def run(file, lang):
try:
imgFile = np.array(PIL.Image.open(file).convert("RGB"))
if lang == 'eng':
reader = easyocr.Reader(['en'])
else:
reader = easyocr.Reader([lang, 'en'])
text = reader.readtext(imgFile)
res_list = list()
for i in text:
res_list.append(i[1])
res_str = ''
for i in res_list:
res_str = res_str + ' ' + i
imgFile = PIL.Image.fromarray(imgFile)
img_io = io.BytesIO()
imgFile.save(img_io, 'jpeg', quality=100)
img_io.seek(0)
img = base64.b64encode(img_io.getvalue())
return [res_str, img]
except Exception as e:
print("error : ", e)
return 500
class OCRReader:
sim_reader = easyocr.Reader(["ch_sim", "en"])
tra_reader = easyocr.Reader(["ch_tra", "en"])
reader_modes = {"traditional": tra_reader, "simplified": sim_reader}
def __init__(self):
pass
def read_image(self, file_path, reader="simplified"):
return "".join(OCRReader.reader_modes[reader].readtext(file_path,
detail=0))
def get_ocr_result(img, x1y1, x2y2):
reader = easyocr.Reader(['en'])
img_tmp = "image_%d.jpg" % random.randint(1, 10)
cv2.imwrite(img_tmp, (img.numpy() * 255).astype(int))
result = reader.readtext(img_tmp, detail=0)
return ' '.join(result)
def process_ocr():
docs = Document.objects.filter(ocr_processed=False)
_logger.info('=======Process OCR Task========')
buffer_image = '/code/rest/mediafiles/buffer.jpg'
for doc in docs:
path = doc.file.path
text = ''
reader = easyocr.Reader(lang_list=['pt'])
if doc.file:
if doc.mimetype == 'application/pdf':
pages = convert_from_path(path, 500)
for page in pages:
page.save(buffer_image, 'JPEG')
data = reader.readtext(buffer_image)
for element in data:
text += element[1] + ' '
elif doc.mimetype in ['image/jpeg', 'image/png']:
data = reader.readtext(path)
for element in data:
text += element[1] + ' '
if text:
doc.text = text
doc.ocr_processed = True
doc.save()
_logger.info('=======End Process OCR Task========')
def temp_and_ocr(thermo_photo):
"""Validate content of thermo photo"""
thermo = settings.MEDIA_ROOT + str(Path(thermo_photo.thermo_picture.url[6:]))
temp = []
res = calculate_temp_bbox(thermo)
for i in range(len(res)):
probe_img = cv2.imread(settings.MEDIA_ROOT + str(Path(thermo_photo.thermo_picture.url[6:])))
probe_img = probe_img[res[i]['y1']:res[i]['y2'],
res[i]['x1']:res[i]['x2']]
reader = easyocr.Reader(['th','en'])
bounds = reader.readtext(probe_img)
try:
temp.append(float(bounds[0][1]))
thermo_photo.is_active = True
thermo_photo.save()
except:
thermo_photo.is_active = False
thermo_photo.save()
send_fail_email(
thermo_photo.report.user.pk,
thermo_photo.report.pk,
thermo_photo.pk
)
return
thermo_photo.RTMax = max(temp)
thermo_photo.RTMin = min(temp)
thermo_photo.RTMean = ((max(temp) + min(temp)) / 2)
thermo_photo.is_valid = True
thermo_photo.save()
def test():
leitor = easyocr.Reader(['pt'])
resultados = leitor.readtext('images/dbg.png', paragraph=False)
for palavra in resultados:
print(f'Palavra: {palavra[0]}\n'
f'Posição em relação a imagem: {palavra[1]}\n')
def test_easyocr_artifact_packs():
svc = EasyOCRService()
lang_list = ['ch_sim', 'en']
recog_network = "zh_sim_g2"
model = easyocr.Reader(
lang_list=lang_list,
gpu=False,
download_enabled=True,
recog_network=recog_network,
)
svc.pack('chinese_small',
model,
lang_list=lang_list,
recog_network=recog_network)
assert [x[1] for x in model.readtext(IMAGE_PATH)
] == (TEST_RESULT), 'Run inference before saving the artifact'
saved_path = svc.save()
loaded_svc = bentoml.load(saved_path)
assert loaded_svc.predict(imageio.imread(IMAGE_PATH))['text'] == (
TEST_RESULT), 'Run inference after saving the artifact'
# clean up saved bundle
yc = YataiClient()
yc.repository.delete(f'{svc.name}:{svc.version}')
def detectImage():
try:
time.sleep(0.5)
ImageFile.LOAD_TRUNCATED_IMAGES = True
PIC_PATH = 'image_for_detection/image.jpg'
response = requests.get(
"http://www.041er-blj.ch/2020/dakapka/notewriter/image.jpg",
stream=True)
file = open(PIC_PATH, "wb")
response.raw.decode_content = True
shutil.copyfileobj(response.raw, file)
del response
reader = easyocr.Reader(['en', 'de'], gpu=False)
result = reader.readtext(PIC_PATH, detail=0)
print(result)
result_string = ''
for word in result:
result_string += word + ' '
print(result_string)
os.remove('image_for_detection/image.jpg')
return result_string
except:
return 'Oops! Seems like there was an Error.\nPlease Try again.'
def main():
vc = WebcamVideoStream(src=0).start()
reader = easyocr.Reader(['en'])
while True:
# read the current camera frame
frame = vc.read()
# show the current frame (untouched)
cv2.imshow("My webcam", frame)
# if 'x' key is pressed, exit the loop
if cv2.waitKey(1) & 0XFF == ord('x'):
break
# if 'c' key is pressed, process the frame for OCR
if cv2.waitKey(1) & 0xFF == ord('c'):
# copy the image so it will be a cleaned image for tesseract
img = vc.mask_frame()
# DEBUG
img_box = draw_boxes(img, reader)
# draw the chart containing the image with boxes
cv2.imshow("Tesseract", img_box)
print_img_str(img, reader)
# close the window and de-allocate any associated memory usage
cv2.destroyAllWindows()
# close the already opened camera
vc.stop()
def orc(request):
import easyocr
url = request.GET.get('url', '')
if url:
reader = easyocr.Reader(['ch_sim', 'en']) # need to run only once to load model into memory
result = reader.readtext(url, detail=0)
return HttpResponse(result)
def predict_meme_class(clf, meme_path):
reader = easyocr.Reader(
['en']) # need to run only once to load model into memory
sentence = reader.readtext(meme_path)
result = []
for text in sentence:
result.append(text[1])
sentence = ' '.join(result)
x_text = extract_features(word_embedder, tokenizer, sentence, meme_path,
vgg_model)
x_text = np.reshape(x_text, (1, 4396))
predict_label = clf.predict(x_text)
prediction = np.argmax(predict_label)
if prediction == 0:
print("Non-hateful")
elif prediction == 1:
print("Hateful")
return
def __init__(self):
self.device = True if torch.cuda.is_available() else False
self.list_langs = ['en', 'id']
self.recog_network = 'english_g2'
self.reader = easyocr.Reader(self.list_langs,
gpu=self.device,
recog_network=self.recog_network)
def server(port):
class SimpleServerFactory(ServerFactory):
reader = None
def __init__(self, reader):
self.reader = reader
class SimpleReceiver(LineReceiver, ABC):
def connectionMade(self):
logger.info('Got connection from %s', self.transport.client)
def connectionLost(self, reason):
logger.info('%s disconnected', self.transport.client)
def dataReceived(self, data):
if type(data) == bytes:
logger.info("Received data ...")
else:
logger.info("Received data:%s", data)
result = reader.readtext(data, detail=0)
str = ''
for item in result:
str = str + item
self.sendLine(str.encode('utf-8'))
logger.info("Send response: %s", str)
# need to run only once to load model into memory
reader = easyocr.Reader(['ch_sim', 'en'])
factory = SimpleServerFactory(reader)
factory.protocol = SimpleReceiver
reactor.listenTCP(port, factory)
logger.info("TCP server started on port(s): %s ..." % (port))
reactor.run()
def load(self, event):
time_start = time.time()
address = self.filename.GetValue()
reader = easyocr.Reader(
['ch_sim',
'en']) # need to run only once to load model into memory
result = reader.readtext(address)
time_end = time.time()
#print(result)
res = "Time Cost: " + str(time_end - time_start) + ' s\n'
List = []
for line in result:
res = res + line[1] + '\n'
List.append(line[1])
print(List)
self.contents.SetValue(res) #填充文本框
rels = kg.extract(text=List)
g = kg.rel2graph(rels) ## 依据文本解析结果, 生成networkx有向图
fig = kg.graph2fig(g, x=1500,
y=1500) ## 生成 matplotlib.figure.Figure 图片
fig.savefig("KG_test_output.png")
img = cv2.imread("KG_test_output.png")
img = img[200:1500, :, :]
cv2.imwrite("KG_test_output.png", img)
img = wx.Image("KG_test_output.png",
wx.BITMAP_TYPE_PNG).Rescale(750, 650)
img = img.ConvertToBitmap()
self.bitmap.SetBitmap(img)
def start_ocr():
print('Start OCR License Plate : ')
reader = easyocr.Reader(['ko'])
result = None
temp = None
for a in range(0,5) :
str_a = str(a)
os.remove('media/video/result/frame/detected'+str_a+'.jpg')
for i in range(index2) :
str_i = str(i+5)
if result is not None:
temp = result
result = reader.readtext('media/video/result/frame/detected'+str_i+'.jpg', detail = 0)
str_result = "\n".join(result)
if temp != result :
with open("media/video/result/번호판.txt", "a") as f:
f.write(str_i)
f.write(':')
f.write(str_result)
f.write('\n')
print(result)
def processDir(img_folder, img_lang, file_title):
lang = [img_lang]
# if img_lang != 'en':
# lang.append(img_lang)
file_names = [
int(f.split(".")[0]) for f in os.listdir(img_folder)
if os.path.isfile(os.path.join(img_folder, f))
]
file_names.sort()
content = ""
reader = easyocr.Reader(lang)
for f in file_names:
file = img_folder + "/" + str(f) + ".jpeg"
im = PILIMG.open(file)
result = reader.readtext(file)
print(file, img_lang)
st = ""
for i in result:
st += i[1] + " "
content += st + " "
print(content)
res_img = draw_boxes(im, result)
res_file_path = img_folder + "/" + str(f) + "_res.jpeg"
res_img.save(res_file_path)
print(content)
audio_file = convertAudio(content, img_lang, file_title)
return content, audio_file
def getNumberPlateSting(image):
img = cv2.imread(image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
bfilter = cv2.bilateralFilter(gray, 11, 17, 17) # noise removal
edged = cv2.Canny(bfilter, 30, 200) # edge detection
keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(keypoints)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
location = None
for contour in contours:
approx = cv2.approxPolyDP(contour, 10, True)
if (len(approx) == 4):
location = approx
break
mask = np.zeros(gray.shape, np.uint8)
new_image = cv2.drawContours(mask, [location], 0, 255, -1)
new_image = cv2.bitwise_and(img, img, mask=mask)
(x, y) = np.where(mask == 255)
(x1, y1) = (np.min(x), np.min(y))
(x2, y2) = (np.max(x), np.max(y))
cropped_image = gray[x1:x2 + 1, y1:y2 + 1]
reader = easyocr.Reader(['en'])
result = reader.readtext(cropped_image)
text = result[0][-2]
return text
def Easy_OCR(img):
# Text Extraction
reader = easyocr.Reader(['id', 'en'], gpu=False)
result = reader.readtext(img, detail=1, paragraph=False)
txt_list = reader.readtext(img, detail=0, paragraph=False)
# Doing OCR. Get bounding boxes.
# bounds = reader.readtext(image_path, detail=1) #detail=1 argument will only give text with its position in an image
# bounds
# Draw bounding boxes
# loop over the results
for (bbox, text, prob) in result:
# display the OCR'd text and associated probability
print("[INFO] {:.4f}: {}".format(prob, text))
# unpack the bounding box
(tl, tr, br, bl) = bbox
tl = (int(tl[0]), int(tl[1]))
tr = (int(tr[0]), int(tr[1]))
br = (int(br[0]), int(br[1]))
bl = (int(bl[0]), int(bl[1]))
# cleanup the text and draw the box surrounding the text along
# with the OCR'd text itself
text = cleanup_text(text)
cv2.rectangle(img, tl, br, (0, 255, 0), 2)
cv2.putText(img, text, (tl[0], tl[1] - 10), \
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
# show the output image
show_wait_destroy("Image", img)
return txt_list
def maybe_find_license_plate_text(raw_img, grey_img, contours):
# find rectangles
locations = []
for contour in contours:
approx = cv2.approxPolyDP(contour, 10,
True) # approx polygon in contour
if len(approx) == 4:
locations.append(approx) # found a rectangle location
# print(locations)
for location in locations:
mask = np.zeros(grey_img.shape, np.uint8)
new_img = cv2.drawContours(mask, [location], 0, 255, -1)
new_img = cv2.bitwise_and(raw_img, raw_img, mask=mask)
cropped_img = crop_image(grey_img, mask)
# instantiate easyocr reader with english language
reader = easyocr.Reader(['en'])
# pass cropped image to reader
result = reader.readtext(cropped_img)
try:
text = result[-1][-2]
if len(text) >= MIN_PLATE_NUM_LEN:
# valid license plate text found
return text, location
else:
raise IndexError()
except IndexError:
pass
raise Exception("No license plate found :(")
def main():
# classifier to detect math vs non-math text
clf = Classifier()
reader = easyocr.Reader(['en'])
root = Path(f"./image-dataset/{sys.argv[1]}/")
for image in root.glob("*.jpg"):
print("Processing", image)
boxes_data = reader.readtext(str(image),
batch_size=16,
beamWidth=1,
link_threshold=0.1,
low_text=0.4,
height_ths=0.9,
ycenter_ths=0.9)
boxes_data = remove_non_math(clf, boxes_data)
boxes = non_max_suppression_fast(np.array([
(l[0][0], l[0][1], l[2][0], l[2][1]) for l, _, _ in boxes_data
]),
overlapThresh=0.1)
boxes = [(x1, y1, (x2 - x1), (y2 - y1)) for x1, y1, x2, y2 in boxes]
plot(image, boxes)
# printing
table = []
for _, text, confidence in boxes_data:
table.append([text, 1])
f = open(image.parent / "bb" / (image.parts[-1].replace("jpg", "txt")),
"w")
print(tabulate(table, headers=["Text", "Label"]), file=f)
def text_EASYOCR(IMAGE_PATH):
reader = easyocr.Reader(['en', 'ru'], gpu=True)
result = reader.readtext(IMAGE_PATH)
text = ''
for detection in result:
text = text + '\n' + detection[1]
return text
def car_recogniser(our_img):
car_image = our_img
trans = transforms.Compose([
transforms.Resize((400, 400)),
transforms.RandomRotation(15),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.RandomErasing(inplace=True),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# preprocessing for prediction image
input = trans(car_image)
input = input.view(1, 3, 400, 400)
output = car_model(input)
prediction = int(torch.max(output.data, 1)[1].numpy())
# return prediction label
predicted_val = ([value for value in labels_dict.values()][prediction])
st.text("Detected vehicle model: ")
predicted_val
#converting PIL object into numpy array for ocr
new_array = np.array(car_image)
reader = easyocr.Reader(['en'], gpu=False)
bounds = reader.readtext(new_array, detail=0)
st.text("Detected license plate number: ")
for x in bounds:
x
def __init__(self):
self.mongo_uri = get_project_settings().get('MONGO_URI')
self.mongo_db = get_project_settings().get('MONGO_DATABASE')
self.collection = 'one_petro'
self.client = pymongo.MongoClient(self.mongo_uri)
self.db = self.client[self.mongo_db]
self.reader = easyocr.Reader(['en'], gpu=False)
def main():
# Ornek olarak bir gorseli alip grayscale'a donusturuyoruz.
img = cv2.imread('assets/image1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# plt.imshow(cv2.cvtColor(gray, cv2.COLOR_BGR2RGB))
b_filter = cv2.bilateralFilter(gray, 11, 17, 17) # Noise dusuruyoruz.
edged = cv2.Canny(b_filter, 30, 200) # Kenar tespiti.
# gorsellerde countour(sekil) tespiti.
keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(keypoints)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
location = None
for contour in contours:
# kare sekillerinin tespiti icin ideal parametre verilir.
approx = cv2.approxPolyDP(contour, 10, True)
if len(approx) == 4:
location = approx
break
mask = np.zeros(gray.shape, np.uint8)
new_image = cv2.drawContours(mask, [location], 0, 255, -1)
new_image = cv2.bitwise_and(img, img, mask=mask)
(x, y) = np.where(mask == 255)
(x1, y1) = (np.min(x), np.min(y))
(x2, y2) = (np.max(x), np.max(y))
cropped_image = gray[x1:x2 + 1, y1:y2 + 1]
# OCR teknolojisi kullanarak kirpilan gorseldeki plaka metnini kaziyoruz.
reader = easyocr.Reader(['en'])
result = reader.readtext(cropped_image)
print(result)
# OCR SONU
# maskeleme ve kirpma islemi bitince islenen goruntuyu goster.
# kazinan plaka numarasi.
text = result[0][-2]
font = cv2.FONT_HERSHEY_SIMPLEX
res = cv2.putText(img,
text=text,
org=(approx[0][0][0], approx[1][0][1] + 75),
fontFace=font,
fontScale=1,
color=(0, 255, 0),
thickness=2)
res = cv2.rectangle(img, tuple(approx[0][0]), tuple(approx[2][0]),
(0, 255, 0), 3)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()
def car_recogniser_exit(our_img):
# Establishing the connection
conn = psycopg2.connect(database="vehicle",
user='******',
password='******',
host='127.0.0.1',
port='5432')
# Setting auto commit false
conn.autocommit = True
# Creating a cursor object using the cursor() method
cursor = conn.cursor()
car_image = our_img
trans = transforms.Compose([
transforms.Resize((400, 400)),
transforms.RandomRotation(15),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.RandomErasing(inplace=True),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# preprocessing for prediction image
input = trans(car_image)
input = input.view(1, 3, 400, 400)
output = car_model(input)
prediction = int(torch.max(output.data, 1)[1].numpy())
# return prediction label
predicted_val = ([value for value in labels_dict.values()][prediction])
# comparing algo
st.text("Detected vehicle model: ")
predicted_val
# converting PIL object into numpy array for ocr
new_array = np.array(car_image)
reader = easyocr.Reader(['en'], gpu=False)
bounds = reader.readtext(new_array, detail=0)
st.text("Detected license plate number: ")
num_plate = ' '.join([str(elem) for elem in bounds])
num_plate
ext_time = current_time()
st.text("The vehicle enter the parking at:")
ext_time
time_exit = ext_time.strftime("%Y/%m/%d, %H:%M:%S")
day_exit = ext_time.strftime("%Y %m %d")
sql = """INSERT INTO vehicle_data_exit(vehicle_brand, plate_number, exit_time, exit_date) VALUES(%s,%s,%s,%s)"""
record_to_enter = (predicted_val, num_plate, time_exit, day_exit)
cursor.execute(sql, record_to_enter)
conn.commit()
cursor.close()
conn.close()
def __init__(*args, **kwargs):
self = args[0]
self.ocr = easyocr.Reader(['en']) # need to run only once to load model into memory)
self.debug = False
for arg in kwargs:
if arg == "debug":
self.debug = kwargs[arg]
def __init__(self, imageName, debug=False):
self.reader = easyocr.Reader(['en'], gpu=False)
self.color = 'yellow'
self.width = 2
self.img = PIL.Image
self.input_image = imageName
self.bounds = None
self.debug = debug
def ocr(img_path):
"""
:param img_path: The path to the YouTube mobile screenshot
:return: OCR data including location coordinates and text
"""
reader = easyocr.Reader(['en'])
return reader.readtext(img_path)
def __init__(self, langs, gpu=False, model_dir=None):
# WARNING: This is a temporary usage.
# Ideal thing to do would be just using the recognizer model alone.
self.reader = easyocr.Reader(langs,
gpu=gpu,
model_storage_directory=model_dir)
self.detector = None
self.device = 'cuda' if gpu else 'cpu'
def __init__(self, uuid=None):
if uuid == None:
self.d = u2.connect()
else:
self.d = u2.connect(uuid)
d_width, _ = self.d.window_size()
self.d_width_center = d_width / 2
self.reader = easyocr.Reader(['ch_sim', 'en'], gpu=False)