import os import sys __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 json import sys import time import cv2 import numpy as np import utility from postprocess import build_post_process from ppocr.data import create_operators, transform class TextDetector(object): def __init__(self, args): self.args = args self.det_algorithm = args.det_algorithm self.use_onnx = args.use_onnx pre_process_list = [ { "DetResizeForTest": { "limit_side_len": args.det_limit_side_len, "limit_type": args.det_limit_type, } }, { "NormalizeImage": { "std": [0.229, 0.224, 0.225], "mean": [0.485, 0.456, 0.406], "scale": "1./255.", "order": "hwc", } }, {"ToCHWImage": None}, {"KeepKeys": {"keep_keys": ["image", "shape"]}}, ] postprocess_params = {} if self.det_algorithm == "DB": postprocess_params["name"] = "DBPostProcess" postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode elif self.det_algorithm == "EAST": postprocess_params["name"] = "EASTPostProcess" postprocess_params["score_thresh"] = args.det_east_score_thresh postprocess_params["cover_thresh"] = args.det_east_cover_thresh postprocess_params["nms_thresh"] = args.det_east_nms_thresh elif self.det_algorithm == "SAST": pre_process_list[0] = { "DetResizeForTest": {"resize_long": args.det_limit_side_len} } postprocess_params["name"] = "SASTPostProcess" postprocess_params["score_thresh"] = args.det_sast_score_thresh postprocess_params["nms_thresh"] = args.det_sast_nms_thresh self.det_sast_polygon = args.det_sast_polygon if self.det_sast_polygon: postprocess_params["sample_pts_num"] = 6 postprocess_params["expand_scale"] = 1.2 postprocess_params["shrink_ratio_of_width"] = 0.2 else: postprocess_params["sample_pts_num"] = 2 postprocess_params["expand_scale"] = 1.0 postprocess_params["shrink_ratio_of_width"] = 0.3 elif self.det_algorithm == "PSE": postprocess_params["name"] = "PSEPostProcess" postprocess_params["thresh"] = args.det_pse_thresh postprocess_params["box_thresh"] = args.det_pse_box_thresh postprocess_params["min_area"] = args.det_pse_min_area postprocess_params["box_type"] = args.det_pse_box_type postprocess_params["scale"] = args.det_pse_scale self.det_pse_box_type = args.det_pse_box_type elif self.det_algorithm == "FCE": pre_process_list[0] = {"DetResizeForTest": {"rescale_img": [1080, 736]}} postprocess_params["name"] = "FCEPostProcess" postprocess_params["scales"] = args.scales postprocess_params["alpha"] = args.alpha postprocess_params["beta"] = args.beta postprocess_params["fourier_degree"] = args.fourier_degree postprocess_params["box_type"] = args.det_fce_box_type self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) ( self.predictor, self.input_tensor, self.output_tensors, self.config, ) = utility.create_predictor(args, "det") if self.use_onnx: img_h, img_w = self.input_tensor.shape[2:] if img_h is not None and img_w is not None and img_h > 0 and img_w > 0: pre_process_list[0] = { "DetResizeForTest": {"image_shape": [img_h, img_w]} } self.preprocess_op = create_operators(pre_process_list) def order_points_clockwise(self, pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def clip_det_res(self, points, img_height, img_width): for pno in range(points.shape[0]): points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) return points def filter_tag_det_res(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: box = self.order_points_clockwise(box) box = self.clip_det_res(box, img_height, img_width) rect_width = int(np.linalg.norm(box[0] - box[1])) rect_height = int(np.linalg.norm(box[0] - box[3])) if rect_width <= 3 or rect_height <= 3: continue dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def filter_tag_det_res_only_clip(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: box = self.clip_det_res(box, img_height, img_width) dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def __call__(self, img): ori_im = img.copy() data = {"image": img} st = time.time() data = transform(data, self.preprocess_op) img, shape_list = data if img is None: return None, 0 img = np.expand_dims(img, axis=0) shape_list = np.expand_dims(shape_list, axis=0) img = img.copy() if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = img outputs = self.predictor.run(self.output_tensors, input_dict) else: self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = {} if self.det_algorithm == "EAST": preds["f_geo"] = outputs[0] preds["f_score"] = outputs[1] elif self.det_algorithm == "SAST": preds["f_border"] = outputs[0] preds["f_score"] = outputs[1] preds["f_tco"] = outputs[2] preds["f_tvo"] = outputs[3] elif self.det_algorithm in ["DB", "PSE"]: preds["maps"] = outputs[0] elif self.det_algorithm == "FCE": for i, output in enumerate(outputs): preds["level_{}".format(i)] = output else: raise NotImplementedError # self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]["points"] if (self.det_algorithm == "SAST" and self.det_sast_polygon) or ( self.det_algorithm in ["PSE", "FCE"] and self.postprocess_op.box_type == "poly" ): dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) et = time.time() return dt_boxes, et - st if __name__ == "__main__": args = utility.parse_args() image_file_list = ["images/y.png"] text_detector = TextDetector(args) count = 0 total_time = 0 draw_img_save = "./inference_results" if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(2): res = text_detector(img) if not os.path.exists(draw_img_save): os.makedirs(draw_img_save) save_results = [] for image_file in image_file_list: img = cv2.imread(image_file) for _ in range(10): st = time.time() dt_boxes, _ = text_detector(img) elapse = time.time() - st print(elapse * 1000) if count > 0: total_time += elapse count += 1 save_pred = ( os.path.basename(image_file) + "\t" + str(json.dumps([x.tolist() for x in dt_boxes])) + "\n" ) save_results.append(save_pred) src_im = utility.draw_text_det_res(dt_boxes, image_file) img_name_pure = os.path.split(image_file)[-1] img_path = os.path.join(draw_img_save, "det_res_{}".format(img_name_pure)) cv2.imwrite(img_path, src_im) with open(os.path.join(draw_img_save, "det_results.txt"), "w") as f: f.writelines(save_results) f.close()