#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
image
@author: chineseocr
"""
import cv2
import numpy as np
from config import textPath, anchors
from helper.image import resize_img, get_origin_box, soft_max, reshape
from helper.detectors import TextDetector
textNet = cv2.dnn.readNetFromDarknet(textPath.replace('weights', 'cfg'),
textPath)
def detect_box(image, scale=600, maxScale=900):
H, W = image.shape[:2]
image, rate = resize_img(image, scale, maxScale=maxScale)
h, w = image.shape[:2]
inputBlob = cv2.dnn.blobFromImage(image,
scalefactor=1.0,
size=(w, h),
swapRB=False,
crop=False)
outputName = textNet.getUnconnectedOutLayersNames()
textNet.setInput(inputBlob)
out = textNet.forward(outputName)[0]
clsOut = reshape(out[:, :20, ...])
boxOut = reshape(out[:, 20:, ...])
boxes = get_origin_box((w, h), anchors, boxOut[0])
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
text detect
@author: chineseocr
"""
import cv2
import numpy as np
from config import textPath, anchors, GPU
from helper.image import resize_img, get_origin_box, soft_max, reshape
from helper.detectors import TextDetector
if GPU:
from dnn.darknet import load_net, predict_image, array_to_image
textNet = load_net(
textPath.replace('.weights', '.cfg').encode(), textPath.encode(), 0)
else:
textNet = cv2.dnn.readNetFromDarknet(textPath.replace('weights', 'cfg'),
textPath)
def detect_box(image, scale=600, maxScale=900):
H, W = image.shape[:2]
image, rate = resize_img(image, scale, maxScale=maxScale)
h, w = image.shape[:2]
if GPU:
im = array_to_image(image)
res = predict_image(textNet, im)
scale = 16
iw = int(np.ceil(im.w / scale))
ih = int(np.ceil(im.h / scale))
h, w = image.shape[:2]