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Refactor code (#8341)
### What problem does this PR solve? 1. rename var 2. update if statement ### Type of change - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
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@ -133,7 +133,7 @@ class Recognizer:
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@staticmethod
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def layouts_cleanup(boxes, layouts, far=2, thr=0.7):
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def notOverlapped(a, b):
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def not_overlapped(a, b):
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return any([a["x1"] < b["x0"],
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a["x0"] > b["x1"],
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a["bottom"] < b["top"],
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@ -144,7 +144,7 @@ class Recognizer:
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j = i + 1
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while j < min(i + far, len(layouts)) \
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and (layouts[i].get("type", "") != layouts[j].get("type", "")
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or notOverlapped(layouts[i], layouts[j])):
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or not_overlapped(layouts[i], layouts[j])):
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j += 1
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if j >= min(i + far, len(layouts)):
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i += 1
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@ -163,9 +163,9 @@ class Recognizer:
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area_i, area_i_1 = 0, 0
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for b in boxes:
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if not notOverlapped(b, layouts[i]):
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if not not_overlapped(b, layouts[i]):
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area_i += Recognizer.overlapped_area(b, layouts[i], False)
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if not notOverlapped(b, layouts[j]):
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if not not_overlapped(b, layouts[j]):
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area_i_1 += Recognizer.overlapped_area(b, layouts[j], False)
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if area_i > area_i_1:
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@ -408,18 +408,18 @@ class Recognizer:
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def __call__(self, image_list, thr=0.7, batch_size=16):
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res = []
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imgs = []
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images = []
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for i in range(len(image_list)):
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if not isinstance(image_list[i], np.ndarray):
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imgs.append(np.array(image_list[i]))
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images.append(np.array(image_list[i]))
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else:
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imgs.append(image_list[i])
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images.append(image_list[i])
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batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
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batch_loop_cnt = math.ceil(float(len(images)) / batch_size)
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for i in range(batch_loop_cnt):
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start_index = i * batch_size
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end_index = min((i + 1) * batch_size, len(imgs))
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batch_image_list = imgs[start_index:end_index]
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end_index = min((i + 1) * batch_size, len(images))
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batch_image_list = images[start_index:end_index]
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inputs = self.preprocess(batch_image_list)
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logging.debug("preprocess")
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for ins in inputs:
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