459 lines
16 KiB
Python
459 lines
16 KiB
Python
|
# Copyright (c) Opendatalab. All rights reserved.
|
|||
|
|
import copy
|
|||
|
|
import cv2
|
|||
|
|
import numpy as np
|
|||
|
|
|
|||
|
|
|
|||
|
|
class OcrConfidence:
|
|||
|
|
min_confidence = 0.5
|
|||
|
|
min_width = 3
|
|||
|
|
|
|||
|
|
LINE_WIDTH_TO_HEIGHT_RATIO_THRESHOLD = 4 # 一般情况下，行宽度超过高度4倍时才是一个正常的横向文本块
|
|||
|
|
|
|||
|
|
|
|||
|
|
def merge_spans_to_line(spans, threshold=0.6):
|
|||
|
|
if len(spans) == 0:
|
|||
|
|
return []
|
|||
|
|
else:
|
|||
|
|
# 按照y0坐标排序
|
|||
|
|
spans.sort(key=lambda span: span['bbox'][1])
|
|||
|
|
|
|||
|
|
lines = []
|
|||
|
|
current_line = [spans[0]]
|
|||
|
|
for span in spans[1:]:
|
|||
|
|
# 如果当前的span与当前行的最后一个span在y轴上重叠，则添加到当前行
|
|||
|
|
if _is_overlaps_y_exceeds_threshold(span['bbox'], current_line[-1]['bbox'], threshold):
|
|||
|
|
current_line.append(span)
|
|||
|
|
else:
|
|||
|
|
# 否则，开始新行
|
|||
|
|
lines.append(current_line)
|
|||
|
|
current_line = [span]
|
|||
|
|
|
|||
|
|
# 添加最后一行
|
|||
|
|
if current_line:
|
|||
|
|
lines.append(current_line)
|
|||
|
|
|
|||
|
|
return lines
|
|||
|
|
|
|||
|
|
def _is_overlaps_y_exceeds_threshold(bbox1,
|
|||
|
|
bbox2,
|
|||
|
|
overlap_ratio_threshold=0.8):
|
|||
|
|
"""检查两个bbox在y轴上是否有重叠，并且该重叠区域的高度占两个bbox高度更低的那个超过80%"""
|
|||
|
|
_, y0_1, _, y1_1 = bbox1
|
|||
|
|
_, y0_2, _, y1_2 = bbox2
|
|||
|
|
|
|||
|
|
overlap = max(0, min(y1_1, y1_2) - max(y0_1, y0_2))
|
|||
|
|
height1, height2 = y1_1 - y0_1, y1_2 - y0_2
|
|||
|
|
# max_height = max(height1, height2)
|
|||
|
|
min_height = min(height1, height2)
|
|||
|
|
|
|||
|
|
return (overlap / min_height) > overlap_ratio_threshold if min_height > 0 else False
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _is_overlaps_x_exceeds_threshold(bbox1,
|
|||
|
|
bbox2,
|
|||
|
|
overlap_ratio_threshold=0.8):
|
|||
|
|
"""检查两个bbox在x轴上是否有重叠，并且该重叠区域的宽度占两个bbox宽度更低的那个超过指定阈值"""
|
|||
|
|
x0_1, _, x1_1, _ = bbox1
|
|||
|
|
x0_2, _, x1_2, _ = bbox2
|
|||
|
|
|
|||
|
|
overlap = max(0, min(x1_1, x1_2) - max(x0_1, x0_2))
|
|||
|
|
width1, width2 = x1_1 - x0_1, x1_2 - x0_2
|
|||
|
|
min_width = min(width1, width2)
|
|||
|
|
|
|||
|
|
return (overlap / min_width) > overlap_ratio_threshold if min_width > 0 else False
|
|||
|
|
|
|||
|
|
|
|||
|
|
def img_decode(content: bytes):
|
|||
|
|
np_arr = np.frombuffer(content, dtype=np.uint8)
|
|||
|
|
return cv2.imdecode(np_arr, cv2.IMREAD_UNCHANGED)
|
|||
|
|
|
|||
|
|
def check_img(img):
|
|||
|
|
if isinstance(img, bytes):
|
|||
|
|
img = img_decode(img)
|
|||
|
|
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
|||
|
|
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
|||
|
|
return img
|
|||
|
|
|
|||
|
|
|
|||
|
|
def alpha_to_color(img, alpha_color=(255, 255, 255)):
|
|||
|
|
if len(img.shape) == 3 and img.shape[2] == 4:
|
|||
|
|
B, G, R, A = cv2.split(img)
|
|||
|
|
alpha = A / 255
|
|||
|
|
|
|||
|
|
R = (alpha_color[0] * (1 - alpha) + R * alpha).astype(np.uint8)
|
|||
|
|
G = (alpha_color[1] * (1 - alpha) + G * alpha).astype(np.uint8)
|
|||
|
|
B = (alpha_color[2] * (1 - alpha) + B * alpha).astype(np.uint8)
|
|||
|
|
|
|||
|
|
img = cv2.merge((B, G, R))
|
|||
|
|
return img
|
|||
|
|
|
|||
|
|
|
|||
|
|
def preprocess_image(_image):
|
|||
|
|
alpha_color = (255, 255, 255)
|
|||
|
|
_image = alpha_to_color(_image, alpha_color)
|
|||
|
|
return _image
|
|||
|
|
|
|||
|
|
|
|||
|
|
def sorted_boxes(dt_boxes):
|
|||
|
|
"""
|
|||
|
|
Sort text boxes in order from top to bottom, left to right
|
|||
|
|
args:
|
|||
|
|
dt_boxes(array):detected text boxes with shape [4, 2]
|
|||
|
|
return:
|
|||
|
|
sorted boxes(array) with shape [4, 2]
|
|||
|
|
"""
|
|||
|
|
num_boxes = dt_boxes.shape[0]
|
|||
|
|
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
|
|||
|
|
_boxes = list(sorted_boxes)
|
|||
|
|
|
|||
|
|
for i in range(num_boxes - 1):
|
|||
|
|
for j in range(i, -1, -1):
|
|||
|
|
if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
|
|||
|
|
(_boxes[j + 1][0][0] < _boxes[j][0][0]):
|
|||
|
|
tmp = _boxes[j]
|
|||
|
|
_boxes[j] = _boxes[j + 1]
|
|||
|
|
_boxes[j + 1] = tmp
|
|||
|
|
else:
|
|||
|
|
break
|
|||
|
|
return _boxes
|
|||
|
|
|
|||
|
|
|
|||
|
|
def bbox_to_points(bbox):
|
|||
|
|
""" 将bbox格式转换为四个顶点的数组 """
|
|||
|
|
x0, y0, x1, y1 = bbox
|
|||
|
|
return np.array([[x0, y0], [x1, y0], [x1, y1], [x0, y1]]).astype('float32')
|
|||
|
|
|
|||
|
|
|
|||
|
|
def points_to_bbox(points):
|
|||
|
|
""" 将四个顶点的数组转换为bbox格式 """
|
|||
|
|
x0, y0 = points[0]
|
|||
|
|
x1, _ = points[1]
|
|||
|
|
_, y1 = points[2]
|
|||
|
|
return [x0, y0, x1, y1]
|
|||
|
|
|
|||
|
|
|
|||
|
|
def merge_intervals(intervals):
|
|||
|
|
# Sort the intervals based on the start value
|
|||
|
|
intervals.sort(key=lambda x: x[0])
|
|||
|
|
|
|||
|
|
merged = []
|
|||
|
|
for interval in intervals:
|
|||
|
|
# If the list of merged intervals is empty or if the current
|
|||
|
|
# interval does not overlap with the previous, simply append it.
|
|||
|
|
if not merged or merged[-1][1] < interval[0]:
|
|||
|
|
merged.append(interval)
|
|||
|
|
else:
|
|||
|
|
# Otherwise, there is overlap, so we merge the current and previous intervals.
|
|||
|
|
merged[-1][1] = max(merged[-1][1], interval[1])
|
|||
|
|
|
|||
|
|
return merged
|
|||
|
|
|
|||
|
|
|
|||
|
|
def remove_intervals(original, masks):
|
|||
|
|
# Merge all mask intervals
|
|||
|
|
merged_masks = merge_intervals(masks)
|
|||
|
|
|
|||
|
|
result = []
|
|||
|
|
original_start, original_end = original
|
|||
|
|
|
|||
|
|
for mask in merged_masks:
|
|||
|
|
mask_start, mask_end = mask
|
|||
|
|
|
|||
|
|
# If the mask starts after the original range, ignore it
|
|||
|
|
if mask_start > original_end:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# If the mask ends before the original range starts, ignore it
|
|||
|
|
if mask_end < original_start:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# Remove the masked part from the original range
|
|||
|
|
if original_start < mask_start:
|
|||
|
|
result.append([original_start, mask_start - 1])
|
|||
|
|
|
|||
|
|
original_start = max(mask_end + 1, original_start)
|
|||
|
|
|
|||
|
|
# Add the remaining part of the original range, if any
|
|||
|
|
if original_start <= original_end:
|
|||
|
|
result.append([original_start, original_end])
|
|||
|
|
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
|
|||
|
|
def update_det_boxes(dt_boxes, mfd_res):
|
|||
|
|
new_dt_boxes = []
|
|||
|
|
angle_boxes_list = []
|
|||
|
|
for text_box in dt_boxes:
|
|||
|
|
|
|||
|
|
if calculate_is_angle(text_box):
|
|||
|
|
angle_boxes_list.append(text_box)
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
text_bbox = points_to_bbox(text_box)
|
|||
|
|
masks_list = []
|
|||
|
|
for mf_box in mfd_res:
|
|||
|
|
mf_bbox = mf_box['bbox']
|
|||
|
|
if _is_overlaps_y_exceeds_threshold(text_bbox, mf_bbox):
|
|||
|
|
masks_list.append([mf_bbox[0], mf_bbox[2]])
|
|||
|
|
text_x_range = [text_bbox[0], text_bbox[2]]
|
|||
|
|
text_remove_mask_range = remove_intervals(text_x_range, masks_list)
|
|||
|
|
temp_dt_box = []
|
|||
|
|
for text_remove_mask in text_remove_mask_range:
|
|||
|
|
temp_dt_box.append(bbox_to_points([text_remove_mask[0], text_bbox[1], text_remove_mask[1], text_bbox[3]]))
|
|||
|
|
if len(temp_dt_box) > 0:
|
|||
|
|
new_dt_boxes.extend(temp_dt_box)
|
|||
|
|
|
|||
|
|
new_dt_boxes.extend(angle_boxes_list)
|
|||
|
|
|
|||
|
|
return new_dt_boxes
|
|||
|
|
|
|||
|
|
|
|||
|
|
def merge_overlapping_spans(spans):
|
|||
|
|
"""
|
|||
|
|
Merges overlapping spans on the same line.
|
|||
|
|
|
|||
|
|
:param spans: A list of span coordinates [(x1, y1, x2, y2), ...]
|
|||
|
|
:return: A list of merged spans
|
|||
|
|
"""
|
|||
|
|
# Return an empty list if the input spans list is empty
|
|||
|
|
if not spans:
|
|||
|
|
return []
|
|||
|
|
|
|||
|
|
# Sort spans by their starting x-coordinate
|
|||
|
|
spans.sort(key=lambda x: x[0])
|
|||
|
|
|
|||
|
|
# Initialize the list of merged spans
|
|||
|
|
merged = []
|
|||
|
|
for span in spans:
|
|||
|
|
# Unpack span coordinates
|
|||
|
|
x1, y1, x2, y2 = span
|
|||
|
|
# If the merged list is empty or there's no horizontal overlap, add the span directly
|
|||
|
|
if not merged or merged[-1][2] < x1:
|
|||
|
|
merged.append(span)
|
|||
|
|
else:
|
|||
|
|
# If there is horizontal overlap, merge the current span with the previous one
|
|||
|
|
last_span = merged.pop()
|
|||
|
|
# Update the merged span's top-left corner to the smaller (x1, y1) and bottom-right to the larger (x2, y2)
|
|||
|
|
x1 = min(last_span[0], x1)
|
|||
|
|
y1 = min(last_span[1], y1)
|
|||
|
|
x2 = max(last_span[2], x2)
|
|||
|
|
y2 = max(last_span[3], y2)
|
|||
|
|
# Add the merged span back to the list
|
|||
|
|
merged.append((x1, y1, x2, y2))
|
|||
|
|
|
|||
|
|
# Return the list of merged spans
|
|||
|
|
return merged
|
|||
|
|
|
|||
|
|
|
|||
|
|
def merge_det_boxes(dt_boxes):
|
|||
|
|
"""
|
|||
|
|
Merge detection boxes.
|
|||
|
|
|
|||
|
|
This function takes a list of detected bounding boxes, each represented by four corner points.
|
|||
|
|
The goal is to merge these bounding boxes into larger text regions.
|
|||
|
|
|
|||
|
|
Parameters:
|
|||
|
|
dt_boxes (list): A list containing multiple text detection boxes, where each box is defined by four corner points.
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
list: A list containing the merged text regions, where each region is represented by four corner points.
|
|||
|
|
"""
|
|||
|
|
# Convert the detection boxes into a dictionary format with bounding boxes and type
|
|||
|
|
dt_boxes_dict_list = []
|
|||
|
|
angle_boxes_list = []
|
|||
|
|
for text_box in dt_boxes:
|
|||
|
|
text_bbox = points_to_bbox(text_box)
|
|||
|
|
|
|||
|
|
if calculate_is_angle(text_box):
|
|||
|
|
angle_boxes_list.append(text_box)
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
text_box_dict = {'bbox': text_bbox}
|
|||
|
|
dt_boxes_dict_list.append(text_box_dict)
|
|||
|
|
|
|||
|
|
# Merge adjacent text regions into lines
|
|||
|
|
lines = merge_spans_to_line(dt_boxes_dict_list)
|
|||
|
|
|
|||
|
|
# Initialize a new list for storing the merged text regions
|
|||
|
|
new_dt_boxes = []
|
|||
|
|
for line in lines:
|
|||
|
|
line_bbox_list = []
|
|||
|
|
for span in line:
|
|||
|
|
line_bbox_list.append(span['bbox'])
|
|||
|
|
|
|||
|
|
# 计算整行的宽度和高度
|
|||
|
|
min_x = min(bbox[0] for bbox in line_bbox_list)
|
|||
|
|
max_x = max(bbox[2] for bbox in line_bbox_list)
|
|||
|
|
min_y = min(bbox[1] for bbox in line_bbox_list)
|
|||
|
|
max_y = max(bbox[3] for bbox in line_bbox_list)
|
|||
|
|
line_width = max_x - min_x
|
|||
|
|
line_height = max_y - min_y
|
|||
|
|
|
|||
|
|
# 只有当行宽度超过高度4倍时才进行合并
|
|||
|
|
if line_width > line_height * LINE_WIDTH_TO_HEIGHT_RATIO_THRESHOLD:
|
|||
|
|
|
|||
|
|
# Merge overlapping text regions within the same line
|
|||
|
|
merged_spans = merge_overlapping_spans(line_bbox_list)
|
|||
|
|
|
|||
|
|
# Convert the merged text regions back to point format and add them to the new detection box list
|
|||
|
|
for span in merged_spans:
|
|||
|
|
new_dt_boxes.append(bbox_to_points(span))
|
|||
|
|
else:
|
|||
|
|
# 不进行合并，直接添加原始区域
|
|||
|
|
for bbox in line_bbox_list:
|
|||
|
|
new_dt_boxes.append(bbox_to_points(bbox))
|
|||
|
|
|
|||
|
|
new_dt_boxes.extend(angle_boxes_list)
|
|||
|
|
|
|||
|
|
return new_dt_boxes
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list):
|
|||
|
|
paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
|
|||
|
|
# Adjust the coordinates of the formula area
|
|||
|
|
adjusted_mfdetrec_res = []
|
|||
|
|
for mf_res in single_page_mfdetrec_res:
|
|||
|
|
mf_xmin, mf_ymin, mf_xmax, mf_ymax = mf_res["bbox"]
|
|||
|
|
# Adjust the coordinates of the formula area to the coordinates relative to the cropping area
|
|||
|
|
x0 = mf_xmin - xmin + paste_x
|
|||
|
|
y0 = mf_ymin - ymin + paste_y
|
|||
|
|
x1 = mf_xmax - xmin + paste_x
|
|||
|
|
y1 = mf_ymax - ymin + paste_y
|
|||
|
|
# Filter formula blocks outside the graph
|
|||
|
|
if any([x1 < 0, y1 < 0]) or any([x0 > new_width, y0 > new_height]):
|
|||
|
|
continue
|
|||
|
|
else:
|
|||
|
|
adjusted_mfdetrec_res.append({
|
|||
|
|
"bbox": [x0, y0, x1, y1],
|
|||
|
|
})
|
|||
|
|
return adjusted_mfdetrec_res
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_ocr_result_list(ocr_res, useful_list, ocr_enable, bgr_image, lang):
|
|||
|
|
paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
|
|||
|
|
ocr_result_list = []
|
|||
|
|
ori_im = bgr_image.copy()
|
|||
|
|
for box_ocr_res in ocr_res:
|
|||
|
|
|
|||
|
|
if len(box_ocr_res) == 2:
|
|||
|
|
p1, p2, p3, p4 = box_ocr_res[0]
|
|||
|
|
text, score = box_ocr_res[1]
|
|||
|
|
# logger.info(f"text: {text}, score: {score}")
|
|||
|
|
if score < OcrConfidence.min_confidence: # 过滤低置信度的结果
|
|||
|
|
continue
|
|||
|
|
else:
|
|||
|
|
p1, p2, p3, p4 = box_ocr_res
|
|||
|
|
text, score = "", 1
|
|||
|
|
|
|||
|
|
if ocr_enable:
|
|||
|
|
tmp_box = copy.deepcopy(np.array([p1, p2, p3, p4]).astype('float32'))
|
|||
|
|
img_crop = get_rotate_crop_image(ori_im, tmp_box)
|
|||
|
|
|
|||
|
|
# average_angle_degrees = calculate_angle_degrees(box_ocr_res[0])
|
|||
|
|
# if average_angle_degrees > 0.5:
|
|||
|
|
poly = [p1, p2, p3, p4]
|
|||
|
|
|
|||
|
|
if (p3[0] - p1[0]) < OcrConfidence.min_width:
|
|||
|
|
# logger.info(f"width too small: {p3[0] - p1[0]}, text: {text}")
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
if calculate_is_angle(poly):
|
|||
|
|
# logger.info(f"average_angle_degrees: {average_angle_degrees}, text: {text}")
|
|||
|
|
# 与x轴的夹角超过0.5度，对边界做一下矫正
|
|||
|
|
# 计算几何中心
|
|||
|
|
x_center = sum(point[0] for point in poly) / 4
|
|||
|
|
y_center = sum(point[1] for point in poly) / 4
|
|||
|
|
new_height = ((p4[1] - p1[1]) + (p3[1] - p2[1])) / 2
|
|||
|
|
new_width = p3[0] - p1[0]
|
|||
|
|
p1 = [x_center - new_width / 2, y_center - new_height / 2]
|
|||
|
|
p2 = [x_center + new_width / 2, y_center - new_height / 2]
|
|||
|
|
p3 = [x_center + new_width / 2, y_center + new_height / 2]
|
|||
|
|
p4 = [x_center - new_width / 2, y_center + new_height / 2]
|
|||
|
|
|
|||
|
|
# Convert the coordinates back to the original coordinate system
|
|||
|
|
p1 = [p1[0] - paste_x + xmin, p1[1] - paste_y + ymin]
|
|||
|
|
p2 = [p2[0] - paste_x + xmin, p2[1] - paste_y + ymin]
|
|||
|
|
p3 = [p3[0] - paste_x + xmin, p3[1] - paste_y + ymin]
|
|||
|
|
p4 = [p4[0] - paste_x + xmin, p4[1] - paste_y + ymin]
|
|||
|
|
|
|||
|
|
if ocr_enable:
|
|||
|
|
ocr_result_list.append({
|
|||
|
|
'category_id': 15,
|
|||
|
|
'poly': p1 + p2 + p3 + p4,
|
|||
|
|
'score': 1,
|
|||
|
|
'text': text,
|
|||
|
|
'np_img': img_crop,
|
|||
|
|
'lang': lang,
|
|||
|
|
})
|
|||
|
|
else:
|
|||
|
|
ocr_result_list.append({
|
|||
|
|
'category_id': 15,
|
|||
|
|
'poly': p1 + p2 + p3 + p4,
|
|||
|
|
'score': float(round(score, 2)),
|
|||
|
|
'text': text,
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return ocr_result_list
|
|||
|
|
|
|||
|
|
|
|||
|
|
def calculate_is_angle(poly):
|
|||
|
|
p1, p2, p3, p4 = poly
|
|||
|
|
height = ((p4[1] - p1[1]) + (p3[1] - p2[1])) / 2
|
|||
|
|
if 0.8 * height <= (p3[1] - p1[1]) <= 1.2 * height:
|
|||
|
|
return False
|
|||
|
|
else:
|
|||
|
|
# logger.info((p3[1] - p1[1])/height)
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
def is_bbox_aligned_rect(points):
|
|||
|
|
x_coords = points[:, 0]
|
|||
|
|
y_coords = points[:, 1]
|
|||
|
|
unique_x = np.unique(x_coords)
|
|||
|
|
unique_y = np.unique(y_coords)
|
|||
|
|
return len(unique_x) == 2 and len(unique_y) == 2
|
|||
|
|
|
|||
|
|
def get_rotate_crop_image(img, points):
|
|||
|
|
'''
|
|||
|
|
img_height, img_width = img.shape[0:2]
|
|||
|
|
left = int(np.min(points[:, 0]))
|
|||
|
|
right = int(np.max(points[:, 0]))
|
|||
|
|
top = int(np.min(points[:, 1]))
|
|||
|
|
bottom = int(np.max(points[:, 1]))
|
|||
|
|
img_crop = img[top:bottom, left:right, :].copy()
|
|||
|
|
points[:, 0] = points[:, 0] - left
|
|||
|
|
points[:, 1] = points[:, 1] - top
|
|||
|
|
'''
|
|||
|
|
assert len(points) == 4, "shape of points must be 4*2"
|
|||
|
|
|
|||
|
|
if is_bbox_aligned_rect(points):
|
|||
|
|
xmin = int(np.min(points[:, 0]))
|
|||
|
|
xmax = int(np.max(points[:, 0]))
|
|||
|
|
ymin = int(np.min(points[:, 1]))
|
|||
|
|
ymax = int(np.max(points[:, 1]))
|
|||
|
|
new_img = img[ymin:ymax, xmin:xmax].copy()
|
|||
|
|
if new_img.shape[0] > 0 and new_img.shape[1] > 0:
|
|||
|
|
return new_img
|
|||
|
|
|
|||
|
|
img_crop_width = int(
|
|||
|
|
max(
|
|||
|
|
np.linalg.norm(points[0] - points[1]),
|
|||
|
|
np.linalg.norm(points[2] - points[3])))
|
|||
|
|
img_crop_height = int(
|
|||
|
|
max(
|
|||
|
|
np.linalg.norm(points[0] - points[3]),
|
|||
|
|
np.linalg.norm(points[1] - points[2])))
|
|||
|
|
pts_std = np.float32([[0, 0], [img_crop_width, 0],
|
|||
|
|
[img_crop_width, img_crop_height],
|
|||
|
|
[0, img_crop_height]])
|
|||
|
|
M = cv2.getPerspectiveTransform(points, pts_std)
|
|||
|
|
dst_img = cv2.warpPerspective(
|
|||
|
|
img,
|
|||
|
|
M, (img_crop_width, img_crop_height),
|
|||
|
|
borderMode=cv2.BORDER_REPLICATE,
|
|||
|
|
flags=cv2.INTER_CUBIC)
|
|||
|
|
dst_img_height, dst_img_width = dst_img.shape[0:2]
|
|||
|
|
rotate_radio = 2
|
|||
|
|
if dst_img_height * 1.0 / dst_img_width >= rotate_radio:
|
|||
|
|
dst_img = np.rot90(dst_img)
|
|||
|
|
return dst_img
|