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demo.py
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demo.py
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import argparse
import json
import os
#from pytesseract import image_to_string
import sys
from concurrent.futures import ThreadPoolExecutor
import cv2
import lanms
import numpy as np
from numpy.lib.type_check import imag
import torch
from PIL import Image, ImageDraw, ImageFont
from torch.autograd import Variable
from torchvision import transforms
from tqdm import tqdm
from dataset.crnn import ResizeNormalize
from dataset.east import get_rotate_mat
from model.east import EAST
from util.label import LabelDecoder
decoder = LabelDecoder()
parser = argparse.ArgumentParser('EAST Detect')
parser.add_argument('--folder',
type=str,
default='sample',
help='detect imgs folder')
parser.add_argument('--n_cpu', type=int, default=1)
parser.add_argument('--output',
type=str,
default='output',
help='output folder ,default is output')
parser.add_argument('--east',
type=str,
default='pths/east_50.pth',
help='pretrained east path')
parser.add_argument('--crnn',
type=str,
default='pths/crnn_20.pth',
help='pretrained crnn model')
args = parser.parse_args()
print(args)
class Demo(object):
def __init__(self) -> None:
self.crnn = torch.load(args.crnn).cuda()
self.crnn.eval()
self.east = EAST().cuda()
self.east.load_state_dict(torch.load(args.east))
self.east.eval()
self.transform = ResizeNormalize(32, 100)
def resize_img(self, img):
'''resize image to be divisible by 32
'''
w, h = img.size
resize_w = w
resize_h = h
resize_h = resize_h if resize_h % 32 == 0 else int(resize_h / 32) * 32
resize_w = resize_w if resize_w % 32 == 0 else int(resize_w / 32) * 32
img = img.resize((resize_w, resize_h), Image.BILINEAR)
ratio_h = resize_h / h
ratio_w = resize_w / w
return img, ratio_h, ratio_w
def load_pil(self, img):
'''convert PIL Image to torch.Tensor
'''
t = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
return t(img).unsqueeze(0)
def detect_text(self, img):
image = Variable(self.transform(img)).cuda().unsqueeze(0)
preds = self.crnn(image)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.LongTensor([26] * 1))
# raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
sim_pred = decoder.decode(preds.data, preds_size.data, raw=False)
return sim_pred
def is_valid_poly(self, res, score_shape, scale):
'''check if the poly in image scope
Input:
res : restored poly in original image
score_shape: score map shape
scale : feature map -> image
Output:
True if valid
'''
cnt = 0
for i in range(res.shape[1]):
if res[0,i] < 0 or res[0,i] >= score_shape[1] * scale or \
res[1,i] < 0 or res[1,i] >= score_shape[0] * scale:
cnt += 1
return True if cnt <= 1 else False
def restore_polys(self, valid_pos, valid_geo, score_shape, scale=4):
'''restore polys from feature maps in given positions
Input:
valid_pos : potential text positions <numpy.ndarray, (n,2)>
valid_geo : geometry in valid_pos <numpy.ndarray, (5,n)>
score_shape: shape of score map
scale : image / feature map
Output:
restored polys <numpy.ndarray, (n,8)>, index
'''
polys = []
index = []
valid_pos *= scale
d = valid_geo[:4, :] # 4 x N
angle = valid_geo[4, :] # N,
for i in range(valid_pos.shape[0]):
x = valid_pos[i, 0]
y = valid_pos[i, 1]
y_min = y - d[0, i]
y_max = y + d[1, i]
x_min = x - d[2, i]
x_max = x + d[3, i]
rotate_mat = get_rotate_mat(-angle[i])
temp_x = np.array([[x_min, x_max, x_max, x_min]]) - x
temp_y = np.array([[y_min, y_min, y_max, y_max]]) - y
coordidates = np.concatenate((temp_x, temp_y), axis=0)
res = np.dot(rotate_mat, coordidates)
res[0, :] += x
res[1, :] += y
if self.is_valid_poly(res, score_shape, scale):
index.append(i)
polys.append([
res[0, 0], res[1, 0], res[0, 1], res[1, 1], res[0, 2],
res[1, 2], res[0, 3], res[1, 3]
])
return np.array(polys), index
def get_boxes(self, score, geo, score_thresh=0.9, nms_thresh=0.2):
'''get boxes from feature map
Input:
score : score map from model <numpy.ndarray, (1,row,col)>
geo : geo map from model <numpy.ndarray, (5,row,col)>
score_thresh: threshold to segment score map
nms_thresh : threshold in nms
Output:
boxes : final polys <numpy.ndarray, (n,9)>
'''
score = score[0, :, :]
xy_text = np.argwhere(score > score_thresh) # n x 2, format is [r, c]
if xy_text.size == 0:
return None
xy_text = xy_text[np.argsort(xy_text[:, 0])]
valid_pos = xy_text[:, ::-1].copy() # n x 2, [x, y]
valid_geo = geo[:, xy_text[:, 0], xy_text[:, 1]] # 5 x n
polys_restored, index = self.restore_polys(valid_pos, valid_geo,
score.shape)
if polys_restored.size == 0:
return None
boxes = np.zeros((polys_restored.shape[0], 9), dtype=np.float32)
boxes[:, :8] = polys_restored
boxes[:, 8] = score[xy_text[index, 0], xy_text[index, 1]]
boxes = lanms.merge_quadrangle_n9(boxes.astype('float32'), nms_thresh)
return boxes
def adjust_ratio(self, boxes, ratio_w, ratio_h):
'''refine boxes
Input:
boxes : detected polys <numpy.ndarray, (n,9)>
ratio_w: ratio of width
ratio_h: ratio of height
Output:
refined boxes
'''
if boxes is None or boxes.size == 0:
return []
boxes[:, [0, 2, 4, 6]] /= ratio_w
boxes[:, [1, 3, 5, 7]] /= ratio_h
return np.around(boxes)
def detect(self, img, device):
'''detect text regions of img using model
Input:
img : PIL Image
model : detection model
device: gpu if gpu is available
Output:
detected polys
'''
img, ratio_h, ratio_w = self.resize_img(img)
with torch.no_grad():
score, geo = self.east(self.load_pil(img).to(device))
boxes = self.get_boxes(
score.squeeze(0).cpu().numpy(),
geo.squeeze(0).cpu().numpy())
return self.adjust_ratio(boxes, ratio_w, ratio_h)
def plot_boxes(self, img, boxes):
'''plot boxes on image
'''
if boxes is None:
return img
draw = ImageDraw.Draw(img)
for box in boxes:
draw.polygon([
box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7]
],
outline=(0, 255, 0))
return img
def detect_boxes(self, paths):
rtn = {}
for path in tqdm(paths, desc=f"Detector "):
arr = cv2.imread(path)
img = Image.open(path)
#img = new(img)
img = img.convert('RGB')
img_name = path.split('/')[-1]
boxes = self.detect(img, 'cuda:0')
#img.save(f'output/{img_name}')
img = self.infer(boxes, img)
img = self.plot_boxes(img, boxes)
img.save(f'{args.output}/{img_name}')
def infer(self, boxes, img):
draw = ImageDraw.Draw(img)
font = ImageFont.truetype('Hack-Bold.ttf', size=30)
for box in boxes:
x, y = int(box[0]), int(box[1])
rx, ry = int(box[4]), int(box[5])
crop = img.crop((x, y, rx, ry)).convert('L')
text = self.detect_text(crop)
draw.text((x, y - 30), text, (128, 0, 128), font)
return img
if __name__ == '__main__':
demo = Demo()
if args.folder:
names = [
os.path.join(args.folder, ele) for ele in os.listdir(args.folder)
]
demo.detect_boxes(names)
else:
print("FOlder or Image Should be Provided")