import click
import tensorflow as tf
from flask import Flask, jsonify, request, render_template
from threading import Thread
from PIL import Image
from six.moves import _thread
from luminoth.tools.checkpoint import get_checkpoint_config
from luminoth.utils.config import get_config, override_config_params
from luminoth.utils.predicting import PredictorNetwork
from luminoth.vis import vis_objects
import cv2
from pytesseract import image_to_string
app = Flask(__name__)
import numpy as np
def get_image():
image = request.files.get('image')
if not image:
raise ValueError
basewidth = 300
#wpercent = (basewidth/float(Image.open(image.stream).size[0]))
#hsize = int((float(Image.open(image.stream).size[1])*float(wpercent)))
img = Image.open(image.stream).convert('RGB')
img = np.asarray(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
b = cv2.distanceTransform(img, distanceType=cv2.DIST_L2, maskSize=5)
g = cv2.distanceTransform(img, distanceType=cv2.DIST_L1, maskSize=5)
r = cv2.distanceTransform(img, distanceType=cv2.DIST_C, maskSize=5)
# merge the transformed channels back to an image
transformed_image = cv2.merge((b, g, r))
return transformed_image
@app.route('/')
def index():
return render_template('index.html')
import json
@app.route('/api/<model_name>/predict/', methods=['GET', 'POST'])
def predict(model_name):
print ("preditct")
if request.method == 'GET':
return jsonify(error='Use POST method to send image.'), 400
try:
image_array = get_image()
except ValueError:
return jsonify(error='Missing image'), 400
except OSError:
return jsonify(error='Incompatible file type'), 400
total_predictions = request.args.get('total')
if total_predictions is not None:
try:
total_predictions = int(total_predictions)
except ValueError:
total_predictions = None
# Wait for the model to finish loading.
NETWORK_START_THREAD.join()
objects = PREDICTOR_NETWORK.predict_image(image_array)
image = request.files.get('image')
if not image:
raise ValueError
img = Image.open(image.stream).convert('RGB')
#vis_objects(np.array(image_array), objects).save("c:\\temp\\data.png")
vis_objects(np.array(img), objects).save("c:\\temp\\data.png")
global ouputObjects
ouputObjects = objects
objects = objects[:total_predictions]
return jsonify({'objects': objects})
@app.route('/api/<model_name>/extract/', methods=['GET', 'POST'])
def extract(model_name):
print ("extract")
if request.method == 'GET':
return jsonify(error='Use POST method to send image.'), 400
#total_predictions = request.args.get('total')
# Wait for the model to finish loading.
image = request.files.get('image')
thres = request.values.get("th")
img = Image.open(image.stream).convert('RGB')
#vis_objects(np.array(image_array), objects).save("c:\\temp\\data.png")
s = ""
for obj in ouputObjects:
if (obj['prob'] > (int(thres)/100)):
coordinates = obj['bbox']
print (coordinates)
width, height = img.size
print (height, width)
width_percentage = (coordinates[2]-coordinates[0])/width*100
if width_percentage >= 70:
coordinates[0] = 0
coordinates[2] = width
coordinates[1] = coordinates[1] - 15
coordinates[3] = coordinates[3] + 10
print (width_percentage)
print (coordinates)
cropped = img.crop( ( coordinates[0], coordinates[1], coordinates[2], coordinates[3] ) )
file = "c:\\temp\\" +str(obj['prob']) +".jpg"
cropped.save(file)
#print (file)
print (image_to_string(Image.open(file)))
# Define config parameters.
# '-l eng' for using the English language
# '--oem 1' for using LSTM OCR Engine
config = ('-l eng --oem 1 --psm 6')
s += image_to_string(Image.open(file).convert('LA'),config=config)
#print (s)
return s
def start_network(config):
global PREDICTOR_NETWORK
try:
PREDICTOR_NETWORK = PredictorNetwork(config)
except Exception as e:
# An error occurred loading the model; interrupt the whole server.
tf.logging.error(e)
_thread.interrupt_main()
@click.command(help='Start basic web application.')
@click.option('config_files', '--config', '-c', multiple=True, help='Config to use.') # noqa
@click.option('--checkpoint', help='Checkpoint to use.')
@click.option('override_params', '--override', '-o', multiple=True, help='Override model config params.') # noqa
@click.option('--host', default='127.0.0.1', help='Hostname to listen on. Set this to "0.0.0.0" to have the server available externally.') # noqa
@click.option('--port', default=5000, help='Port to listen to.')
@click.option('--debug', is_flag=True, help='Set debug level logging.')
def web(config_files, checkpoint, override_params, host, port, debug):
if debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
else:
tf.logging.set_verbosity(tf.logging.INFO)
if checkpoint:
config = get_checkpoint_config(checkpoint)
elif config_files:
config = get_config(config_files)
else:
click.echo(
'Neither checkpoint not config specified, assuming `accurate`.'
)
config = get_checkpoint_config('accurate')
if override_params:
config = override_config_params(config, override_params)
# Bounding boxes will be filtered by frontend (using slider), so we set a
# low threshold.
if config.model.type == 'fasterrcnn':
config.model.rcnn.proposals.min_prob_threshold = 0.01
elif config.model.type == 'ssd':
config.model.proposals.min_prob_threshold = 0.01
else:
raise ValueError(
"Model type '{}' not supported".format(config.model.type)
)
# Initialize model
global NETWORK_START_THREAD
NETWORK_START_THREAD = Thread(target=start_network, args=(config,))
NETWORK_START_THREAD.start()
app.run(host=host, port=port, debug=debug)
