpost.com/gen/1321037/thumbs/o-KATE-LANPHEAR-570.jpg'
url = 'http://schoolofba.com/wp-content/uploads/2014/09/faces-300x300.jpg'
url = 'http://img07.deviantart.net/ee95/i/2011/058/8/4/many_faces_of_david_tennant_2_by_pfeifhuhn-d3ag5g6.jpg'
url = 'http://face-negotiationtheory.weebly.com/uploads/4/2/1/6/4216257/1771161.jpg'
url = 'http://www.travelmarketing.fr/wp-content/uploads/2014/04/multiple-faces-e1401642770899.jpg'
# url = 'http://www.joancanto.com/blog/wp-content/uploads/2008/07/unai-jvcanto.jpg'
# url = 'http://p1cdn02.thewrap.com/images/2015/01/e-fashion-police-joan-rivers-premiere.jpg'
file = os.path.join('images', url.split('/')[-1])
print('file:' + file)
img = cv2.imread(file)
if img is None:
print('img doesnt exists locally, getting from url')
response = requests.get(url, stream=True)
with open(file, 'wb') as out_file:
shutil.copyfileobj(response.raw, out_file)
del response
time.sleep(0.5)
img = cv2.imread(file)
if img is None:
print('cant get image')
return
relevance = background_removal.image_is_relevant(img)
print relevance
if len(relevance.faces) > 0:
for face in relevance.faces:
print('face:' + str(face))
cv2.rectangle(img, (face[0], face[1]),
(face[0] + face[2], face[1] + face[3]),
[0, 255, 0])
cv2.imshow('faces', img)
cv2.waitKey(0)
def mr8_4_demo(img, fc=None):
mask, labels, pose = paperdoll_parse_enqueue.paperdoll_enqueue(
img, async=False).result[:3]
final_mask = paperdolls.after_pd_conclusions(mask, labels, person['face'])
for num in np.unique(final_mask):
category = list(labels.keys())[list(labels.values()).index(num)]
if category in constants.paperdoll_shopstyle_women.keys():
item_mask = 255 * np.array(final_mask == num, dtype=np.uint8)
faces = background_removal.image_is_relevant(image)
if faces[0] is True and len(faces[1]) != 0:
x0, y0, w, h = faces[1][0]
s_size = np.amin(np.asarray(w, h))
print "s_size:", s_size
#sample = gray_img[y0 + 3 * s_size:y0 + 4 * s_size, x0:x0 + s_size]
else:
s_size = np.asarray(0.1 * image.shape[0])
print "s_size:", s_size
trimmed_mask = fp_yuli_MR8.trim_mask(image, mask)
response = fp_yuli_MR8.yuli_fp(trimmed_mask, s_size)
print len(response)
ms_response = []
for idx, val in enumerate(response):
ms_response = ms_response + fp_yuli_MR8.mean_std_pooling(val, 5)
return ms_response
def from_image_url_to_categorization_task(image_url):
image_obj = images.find_one({"image_urls": image_url})
if not image_obj: # new image
image = background_removal.standard_resize(Utils.get_cv2_img_array(image_url), 400)[0]
if image is None:
logging.warning("There's no image in the url!")
return None
relevance = background_removal.image_is_relevant(image)
image_dict = {'image_urls': [], 'relevant': relevance.is_relevant, '_id': bson.ObjectId()}
image_dict['image_urls'].append(image_url)
if relevance.is_relevant:
image_dict['people'] = []
for face in relevance.faces:
x, y, w, h = face
person = {'face': face.tolist(), 'person_id': str(bson.ObjectId())}
image_copy = image.copy()
cv2.rectangle(image_copy, (x, y), (x + w, y + h), [0, 255, 0], 2)
person['url'] = upload_image(image_copy, str(person['person_id']))
image_dict['people'].append(person)
q2.enqueue(send_image_to_qc_categorization, person['url'], person['person_id'])
else:
logging.warning('image is not relevant, but stored anyway..')
images.insert(image_dict)
else:
if image_url not in image_obj['image_urls']:
image_obj['image_urls'].append(image_url)
if image_obj['relevant']:
logging.warning("Image is in the DB and relevant!")
else:
logging.warning("Image is in the DB and not relevant!")
return image_obj
def check_if_relevant(image_url, page_url, products_collection, method):
image = Utils.get_cv2_img_array(image_url)
if image is None:
return
small_img, _ = background_removal.standard_resize(image, 600)
relevance = background_removal.image_is_relevant(small_img, use_caffe=False, image_url=image_url)
if not relevance.is_relevant:
hashed = get_hash(image)
try:
label = labelize(image).replace('.', '')
except:
label = None
image_obj = {'image_hash': hashed, 'image_urls': [image_url], 'page_urls': [page_url], 'people': [],
'relevant': False, 'saved_date': str(datetime.datetime.utcnow()), 'views': 1,
'labels': label}
db.irrelevant_images.insert_one(image_obj)
db.labeled_irrelevant.insert_one(image_obj)
return image_obj
image_obj = {'people': [{'person_id': str(bson.ObjectId()), 'face': face.tolist(),
'gender': classifier_client.get('gender', image, face=face.tolist())['gender']} for face in
relevance.faces],
'image_urls': image_url, 'page_url': page_url, 'insert_time': datetime.datetime.now()}
db.iip.insert_one(image_obj)
start_pipeline.enqueue_call(func="", args=(page_url, image_url, products_collection, method),
ttl=2000, result_ttl=2000, timeout=2000)
def dl_keyword_images(
category_id,
total=2000,
keyword=None,
# dir='/home/jeremy/[email protected]/TrendiGuru/techdev/trendi_guru_modules/images',
# dir='/home/ubuntu/Dev/trendi_guru_modules/images',
dir='images',
show_visual_output=False):
query = {
"categories": {
"$elemMatch": {
"id": {
"$in": get_all_subcategories(db.categories, category_id)
}
}
}
}
if keyword is None:
path = os.path.join(dir, category_id)
cursor = db.products.find(query)
else:
path = os.path.join(dir, category_id)
path = os.path.join(path, keyword)
cursor = db.products.find(
{'$and': [{
"description": {
'$regex': keyword
}
}, query]})
print('path:' + path)
if not os.path.exists(path):
print('creating dir')
os.makedirs(path)
i = 0
for item in cursor:
if i > total:
break
i += 1
url = item['image']['sizes']['XLarge']['url']
print('url:' + url)
item_image = Utils.get_cv2_img_array(url)
if item_image is None:
return None
if show_visual_output == True:
cv2.imshow('im1', item_image)
k = cv2.waitKey(200)
if background_removal.image_is_relevant(
background_removal.standard_resize(item_image, 400)[0]):
name = os.path.join(path, str(item['id']) + '.jpg')
try:
print('writing ' + name)
cv2.imwrite(name, item_image)
except:
print('couldnt write file:' + name)
def find_images(description):
print('starting to find ' + str(description))
no_face_count = 0
face_count = 0
for i in range(0, 10500):
mdoc = dbUtils.lookfor_next_unbounded_feature_from_db_category(current_item=i, skip_if_marked_to_skip=True,
which_to_show='showAll',
filter_type='byWordInDescription',
category_id=None,
word_in_description=description,
db=db)
if mdoc is not None and 'doc' in mdoc:
i = i + 1
print('not none')
doc = mdoc['doc']
print doc
xlarge_url = doc['image']['sizes']['XLarge']['url']
print('large img url:' + str(xlarge_url))
img_arr = Utils.get_cv2_img_array(xlarge_url)
if img_arr is None:
return None
small_img = background_removal.standard_resize(img_arr, 400)[0]
show_visual_output = False
if show_visual_output == True:
cv2.imshow('im1', img_arr)
# k = cv2.waitKey(200)
# cv2.imshow('smallim1', small_img)
# k = cv2.waitKey(200)
relevance = background_removal.image_is_relevant(small_img)
print('relevance:' + str(relevance))
rel = relevance.is_relevant
if rel == False:
no_face_count += 1
print('relevant')
fname = os.path.join('netanel', description)
fname = os.path.join(fname, 'noface')
Utils.ensure_dir(fname)
fname = os.path.join(fname, str(no_face_count) + '.jpg')
else:
face_count += 1
print('not relevant')
fname = os.path.join('netanel', description)
Utils.ensure_dir(fname)
fname = os.path.join(description, str(face_count) + '.jpg')
print('writing ' + str(fname))
cv2.imwrite(fname, img_arr)
else:
break
def find_images(description):
print('starting to find ' + str(description))
for i in range(0, 500):
mdoc = dbUtils.lookfor_next_unbounded_feature_from_db_category(
current_item=i,
skip_if_marked_to_skip=True,
which_to_show='showAll',
filter_type='byWordInDescription',
category_id=None,
word_in_description=description,
db=None)
if mdoc is not None and 'doc' in mdoc:
doc = mdoc['doc']
# print doc
xlarge_url = doc['image']['sizes']['XLarge']['url']
# print('large img url:' + str(xlarge_url))
img_arr = Utils.get_cv2_img_array(xlarge_url)
if img_arr is None:
return None
small_img = background_removal.standard_resize(img_arr, 400)[0]
show_visual_output = False
if show_visual_output == True:
cv2.imshow('im1', img_arr)
k = cv2.waitKey(200)
cv2.imshow('smallim1', small_img)
k = cv2.waitKey(200)
relevance = background_removal.image_is_relevant(small_img)
if not relevance:
print('image is not relevant:' + str(description))
continue
print('image is relevant:' + str(description))
face1 = background_removal.find_face(img_arr)
if face1 is not None and len(face1) != 0:
print('face1:' + str(face1))
bb1 = face1[0]
get_pose_est_bbs(xlarge_url,
description,
n=i,
show_visual_output=show_visual_output,
bb=bb1)
else:
get_pose_est_bbs(xlarge_url,
description,
n=i,
show_visual_output=show_visual_output)