155 lines
5.4 KiB
Python
155 lines
5.4 KiB
Python
import os
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import cv2
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import numpy as np
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import faiss
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import pickle
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from paddleclas.deploy.utils import logger, config
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from paddleclas.deploy.utils.get_image_list import get_image_and_label_list
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from paddleclas.deploy.python.build_gallery import GalleryBuilder
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from paddleclas.deploy.python.predict_rec import RecPredictor
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from paddleclas.deploy.python.predict_det import DetPredictor
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class SystemPredictor(object):
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def __init__(self, config):
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self.config = config
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self.det_predictor = DetPredictor(config)
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self.rec_predictor = RecPredictor(config)
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# create searcher
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self.return_k = self.config['IndexProcess']['return_k']
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self.index_dir = self.config['IndexProcess']['index_dir']
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if config['IndexProcess'].get("binary_index", False):
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self.Searcher = faiss.read_index_binary(
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os.path.join(self.index_dir, "vector.index"))
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else:
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self.Searcher = faiss.read_index(
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os.path.join(self.index_dir, "vector.index"))
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with open(os.path.join(self.index_dir, "id_map.pkl"), "rb") as fd:
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self.id_map = pickle.load(fd)
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def append_self(self, results, shape):
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results.append({
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"class_id": 0,
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"score": 1.0,
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"bbox":
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np.array([0, 0, shape[1], shape[0]]), # xmin, ymin, xmax, ymax
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"label_name": "foreground",
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})
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return results
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def nms_to_rec_results(self, results, thresh=0.1):
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filtered_results = []
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x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
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y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
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x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
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y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
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scores = np.array([r["rec_scores"] for r in results])
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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while order.size > 0:
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i = order[0]
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= thresh)[0]
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order = order[inds + 1]
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filtered_results.append(results[i])
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return filtered_results
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def sort_output_by_scores(self, outputs_list, scores_list):
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scores_list = np.array(scores_list)
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order = scores_list.argsort()[::-1]
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outputs = []
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for idx in order:
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outputs.append(outputs_list[idx])
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return outputs
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def predict(self, img):
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all_det_results = self.det_predictor.predict(img)
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results = self.append_self(all_det_results, img.shape)
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outputs_list = []
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scores_list = []
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for result in results:
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preds = {}
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xmin, ymin, xmax, ymax = result["bbox"].astype("int")
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crop_img = img[ymin:ymax, xmin:xmax, :].copy()
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rec_results = self.rec_predictor.predict(crop_img)
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scores, docs = self.Searcher.search(rec_results, self.return_k)
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outputs_list.append(self.id_map[docs[0][0]].split()[1])
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scores_list.append(scores[0][0])
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outputs = self.sort_output_by_scores(outputs_list, scores_list)
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return outputs
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def get_recall(gth, pred):
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assert len(gth) == len(pred)
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recall_list = [0] * len(pred[0])
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for g, p in zip(gth, pred):
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for i in range(len(pred[0])):
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if g in p[:i + 1]:
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recall_list[i] += 1
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recall_list = [x / len(pred) for x in recall_list]
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return recall_list
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def main(config):
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# build gallery
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assert "IndexProcess" in config.keys(), "Index config not found ... "
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operation_method = config["IndexProcess"].get("index_operation",
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"new").lower()
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assert operation_method == "new", "The operation should be 'new' during evaluating."
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GalleryBuilder(config)
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syster_predictor = SystemPredictor(config)
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# get images
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assert "Eval" in config.keys(), "Eval config not found ... "
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eval_imgs_list, eval_gth = get_image_and_label_list(
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config["Eval"]["image_root"], config["Eval"]["cls_label_path"])
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# create output file
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assert "output_dir" in config['Eval'].keys(
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), "Output dir config not found ... "
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output_dir = config['Eval']["output_dir"]
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if os.path.exists(output_dir) is False:
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os.mkdir(output_dir)
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results_file = open(os.path.join(output_dir, 'eval_resutls.txt'), 'a+')
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results_file.write("Dataset name: %s\n" % (config['Eval']['name']))
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# evaluation
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predict = []
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for img_name in eval_imgs_list:
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img = cv2.imread(img_name)
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img = img[:, :, ::-1]
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output = syster_predictor.predict(img)
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predict.append(output)
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recall_list = get_recall(eval_gth, predict)
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for i, x in enumerate(recall_list):
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print("recal_{}: {:0.4f}".format(i + 1, x))
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results_file.write("recal_{}: {:0.4f}\n".format(i + 1, x))
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results_file.write('\n')
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(args.config, overrides=args.override, show=True)
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main(config)
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