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jblitzar
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github-python-corpus

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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import argparse
import os
import cv2
import mxnet as mx
import numpy as np
from rcnn.logger import logger
from rcnn.config import config
from rcnn.symbol import get_vgg_text_rpn_test, gensym
from rcnn.io.image import resize, transform
from rcnn.core.tester import Predictor, im_detect, im_rpn_detect, im_proposal, vis_all_detection, draw_all_detection
from rcnn.utils.load_model import load_param
from rcnn.text_connector.detectors import TextDetector
import matplotlib.pyplot as plt
import random
import logging
SHORT_SIDE = config.SCALES[0][0]
LONG_SIDE = config.SCALES[0][1]
PIXEL_MEANS = config.PIXEL_MEANS
DATA_NAMES = ['data', 'im_info']
LABEL_NAMES = None
DATA_SHAPES = [('data', (1, 3, SHORT_SIDE, LONG_SIDE)), ('im_info', (1, 3))]
LABEL_SHAPES = None
# visualization
CONF_THRESH = 0.7
NMS_THRESH = 0.3
def get_net(prefix, epoch, ctx):
arg_params, aux_params = load_param(prefix, epoch, convert=True, ctx=ctx, process=True)
predictor = Predictor(gensym.gen_sym_infer, DATA_NAMES, LABEL_NAMES, context=ctx, max_data_shapes= dict(DATA_SHAPES),
provide_data=DATA_SHAPES, provide_label=LABEL_SHAPES,
arg_params=arg_params, aux_params=aux_params)
return predictor
def generate_batch(im):
"""
preprocess image, return batch
:param im: cv2.imread returns [height, width, channel] in BGR
:return:
data_batch: MXNet input batch
data_names: names in data_batch
im_scale: float number
"""
im_array, im_scale = resize(im, SHORT_SIDE, LONG_SIDE)
im_array = transform(im_array, PIXEL_MEANS)
im_info = np.array([[im_array.shape[2], im_array.shape[3], im_scale]], dtype=np.float32)
data = [mx.nd.array(im_array), mx.nd.array(im_info)]
data_shapes = [('data', im_array.shape), ('im_info', im_info.shape)]
data_batch = mx.io.DataBatch(data=data, label=None, provide_data=data_shapes, provide_label=None)
return data_batch, DATA_NAMES, im_scale
def demo_net(predictor, detector, image_name):
"""
generate data_batch -> im_detect -> post process
:param predictor: Predictor
:param image_name: image name
:param vis: will save as a new image if not visualized
:return: None
"""
assert os.path.exists(image_name), image_name + ' not found'
im = cv2.imread(image_name)
data_batch, data_names, im_scale = generate_batch(im)
scores, boxes, data_dict = im_rpn_detect(predictor, data_batch, data_names, im_scale)
textrois = detector.detect(boxes, scores, (im.shape[0], im.shape[1]))
#plt.imshow(im[:,:,::-1])
for bbox in textrois:
x0, y0, x1, y1 = bbox[0], bbox[1], bbox[2], bbox[5]
# color = (random.random(), random.random(), random.random())
color = (0,1,0)
rect = plt.Rectangle((x0, y0),
x1 - x0,
y1 - y0, fill=False,
edgecolor=color, linewidth=1.0)
#plt.gca().add_patch(rect)
cv2.rectangle(im, (int(x0), int(y0)), (int(x1), int(y1)),
(0,0,255), 1)
#im = cv2.resize(im, (0,0), fx=0.5, fy=0.5)