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test_ctw1500.py
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import os
import cv2
import sys
import time
import collections
import argparse
import numpy as np
import tensorflow as tf
from dataset import CTW1500TestLoader, ctw_test_loader
import models
import util
# c++ version pse based on opencv 3+
#from pse import pse
# python pse
from pypse import pse as pypse
def extend_3c(img):
img = img.reshape(img.shape[0], img.shape[1], 1)
img = np.concatenate((img, img, img), axis=2)
return img
def debug(idx, img_paths, imgs, output_root):
if not os.path.exists(output_root):
os.makedirs(output_root)
col = []
for i in range(len(imgs)):
row = []
for j in range(len(imgs[i])):
# img = cv2.copyMakeBorder(imgs[i][j], 3, 3, 3, 3, cv2.BORDER_CONSTANT, value=[255, 0, 0])
row.append(imgs[i][j])
res = np.concatenate(row, axis=1)
col.append(res)
res = np.concatenate(col, axis=0)
img_name = img_paths[idx].split('/')[-1]
print(idx, '/', len(img_paths), img_name)
cv2.imwrite(output_root + img_name, res)
def write_result_as_txt(image_name, bboxes, path):
if not os.path.exists(path):
os.makedirs(path)
filename = util.io.join_path(path, '%s.txt'%(image_name))
lines = []
for b_idx, bbox in enumerate(bboxes):
values = [int(v) for v in bbox]
# line = "%d, %d, %d, %d, %d, %d, %d, %d\n"%tuple(values)
line = "%d"%values[0]
for v_id in range(1, len(values)):
line += ", %d"%values[v_id]
line += '\n'
lines.append(line)
util.io.write_lines(filename, lines)
def polygon_from_points(points):
"""
Returns a Polygon object to use with the Polygon2 class from a list of 8 points: x1,y1,x2,y2,x3,y3,x4,y4
"""
resBoxes=np.empty([1, 8],dtype='int32')
resBoxes[0, 0] = int(points[0])
resBoxes[0, 4] = int(points[1])
resBoxes[0, 1] = int(points[2])
resBoxes[0, 5] = int(points[3])
resBoxes[0, 2] = int(points[4])
resBoxes[0, 6] = int(points[5])
resBoxes[0, 3] = int(points[6])
resBoxes[0, 7] = int(points[7])
pointMat = resBoxes[0].reshape([2, 4]).T
return plg.Polygon(pointMat)
def test(args):
data_loader = CTW1500TestLoader(long_size=args.long_size)
test_loader = ctw_test_loader(data_loader, 1)
# Setup Model
if args.arch == "resnet50":
model = models.resnet50(pretrained=True, num_classes=7, scale=args.scale)
elif args.arch == "resnet101":
model = models.resnet101(pretrained=True, num_classes=7, scale=args.scale)
elif args.arch == "resnet152":
model = models.resnet152(pretrained=True, num_classes=7, scale=args.scale)
elif args.arch == "resnet18":
model = models.resnet18(pretrained=True, num_classes=7, scale=args.scale)
if args.resume is not None:
print("Loading model and optimizer from checkpoint '{}'".format(args.resume))
model.load_weights(args.resume)
print("Loaded checkpoint '{}' ".format(args.resume,))
sys.stdout.flush()
else:
print("No checkpoint found at '{}'".format(args.resume))
sys.stdout.flush()
total_frame = 0.0
total_time = 0.0
for idx, (org_img, img, data_length) in enumerate(test_loader):
print('progress: %d / %d'%(idx, data_length))
sys.stdout.flush()
org_img = org_img.numpy().astype('uint8')[0]
text_box = org_img.copy()
start = time.time()
outputs = model(img)
outputs = tf.transpose(outputs,(0,3,1,2))
score = tf.sigmoid(outputs[:, 0, :, :])
outputs = (tf.sign(outputs - args.binary_th) + 1) / 2
text = outputs[:, 0, :, :]
kernels = outputs[:, 0:args.kernel_num, :, :] * text
score = score.numpy()[0].astype(np.float32)
text = text.numpy()[0].astype(np.uint8)
kernels = kernels.numpy()[0].astype(np.uint8)
# c++ version pse #编译问题 暂时不用
#pred = pse(kernels, args.min_kernel_area / (args.scale * args.scale))
# python version pse
pred = pypse(kernels, args.min_kernel_area / (args.scale * args.scale))
# scale = (org_img.shape[0] * 1.0 / pred.shape[0], org_img.shape[1] * 1.0 / pred.shape[1])
scale = (org_img.shape[1] * 1.0 / pred.shape[1], org_img.shape[0] * 1.0 / pred.shape[0])
label = pred
label_num = np.max(label) + 1
bboxes = []
for i in range(1, label_num):
points = np.array(np.where(label == i)).transpose((1, 0))[:, ::-1]
if points.shape[0] < args.min_area / (args.scale * args.scale):
continue
score_i = np.mean(score[label == i])
if score_i < args.min_score:
continue
# rect = cv2.minAreaRect(points)
binary = np.zeros(label.shape, dtype='uint8')
binary[label == i] = 1
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contour = contours[0]
# epsilon = 0.01 * cv2.arcLength(contour, True)
# bbox = cv2.approxPolyDP(contour, epsilon, True)
bbox = contour
if bbox.shape[0] <= 2:
continue
bbox = bbox * scale
bbox = bbox.astype('int32')
bboxes.append(bbox.reshape(-1))
end = time.time()
total_frame += 1
total_time += (end - start)
print('fps: %.2f'%(total_frame / total_time))
sys.stdout.flush()
for bbox in bboxes:
cv2.drawContours(text_box, [bbox.reshape(bbox.shape[0] // 2, 2)], -1, (0, 255, 0), 2)
image_name = data_loader.img_paths[idx].split('/')[-1].split('.')[0]
write_result_as_txt(image_name, bboxes, 'outputs/submit_ctw1500/')
text_box = cv2.resize(text_box, (text.shape[1], text.shape[0]))
debug(idx, data_loader.img_paths, [[text_box]], 'outputs/vis_ctw1500/')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Hyperparams')
parser.add_argument('--arch', nargs='?', type=str, default='resnet18')
parser.add_argument('--resume', nargs='?', type=str, default='checkpoints/',
help='Path to previous saved model to restart from')
parser.add_argument('--binary_th', nargs='?', type=float, default=1.0,
help='Path to previous saved model to restart from')
parser.add_argument('--kernel_num', nargs='?', type=int, default=3,
help='Path to previous saved model to restart from')
parser.add_argument('--scale', nargs='?', type=int, default=1,
help='Path to previous saved model to restart from')
parser.add_argument('--long_size', nargs='?', type=int, default=1280,
help='')
parser.add_argument('--min_kernel_area', nargs='?', type=float, default=10.0,
help='min kernel area')
parser.add_argument('--min_area', nargs='?', type=float, default=300.0,
help='min area')
parser.add_argument('--min_score',nargs='?', type=float, default=0.93,
help='min score')
args = parser.parse_args()
test(args)