add corp.py

corp.py

@@ -0,0 +1,193 @@
+
+# DB/data/processes/random_crop_data.py
+
+import numpy as np
+import math
+import cv2
+import imgaug
+import imgaug.augmenters as iaa
+
+# from .data_process import DataProcess
+# from concern.config import Configurable, State
+
+class State:
+    def __init__(self, autoload=True, default=None):
+        self.autoload = autoload
+        self.default = default
+
+# random crop algorithm similar to https://github.com/argman/EAST
+class RandomCropData():
+    size = (512, 512)
+    max_tries = 50
+    min_crop_side_ratio = 0.1
+    require_original_image = False
+
+    def __init__(self, **kwargs):
+        pass
+
+    def process(self, data):
+        img = data['image']
+        ori_img = img
+        ori_lines = data['polys']
+
+        all_care_polys = [line['points']
+                          for line in data['polys'] if not line['ignore']]
+        crop_x, crop_y, crop_w, crop_h = self.crop_area(img, all_care_polys)
+        scale_w = self.size[0] / crop_w
+        scale_h = self.size[1] / crop_h
+        scale = min(scale_w, scale_h)
+        h = int(crop_h * scale)
+        w = int(crop_w * scale)
+        padimg = np.zeros(
+            (self.size[1], self.size[0], img.shape[2]), img.dtype)
+        padimg[:h, :w] = cv2.resize(
+            img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
+        img = padimg
+
+        lines = []
+        for line in data['polys']:
+            poly = ((np.array(line['points']) -
+                     (crop_x, crop_y)) * scale).tolist()
+            if not self.is_poly_outside_rect(poly, 0, 0, w, h):
+                lines.append({**line, 'points': poly})
+        data['polys'] = lines
+
+        if self.require_original_image:
+            data['image'] = ori_img
+        else:
+            data['image'] = img
+        data['lines'] = ori_lines
+        data['scale_w'] = scale
+        data['scale_h'] = scale
+
+        return data
+
+    def is_poly_in_rect(self, poly, x, y, w, h):
+        poly = np.array(poly)
+        if poly[:, 0].min() < x or poly[:, 0].max() > x + w:
+            return False
+        if poly[:, 1].min() < y or poly[:, 1].max() > y + h:
+            return False
+        return True
+
+    def is_poly_outside_rect(self, poly, x, y, w, h):
+        poly = np.array(poly)
+        if poly[:, 0].max() < x or poly[:, 0].min() > x + w:
+            return True
+        if poly[:, 1].max() < y or poly[:, 1].min() > y + h:
+            return True
+        return False
+
+    def split_regions(self, axis):
+        regions = []
+        min_axis = 0
+        for i in range(1, axis.shape[0]):
+            if axis[i] != axis[i-1] + 1:
+                region = axis[min_axis:i]
+                min_axis = i
+                regions.append(region)
+        return regions
+
+    def random_select(self, axis, max_size):
+        xx = np.random.choice(axis, size=2)
+        xmin = np.min(xx)
+        xmax = np.max(xx)
+        xmin = np.clip(xmin, 0, max_size - 1)
+        xmax = np.clip(xmax, 0, max_size - 1)
+        return xmin, xmax
+
+    def region_wise_random_select(self, regions, max_size):
+        selected_index = list(np.random.choice(len(regions), 2))
+        selected_values = []
+        for index in selected_index:
+            axis = regions[index]
+            xx = int(np.random.choice(axis, size=1))
+            selected_values.append(xx)
+        xmin = min(selected_values)
+        xmax = max(selected_values)
+        return xmin, xmax
+
+    def crop_area(self, img, polys):
+        h, w, _ = img.shape
+        h_array = np.zeros(h, dtype=np.int32)
+        w_array = np.zeros(w, dtype=np.int32)
+        for points in polys:
+            points = np.round(points, decimals=0).astype(np.int32)
+            minx = np.min(points[:, 0])
+            maxx = np.max(points[:, 0])
+            w_array[minx:maxx] = 1
+            miny = np.min(points[:, 1])
+            maxy = np.max(points[:, 1])
+            h_array[miny:maxy] = 1
+        # ensure the cropped area not across a text
+        h_axis = np.where(h_array == 0)[0]
+        w_axis = np.where(w_array == 0)[0]
+
+        if len(h_axis) == 0 or len(w_axis) == 0:
+            return 0, 0, w, h
+
+        h_regions = self.split_regions(h_axis)
+        w_regions = self.split_regions(w_axis)
+
+        for i in range(self.max_tries):
+            if len(w_regions) > 1:
+                xmin, xmax = self.region_wise_random_select(w_regions, w)
+            else:
+                xmin, xmax = self.random_select(w_axis, w)
+            if len(h_regions) > 1:
+                ymin, ymax = self.region_wise_random_select(h_regions, h)
+            else:
+                ymin, ymax = self.random_select(h_axis, h)
+
+            if xmax - xmin < self.min_crop_side_ratio * w or ymax - ymin < self.min_crop_side_ratio * h:
+                # area too small
+                continue
+            num_poly_in_rect = 0
+            for poly in polys:
+                if not self.is_poly_outside_rect(poly, xmin, ymin, xmax - xmin, ymax - ymin):
+                    num_poly_in_rect += 1
+                    break
+
+            if num_poly_in_rect > 0:
+                return xmin, ymin, xmax - xmin, ymax - ymin
+
+        return 0, 0, w, h
+
+
+if __name__ == "__main__":
+    
+    im = './datasets/icdar2015/train_images/img_1.jpg'
+    gt = './datasets/icdar2015/train_gts/gt_img_1.txt'
+    
+    items = []
+    reader = open(gt, 'r').readlines()
+    for line in reader:
+        item = {}
+        parts = line.strip().split(',')
+        label = parts[-1]
+        if 'TD' in gt and label == '1':
+            label = '###'
+        line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in parts]
+        if 'icdar' in gt:
+            poly = np.array(list(map(float, line[:8]))).reshape(
+                (-1, 2)).tolist()
+        else:
+            num_points = math.floor((len(line) - 1) / 2) * 2
+            poly = np.array(list(map(float, line[:num_points]))).reshape(
+                (-1, 2)).tolist()
+        item['points'] = poly  # 多边形是用一个个的点表示的，起点连接第二个点，第二个连接第三个 ... 最后一点连接起点，构成一个闭合的区域
+        item['text'] = label
+        item['ignore'] = True if label == '###' else False  # 此标记表示文字模糊不可辨认，文本框的标记是不可靠的
+        items.append( item )
+
+    img = cv2.imdecode(np.fromfile(im, dtype=np.uint8), -1)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_shape = img.shape
+
+    data = dict(
+        image = img,
+        polys = items,
+    )
+
+    crop = RandomCropData()
+    crop.process(data)