AL-2020/codes_recognizer/rocognizer.py

53 lines
1.4 KiB
Python

import cv2
import torch
from nn_model import Net
from torchvision.transforms import transforms
def recognizer(path):
codes = []
code = []
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
# load nn model
model = Net()
model.load_state_dict(torch.load('model.pt'))
model.eval()
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]
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)))
codes.append(code)
# cv2.imshow("Code", img)
# cv2.waitKey()
return codes