diff --git a/detector/ctpn/infer.py b/detector/ctpn/infer.py index 0f70dbb..eaaf34a 100644 --- a/detector/ctpn/infer.py +++ b/detector/ctpn/infer.py @@ -342,8 +342,12 @@ def random_test(net): if __name__ == '__main__': running_mode = sys.argv[2] # cpu or gpu + print("Mode: %s" % running_mode) net = Net.CTPN() - net.load_state_dict(torch.load(MODEL, map_location=running_mode)) + if running_mode == 'cpu': + net.load_state_dict(torch.load(MODEL, map_location=running_mode)) + else: + net.load_state_dict(torch.load(MODEL)) print(net) net.eval() diff --git a/recognizer/crnn/Config.py b/recognizer/crnn/Config.py index f4d1091..ce03e2b 100644 --- a/recognizer/crnn/Config.py +++ b/recognizer/crnn/Config.py @@ -8,7 +8,7 @@ gpu_id = '4' model_dir = './model' data_worker = 4 -batch_size = 128 +batch_size = 64 img_height = 32 img_width = 100 alphabet = alphabets.alphabet diff --git a/recognizer/crnn/alphabets.py b/recognizer/crnn/alphabets.py new file mode 100644 index 0000000..7ddc43b --- /dev/null +++ b/recognizer/crnn/alphabets.py @@ -0,0 +1,2 @@ +#coding=utf-8 +alphabet = 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diff --git a/recognizer/crnn/infer.py b/recognizer/crnn/infer.py index df47699..f422ac9 100644 --- a/recognizer/crnn/infer.py +++ b/recognizer/crnn/infer.py @@ -8,12 +8,27 @@ from PIL import Image import Net.net as Net import alphabets +import numpy as np +import cv2 -crnn_model_path = 'trained_models/mixed_second_finetune_acc97p7.pth' -IMG_ROOT = './test' +crnn_model_path = './model/mixed_second_finetune_acc97p7.pth' +IMG_ROOT = './test_images' +running_mode = 'gpu' alphabet = alphabets.alphabet nclass = len(alphabet) + 1 +# Testing images are scaled to have height 32. Widths are +# proportionally scaled with heights, but at least 100 pixels +def scale_img_para(img, min_width=100, fixed_height=32): + height = img.size[1] + width = img.size[0] + scale = float(fixed_height)/height + w = int(width * scale) + if w < min_width: + w = min_width + + return w, fixed_height + def crnn_recognition(cropped_image, model): converter = lib.utils.strLabelConverter(alphabet) @@ -21,8 +36,9 @@ def crnn_recognition(cropped_image, model): image = cropped_image.convert('L') ## - w = int(image.size[0] / (280 * 1.0 / 160)) - transformer = lib.dataset.resizeNormalize((w, 32)) + #w = int(image.size[0] / (280 * 1.0 / 160)) + w, h = scale_img_para(image) + transformer = lib.dataset.resizeNormalize((w, h)) image = transformer(image) if torch.cuda.is_available(): image = image.cuda() @@ -44,11 +60,13 @@ def crnn_recognition(cropped_image, model): # crnn network model = Net.CRNN(nclass) - if torch.cuda.is_available(): + if running_mode == 'gpu' and torch.cuda.is_available(): model = model.cuda() - print('loading pretrained model from {0}'.format(crnn_model_path)) + model.load_state_dict(torch.load(crnn_model_path)) + else: + model.load_state_dict(torch.load(crnn_model_path, map_location='cpu')) - model.load_state_dict(torch.load(crnn_model_path)) + print('loading pretrained model from {0}'.format(crnn_model_path)) files = os.listdir(IMG_ROOT) for file in files: