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feat(rapidocr_paddle): support en char rec
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@@ -77,34 +77,26 @@ class CalRecBoxes:
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word_box_list = []
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word_box_content_list = []
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cn_width_list = []
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en_width_list = []
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cn_col_list = []
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for word, word_col, state in zip(word_list, word_col_list, state_list):
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if state == "cn":
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if len(word_col) != 1:
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char_seq_length = (word_col[-1] - word_col[0] + 1) * cell_width
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char_width = char_seq_length / (len(word_col) - 1)
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cn_width_list.append(char_width)
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cn_col_list += word_col
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word_box_content_list += word
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else:
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cell_x_start = bbox_x_start + int(word_col[0] * cell_width)
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cell_x_end = bbox_x_start + int((word_col[-1] + 1) * cell_width)
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cell = [
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[cell_x_start, bbox_y_start],
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[cell_x_end, bbox_y_start],
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[cell_x_end, bbox_y_end],
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[cell_x_start, bbox_y_end],
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]
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word_box_list.append(cell)
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word_box_content_list.append("".join(word))
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en_col_list = []
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if len(cn_col_list) != 0:
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if len(cn_width_list) != 0:
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avg_char_width = np.mean(cn_width_list)
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def cal_char_width(width_list, word_col_):
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if len(word_col_) == 1:
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return
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char_total_length = (word_col_[-1] - word_col_[0] + 1) * cell_width
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char_width = char_total_length / (len(word_col_) - 1)
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width_list.append(char_width)
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def cal_box(col_list, width_list, word_box_list_):
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if len(col_list) == 0:
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return
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if len(width_list) != 0:
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avg_char_width = np.mean(width_list)
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else:
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avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_txt)
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for center_idx in cn_col_list:
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for center_idx in col_list:
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center_x = (center_idx + 0.5) * cell_width
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cell_x_start = max(int(center_x - avg_char_width / 2), 0) + bbox_x_start
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cell_x_end = (
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@@ -117,7 +109,20 @@ class CalRecBoxes:
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[cell_x_end, bbox_y_end],
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[cell_x_start, bbox_y_end],
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]
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word_box_list.append(cell)
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word_box_list_.append(cell)
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for word, word_col, state in zip(word_list, word_col_list, state_list):
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if state == "cn":
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cal_char_width(cn_width_list, word_col)
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cn_col_list += word_col
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word_box_content_list += word
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else:
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cal_char_width(en_width_list, word_col)
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en_col_list += word_col
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word_box_content_list += word
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cal_box(cn_col_list, cn_width_list, word_box_list)
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cal_box(en_col_list, en_width_list, word_box_list)
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sorted_word_box_list = sorted(word_box_list, key=lambda box: box[0][0])
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return word_box_content_list, sorted_word_box_list
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@@ -256,5 +261,9 @@ class CalRecBoxes:
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p23[np.where(p23[:, 1] == np.min(p23[:, 1]))],
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p23[np.where(p23[:, 1] == np.max(p23[:, 1]))],
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)
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# 解决单字矩形框重叠导致多个相同框的情况
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p1 = p1[:1, :]
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p2 = p2[:1, :]
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p3 = p3[:1, :]
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p4 = p4[:1, :]
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return np.array([p1, p2, p3, p4]).reshape((-1, 2)).tolist()
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@@ -29,7 +29,7 @@ MODULE_NAME = "rapidocr_paddle"
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obtainer = GetPyPiLatestVersion()
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try:
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latest_version = obtainer(MODULE_NAME)
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VERSION_NUM = obtainer.version_add_one(latest_version)
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VERSION_NUM = obtainer.version_add_one(latest_version, add_patch=True)
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except:
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VERSION_NUM = "0.0.1"
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@@ -212,18 +212,11 @@ def test_input_three_ndim_one_channel():
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),
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(
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"text_vertical_words.png",
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[
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"已",
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"取",
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"之",
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"時",
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"不",
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"參",
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"一",
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"人",
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"见",
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"而",
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], # rapidocr_onnxruntime: 見, rapidocr_paddle: 见
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["已", "取", "之", "時", "不", "參", "一", "人", "见", "而"],
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),
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(
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"issue_170.png",
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["T", "E", "S", "T"],
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),
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],
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)
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