64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
import cv2
|
|
import matplotlib.pyplot as plt
|
|
import torch
|
|
from PIL.Image import Image
|
|
from torch import nn
|
|
from torchvision.transforms import transforms
|
|
from torch.autograd import Variable
|
|
import numpy as np
|
|
|
|
from nn_model import Net
|
|
|
|
|
|
def recognizer(a_path):
|
|
code = []
|
|
path = a_path
|
|
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
|
transforms.Normalize((0.5,), (0.5,)),
|
|
])
|
|
|
|
img = cv2.imread(path)
|
|
|
|
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)
|
|
|
|
ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV)
|
|
|
|
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
|
|
|
|
# load nn model
|
|
input_size = 784 # = 28*28
|
|
hidden_sizes = [128, 128, 64]
|
|
output_size = 10
|
|
model = Net()
|
|
model.load_state_dict(torch.load('model.pt'))
|
|
model.eval()
|
|
|
|
for rect in rects:
|
|
# Crop image
|
|
crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
|
|
# Resize the image
|
|
roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC)
|
|
# roi = cv2.dilate(roi, (3, 3))
|
|
# plt.imshow(roi)
|
|
# plt.show()
|
|
im = transform(roi)
|
|
im = im.view(1, 1, 28, 28)
|
|
with torch.no_grad():
|
|
logps = model(im)
|
|
ps = torch.exp(logps)
|
|
probab = list(ps.numpy()[0])
|
|
code.append(probab.index(max(probab)))
|
|
|
|
print(code)
|
|
# cv2.imshow("Code", img)
|
|
# cv2.waitKey()
|
|
return code
|
|
|
|
|
|
recognizer("55555.jpg")
|
|
# print(recognizer("55555.jpg"))
|