def POST(self):
data = web.data()
data = json.loads(data)
path = data['path']
im = Image.open(path).convert('L')
res = ''
if crnnOcr is not None:
res = crnnOcr(im)
return res
def rec_txt(crop_img):
H, W = crop_img.shape[:2]
H2 = 32
W2 = float(W * H2) / H
W2 = int(W2)
size = (W2, H2)
cv2.resize(crop_img, size, interpolation=cv2.INTER_LINEAR)
crop_img = Image.fromarray(crop_img)
text = crnnOcr(crop_img.convert('L'))
return text
def crnnRec(im, boxes, leftAdjust=False, rightAdjust=False, alph=0.2, f=1.0):
"""
crnn模型，ocr识别
leftAdjust,rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust, rightAdjust, alph)
text = crnnOcr(partImg.convert('L'))
if text.strip() != u'':
results.append({'cx': cx * f, 'cy': cy * f, 'text': text, 'w': newW * f, 'h': newH * f,
'degree': degree * 180.0 / np.pi})
return results
def crnnRec(im,
boxes,
leftAdjust=False,
rightAdjust=False,
alph=0.2,
f=1.0,
save=True):
"""
crnn模型，ocr识别
leftAdjust, rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
# im = Image.fromarray(im)
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
# 按照box大小，裁剪图片
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust,
rightAdjust, alph)
# partImg, newW, newH = cut_img = ()
image_cv = cv2.cvtColor(numpy.asarray(partImg), cv2.COLOR_RGB2BGR)
cv2.imshow("crnnRec", image_cv)
# partImg = Image.fromarray(cv2.cvtColor(partImg, cv2.COLOR_BGR2RGB))
# 图片会转灰度图，进行识别
# print('crnnRec', partImg.size)
text, w = crnnOcr(partImg.convert('L'))
# text, w = crnnOcr(partImg)
if save:
image = partImg.resize((w, 32), Image.BILINEAR)
save_image(image, text)
# text = crnnOcr(partImg)
if text.strip() != u'':
results.append({
'cx': cx * f,
'cy': cy * f,
'text': text,
'w': newW * f,
'h': newH * f,
'degree': degree * 180.0 / np.pi
})
return results
def crnnRec(im,
boxes,
leftAdjust=False,
rightAdjust=False,
alph=0.2,
f=1.0,
tp_groups=None,
boxAll=None,
scoreAll=None):
"""
crnn模型，ocr识别
leftAdjust,rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust,
rightAdjust, alph)
text = crnnOcr(partImg.convert('L'))
detailbox = boxAll[tp_groups[index]]
detailscore = scoreAll[tp_groups[index]]
detaildex = tp_groups[index]
if text.strip() != u'':
results.append({
'cx': cx * f,
'cy': cy * f,
'text': text,
'w': newW * f,
'h': newH * f,
'degree': degree * 180.0 / np.pi,
'detailbox': detailbox,
'detailscore': detailscore,
'detaildex': detaildex
})
#degree表示顺时针转多少度.
return results