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eval.py
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eval.py
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import cv2
import time
import math
import os
import argparse
import numpy as np
import tensorflow as tf
from keras.models import load_model, model_from_json
import locality_aware_nms as nms_locality
import lanms
parser = argparse.ArgumentParser()
parser.add_argument('--test_data_path', type=str, default='../data/ICDAR2015/test_data')
parser.add_argument('--gpu_list', type=str, default='0')
parser.add_argument('--model_path', type=str, default='')
parser.add_argument('--output_dir', type=str, default='tmp/eval/east_icdar2015_resnet_v1_50_rbox/')
FLAGS = parser.parse_args()
from model import *
from losses import *
from data_processor import restore_rectangle
def get_images():
'''
find image files in test data path
:return: list of files found
'''
files = []
exts = ['jpg', 'png', 'jpeg', 'JPG']
for parent, dirnames, filenames in os.walk(FLAGS.test_data_path):
for filename in filenames:
for ext in exts:
if filename.endswith(ext):
files.append(os.path.join(parent, filename))
break
print('Find {} images'.format(len(files)))
return files
def resize_image(im, max_side_len=2400):
'''
resize image to a size multiple of 32 which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
'''
h, w, _ = im.shape
resize_w = w
resize_h = h
# limit the max side
if max(resize_h, resize_w) > max_side_len:
ratio = float(max_side_len) / resize_h if resize_h > resize_w else float(max_side_len) / resize_w
else:
ratio = 1.
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
resize_h = resize_h if resize_h % 32 == 0 else (resize_h // 32) * 32
resize_w = resize_w if resize_w % 32 == 0 else (resize_w // 32) * 32
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def detect(score_map, geo_map, timer, score_map_thresh=0.8, box_thresh=0.1, nms_thres=0.2):
'''
restore text boxes from score map and geo map
:param score_map:
:param geo_map:
:param timer:
:param score_map_thresh: threshhold for score map
:param box_thresh: threshhold for boxes
:param nms_thres: threshold for nms
:return:
'''
if len(score_map.shape) == 4:
score_map = score_map[0, :, :, 0]
geo_map = geo_map[0, :, :, ]
# filter the score map
xy_text = np.argwhere(score_map > score_map_thresh)
# sort the text boxes via the y axis
xy_text = xy_text[np.argsort(xy_text[:, 0])]
# restore
start = time.time()
text_box_restored = restore_rectangle(xy_text[:, ::-1]*4, geo_map[xy_text[:, 0], xy_text[:, 1], :]) # N*4*2
print('{} text boxes before nms'.format(text_box_restored.shape[0]))
boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
boxes[:, :8] = text_box_restored.reshape((-1, 8))
boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
timer['restore'] = time.time() - start
# nms part
start = time.time()
# boxes = nms_locality.nms_locality(boxes.astype(np.float64), nms_thres)
boxes = lanms.merge_quadrangle_n9(boxes.astype('float32'), nms_thres)
timer['nms'] = time.time() - start
if boxes.shape[0] == 0:
return None, timer
# here we filter some low score boxes by the average score map, this is different from the orginal paper
for i, box in enumerate(boxes):
mask = np.zeros_like(score_map, dtype=np.uint8)
cv2.fillPoly(mask, box[:8].reshape((-1, 4, 2)).astype(np.int32) // 4, 1)
boxes[i, 8] = cv2.mean(score_map, mask)[0]
boxes = boxes[boxes[:, 8] > box_thresh]
return boxes, timer
def sort_poly(p):
min_axis = np.argmin(np.sum(p, axis=1))
p = p[[min_axis, (min_axis+1)%4, (min_axis+2)%4, (min_axis+3)%4]]
if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
return p
else:
return p[[0, 3, 2, 1]]
def main(argv=None):
import os
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_list
try:
os.makedirs(FLAGS.output_dir)
except OSError as e:
if e.errno != 17:
raise
# load trained model
json_file = open(os.path.join('/'.join(FLAGS.model_path.split('/')[0:-1]), 'model.json'), 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json, custom_objects={'tf': tf, 'RESIZE_FACTOR': RESIZE_FACTOR})
model.load_weights(FLAGS.model_path)
img_list = get_images()
for img_file in img_list:
img = cv2.imread(img_file)[:, :, ::-1]
start_time = time.time()
img_resized, (ratio_h, ratio_w) = resize_image(img)
img_resized = (img_resized / 127.5) - 1
timer = {'net': 0, 'restore': 0, 'nms': 0}
start = time.time()
# feed image into model
score_map, geo_map = model.predict(img_resized[np.newaxis, :, :, :])
timer['net'] = time.time() - start
boxes, timer = detect(score_map=score_map, geo_map=geo_map, timer=timer)
print('{} : net {:.0f}ms, restore {:.0f}ms, nms {:.0f}ms'.format(
img_file, timer['net']*1000, timer['restore']*1000, timer['nms']*1000))
if boxes is not None:
boxes = boxes[:, :8].reshape((-1, 4, 2))
boxes[:, :, 0] /= ratio_w
boxes[:, :, 1] /= ratio_h
duration = time.time() - start_time
print('[timing] {}'.format(duration))
# save to file
if boxes is not None:
res_file = os.path.join(
FLAGS.output_dir,
'{}.txt'.format(
os.path.basename(img_file).split('.')[0]))
with open(res_file, 'w') as f:
for box in boxes:
# to avoid submitting errors
box = sort_poly(box.astype(np.int32))
if np.linalg.norm(box[0] - box[1]) < 5 or np.linalg.norm(box[3]-box[0]) < 5:
continue
f.write('{},{},{},{},{},{},{},{}\r\n'.format(
box[0, 0], box[0, 1], box[1, 0], box[1, 1], box[2, 0], box[2, 1], box[3, 0], box[3, 1],
))
cv2.polylines(img[:, :, ::-1], [box.astype(np.int32).reshape((-1, 1, 2))], True, color=(255, 255, 0), thickness=1)
img_path = os.path.join(FLAGS.output_dir, os.path.basename(img_file))
cv2.imwrite(img_path, img[:, :, ::-1])
if __name__ == '__main__':
main()