import os import sys from PIL import Image __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../.."))) os.environ["FLAGS_allocator_strategy"] = "auto_growth" import math import time import cv2 import numpy as np import utility from postprocess import build_post_process def _check_image_file(path): img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif"} return any([path.lower().endswith(e) for e in img_end]) def get_image_file_list(img_file): imgs_lists = [] if img_file is None or not os.path.exists(img_file): raise Exception("not found any img file in {}".format(img_file)) img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif"} if os.path.isfile(img_file) and _check_image_file(img_file): imgs_lists.append(img_file) elif os.path.isdir(img_file): for single_file in os.listdir(img_file): file_path = os.path.join(img_file, single_file) if os.path.isfile(file_path) and _check_image_file(file_path): imgs_lists.append(file_path) if len(imgs_lists) == 0: raise Exception("not found any img file in {}".format(img_file)) imgs_lists = sorted(imgs_lists) return imgs_lists def check_and_read_gif(img_path): if os.path.basename(img_path)[-3:] in ["gif", "GIF"]: gif = cv2.VideoCapture(img_path) ret, frame = gif.read() if not ret: return None, False if len(frame.shape) == 2 or frame.shape[-1] == 1: frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) imgvalue = frame[:, :, ::-1] return imgvalue, True return None, False class TextRecognizer(object): def __init__(self, args): self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] self.rec_batch_num = args.rec_batch_num self.rec_algorithm = args.rec_algorithm postprocess_params = { "name": "CTCLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } if self.rec_algorithm == "SRN": postprocess_params = { "name": "SRNLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } elif self.rec_algorithm == "RARE": postprocess_params = { "name": "AttnLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } elif self.rec_algorithm == "NRTR": postprocess_params = { "name": "NRTRLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } elif self.rec_algorithm == "SAR": postprocess_params = { "name": "SARLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } self.postprocess_op = build_post_process(postprocess_params) ( self.predictor, self.input_tensor, self.output_tensors, self.config, ) = utility.create_predictor(args, "rec") self.use_onnx = args.use_onnx def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape if self.rec_algorithm == "NRTR": img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # return padding_im image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize([100, 32], Image.ANTIALIAS) img = np.array(img) norm_img = np.expand_dims(img, -1) norm_img = norm_img.transpose((2, 0, 1)) return norm_img.astype(np.float32) / 128.0 - 1.0 assert imgC == img.shape[2] imgW = int((imgH * max_wh_ratio)) if self.use_onnx: w = self.input_tensor.shape[3:][0] if w is not None and w > 0: imgW = w h, w = img.shape[:2] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) if self.rec_algorithm == "RARE": if resized_w > self.rec_image_shape[2]: resized_w = self.rec_image_shape[2] imgW = self.rec_image_shape[2] resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype("float32") resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image return padding_im def resize_norm_img_svtr(self, img, image_shape): imgC, imgH, imgW = image_shape resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) resized_image = resized_image.astype("float32") resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 return resized_image def resize_norm_img_srn(self, img, image_shape): imgC, imgH, imgW = image_shape img_black = np.zeros((imgH, imgW)) im_hei = img.shape[0] im_wid = img.shape[1] if im_wid <= im_hei * 1: img_new = cv2.resize(img, (imgH * 1, imgH)) elif im_wid <= im_hei * 2: img_new = cv2.resize(img, (imgH * 2, imgH)) elif im_wid <= im_hei * 3: img_new = cv2.resize(img, (imgH * 3, imgH)) else: img_new = cv2.resize(img, (imgW, imgH)) img_np = np.asarray(img_new) img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) img_black[:, 0 : img_np.shape[1]] = img_np img_black = img_black[:, :, np.newaxis] row, col, c = img_black.shape c = 1 return np.reshape(img_black, (c, row, col)).astype(np.float32) def srn_other_inputs(self, image_shape, num_heads, max_text_length): imgC, imgH, imgW = image_shape feature_dim = int((imgH / 8) * (imgW / 8)) encoder_word_pos = ( np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64") ) gsrm_word_pos = ( np.array(range(0, max_text_length)) .reshape((max_text_length, 1)) .astype("int64") ) gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( [-1, 1, max_text_length, max_text_length] ) gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype( "float32" ) * [-1e9] gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( [-1, 1, max_text_length, max_text_length] ) gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype( "float32" ) * [-1e9] encoder_word_pos = encoder_word_pos[np.newaxis, :] gsrm_word_pos = gsrm_word_pos[np.newaxis, :] return [ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, ] def process_image_srn(self, img, image_shape, num_heads, max_text_length): norm_img = self.resize_norm_img_srn(img, image_shape) norm_img = norm_img[np.newaxis, :] [ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, ] = self.srn_other_inputs(image_shape, num_heads, max_text_length) gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) encoder_word_pos = encoder_word_pos.astype(np.int64) gsrm_word_pos = gsrm_word_pos.astype(np.int64) return ( norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, ) def resize_norm_img_sar(self, img, image_shape, width_downsample_ratio=0.25): imgC, imgH, imgW_min, imgW_max = image_shape h = img.shape[0] w = img.shape[1] valid_ratio = 1.0 # make sure new_width is an integral multiple of width_divisor. width_divisor = int(1 / width_downsample_ratio) # resize ratio = w / float(h) resize_w = math.ceil(imgH * ratio) if resize_w % width_divisor != 0: resize_w = round(resize_w / width_divisor) * width_divisor if imgW_min is not None: resize_w = max(imgW_min, resize_w) if imgW_max is not None: valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) resize_w = min(imgW_max, resize_w) resized_image = cv2.resize(img, (resize_w, imgH)) resized_image = resized_image.astype("float32") # norm if image_shape[0] == 1: resized_image = resized_image / 255 resized_image = resized_image[np.newaxis, :] else: resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 resize_shape = resized_image.shape padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) padding_im[:, :, 0:resize_w] = resized_image pad_shape = padding_im.shape return padding_im, resize_shape, pad_shape, valid_ratio def __call__(self, img_list): img_num = len(img_list) # Calculate the aspect ratio of all text bars width_list = [] for img in img_list: width_list.append(img.shape[1] / float(img.shape[0])) # Sorting can speed up the recognition process indices = np.argsort(np.array(width_list)) rec_res = [["", 0.0]] * img_num batch_num = self.rec_batch_num st = time.time() for beg_img_no in range(0, img_num, batch_num): end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] imgC, imgH, imgW = self.rec_image_shape max_wh_ratio = imgW / imgH # max_wh_ratio = 0 for ino in range(beg_img_no, end_img_no): h, w = img_list[indices[ino]].shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for ino in range(beg_img_no, end_img_no): if self.rec_algorithm == "SAR": norm_img, _, _, valid_ratio = self.resize_norm_img_sar( img_list[indices[ino]], self.rec_image_shape ) norm_img = norm_img[np.newaxis, :] valid_ratio = np.expand_dims(valid_ratio, axis=0) valid_ratios = [] valid_ratios.append(valid_ratio) norm_img_batch.append(norm_img) elif self.rec_algorithm == "SRN": norm_img = self.process_image_srn( img_list[indices[ino]], self.rec_image_shape, 8, 25 ) encoder_word_pos_list = [] gsrm_word_pos_list = [] gsrm_slf_attn_bias1_list = [] gsrm_slf_attn_bias2_list = [] encoder_word_pos_list.append(norm_img[1]) gsrm_word_pos_list.append(norm_img[2]) gsrm_slf_attn_bias1_list.append(norm_img[3]) gsrm_slf_attn_bias2_list.append(norm_img[4]) norm_img_batch.append(norm_img[0]) elif self.rec_algorithm == "SVTR": norm_img = self.resize_norm_img_svtr( img_list[indices[ino]], self.rec_image_shape ) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) else: norm_img = self.resize_norm_img( img_list[indices[ino]], max_wh_ratio ) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() if self.rec_algorithm == "SRN": encoder_word_pos_list = np.concatenate(encoder_word_pos_list) gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list) gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list) inputs = [ norm_img_batch, encoder_word_pos_list, gsrm_word_pos_list, gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list, ] if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = norm_img_batch outputs = self.predictor.run(self.output_tensors, input_dict) preds = {"predict": outputs[2]} else: input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[i]) input_tensor.copy_from_cpu(inputs[i]) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = {"predict": outputs[2]} elif self.rec_algorithm == "SAR": valid_ratios = np.concatenate(valid_ratios) inputs = [ norm_img_batch, valid_ratios, ] if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = norm_img_batch outputs = self.predictor.run(self.output_tensors, input_dict) preds = outputs[0] else: input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[i]) input_tensor.copy_from_cpu(inputs[i]) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = outputs[0] else: if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = norm_img_batch outputs = self.predictor.run(self.output_tensors, input_dict) preds = outputs[0] else: self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if len(outputs) != 1: preds = outputs else: preds = outputs[0] rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] return rec_res, time.time() - st def main(args): image_file_list = get_image_file_list(args.image_dir) text_recognizer = TextRecognizer(args) valid_image_file_list = [] img_list = [] # warmup 2 times if args.warmup: img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8) for i in range(2): res = text_recognizer([img] * int(args.rec_batch_num)) for image_file in image_file_list: img = cv2.imread(image_file) valid_image_file_list.append(image_file) img_list.append(img) for i in range(10): t0 = time.time() rec_res, _ = text_recognizer(img_list) print((time.time() - t0) * 1000) for ino in range(len(img_list)): print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino])) if __name__ == "__main__": main(utility.parse_args())