48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import os.path
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from collections import defaultdict
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import cv2
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import numpy as np
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from util import image_util
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from . import PADDLE_DET, MODEL_DIR
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from .deploy.third_engine.onnx.infer import PredictConfig
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from .deploy.third_engine.onnx.preprocess import Compose
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def predict_image(infer_config, predictor, img_path):
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# load preprocess transforms
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transforms = Compose(infer_config.preprocess_infos)
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# predict image
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inputs = transforms(img_path)
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inputs['image'] = np.array(inputs['image']).astype('float32')
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inputs_name = [var.name for var in predictor.get_inputs()]
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inputs = {k: inputs[k][None,] for k in inputs_name}
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outputs = predictor.run(output_names=None, input_feed=inputs)
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bboxes = np.array(outputs[0])
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result = defaultdict(list)
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for bbox in bboxes:
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if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
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result[bbox[0]].append({'score': bbox[1], 'box': bbox[2:]})
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return result
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def detect_image(img_path):
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infer_cfg = os.path.join(MODEL_DIR, 'infer_cfg.yml')
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# load infer config
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infer_config = PredictConfig(infer_cfg)
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return predict_image(infer_config, PADDLE_DET, img_path)
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def get_book_areas(img_path):
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detect_result = detect_image(img_path)
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book_areas = detect_result[73]
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result = []
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image = cv2.imread(img_path)
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for book_area in book_areas:
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result.append(image_util.capture(image, book_area['box']))
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return result
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