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PaddleOCR
提交 dbaab10e 编写于 作者: L LDOUBLEV
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add end2end

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tools/end2end/convert_ppocr_label.py 0 → 100644
浏览文件 @ dbaab10e
import numpy as np
import json
import os
def poly_to_string(poly):
if len(poly.shape) > 1:
poly = np.array(poly).flatten()
string = "\t".join(str(i) for i in poly)
return string
def convert_label(label_dir, mode="gt", save_dir="./save_results/"):
if not os.path.exists(label_dir):
raise ValueError(f"The file {label_dir} does not exist!")
assert label_dir != save_dir, "hahahhaha"
label_file = open(label_dir, 'r')
data = label_file.readlines()
gt_dict = {}
for line in data:
try:
tmp = line.split('\t')
assert len(tmp) == 2, ""
except:
tmp = line.strip().split(' ')
gt_lists = []
if tmp[0].split('/')[0] is not None:
img_path = tmp[0]
anno = json.loads(tmp[1])
gt_collect = []
for dic in anno:
#txt = dic['transcription'].replace(' ', '') # ignore blank
txt = dic['transcription']
if 'score' in dic and float(dic['score']) < 0.5:
continue
if u'\u3000' in txt: txt = txt.replace(u'\u3000', u' ')
#while ' ' in txt:
# txt = txt.replace(' ', '')
poly = np.array(dic['points']).flatten()
if txt == "###":
txt_tag = 1 ## ignore 1
else:
txt_tag = 0
if mode == "gt":
gt_label = poly_to_string(poly) + "\t" + str(
txt_tag) + "\t" + txt + "\n"
else:
gt_label = poly_to_string(poly) + "\t" + txt + "\n"
gt_lists.append(gt_label)
gt_dict[img_path] = gt_lists
else:
continue
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for img_name in gt_dict.keys():
save_name = img_name.split("/")[-1]
save_file = os.path.join(save_dir, save_name + ".txt")
with open(save_file, "w") as f:
f.writelines(gt_dict[img_name])
print("The convert label saved in {}".format(save_dir))
if __name__ == "__main__":
ppocr_label_gt = "/paddle/Datasets/chinese/test_set/Label_refine_310_V2.txt"
convert_label(ppocr_label_gt, "gt", "./save_gt_310_V2/")
ppocr_label_gt = "./infer_results/ch_PPOCRV2_infer.txt"
convert_label(ppocr_label_gt_en, "pred", "./save_PPOCRV2_infer/")
tools/end2end/draw_html.py 0 → 100644
浏览文件 @ dbaab10e
import os
def draw_debug_img(html_path):
err_cnt = 0
with open(html_path, 'w') as html:
html.write('<html>\n<body>\n')
html.write('<table border="1">\n')
html.write(
"<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />"
)
image_list = []
path = "./det_results/310_gt/"
#path = "infer_results/"
for i, filename in enumerate(sorted(os.listdir(path))):
if filename.endswith("txt"): continue
print(filename)
# The image path
base = "{}/{}".format(path, filename)
base_2 = "../PaddleOCR/det_results/ch_PPOCRV2_infer/{}".format(
filename)
base_3 = "../PaddleOCR/det_results/ch_ppocr_mobile_infer/{}".format(
filename)
if True:
html.write("<tr>\n")
html.write(f'<td> {filename}\n GT')
html.write('<td>GT\n<img src="%s" width=640></td>' % (base))
html.write('<td>PPOCRV2\n<img src="%s" width=640></td>' %
(base_2))
html.write('<td>ppocr_mobile\n<img src="%s" width=640></td>' %
(base_3))
html.write("</tr>\n")
html.write('<style>\n')
html.write('span {\n')
html.write(' color: red;\n')
html.write('}\n')
html.write('</style>\n')
html.write('</table>\n')
html.write('</html>\n</body>\n')
print("ok")
#print("all cnt: {}, err cnt: {}, acc: {}".format(len(imgs), err_cnt, 1.0 * (len(imgs) - err_cnt) / len(imgs)))
return
if __name__ == "__main__":
html_path = "sys_visual_iou_310.html"
draw_debug_img()
tools/end2end/eval_end2end.py 0 → 100644
浏览文件 @ dbaab10e
#!/usr/bin/env python
import os
import re
import sys
# import Polygon
import shapely
from shapely.geometry import Polygon
import numpy as np
from collections import defaultdict
import operator
import editdistance
# reload(sys)
# sys.setdefaultencoding('utf-8')
def strQ2B(ustring):
rstring = ""
for uchar in ustring:
inside_code = ord(uchar)
if inside_code == 12288:
inside_code = 32
elif (inside_code >= 65281 and inside_code <= 65374):
inside_code -= 65248
rstring += chr(inside_code)
return rstring
def polygon_from_str(polygon_points):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = np.array(polygon_points).reshape(4, 2)
polygon = Polygon(polygon_points).convex_hull
return polygon
def polygon_iou(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(
poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / union_area
except shapely.geos.TopologicalError:
# except Exception as e:
# print(e)
print('shapely.geos.TopologicalError occured, iou set to 0')
iou = 0
return iou
def ed(str1, str2):
return editdistance.eval(str1, str2)
def e2e_eval(gt_dir, res_dir):
print('start testing...')
iou_thresh = 0.5
val_names = os.listdir(gt_dir)
num_gt_chars = 0
gt_count = 0
dt_count = 0
hit = 0
ed_sum = 0
for i, val_name in enumerate(val_names):
with open(os.path.join(gt_dir, val_name), encoding='utf-8') as f:
gt_lines = [o.strip() for o in f.readlines()]
gts = []
ignore_masks = []
for line in gt_lines:
parts = line.strip().split('\t')
# ignore illegal data
if len(parts) < 9:
continue
assert (len(parts) < 11)
if len(parts) == 9:
gts.append(parts[:8] + [''])
else:
gts.append(parts[:8] + [parts[-1]])
ignore_masks.append(parts[8])
val_path = os.path.join(res_dir, val_name)
if not os.path.exists(val_path):
dt_lines = []
else:
with open(val_path, encoding='utf-8') as f:
dt_lines = [o.strip() for o in f.readlines()]
dts = []
for line in dt_lines:
# print(line)
parts = line.strip().split("\t")
assert (len(parts) < 10), "line error: {}".format(line)
if len(parts) == 8:
dts.append(parts + [''])
else:
dts.append(parts)
dt_match = [False] * len(dts)
gt_match = [False] * len(gts)
all_ious = defaultdict(tuple)
for index_gt, gt in enumerate(gts):
gt_coors = [float(gt_coor) for gt_coor in gt[0:8]]
gt_poly = polygon_from_str(gt_coors)
for index_dt, dt in enumerate(dts):
dt_coors = [float(dt_coor) for dt_coor in dt[0:8]]
dt_poly = polygon_from_str(dt_coors)
iou = polygon_iou(dt_poly, gt_poly)
if iou >= iou_thresh:
all_ious[(index_gt, index_dt)] = iou
sorted_ious = sorted(
all_ious.items(), key=operator.itemgetter(1), reverse=True)
sorted_gt_dt_pairs = [item[0] for item in sorted_ious]
# matched gt and dt
for gt_dt_pair in sorted_gt_dt_pairs:
index_gt, index_dt = gt_dt_pair
if gt_match[index_gt] == False and dt_match[index_dt] == False:
gt_match[index_gt] = True
dt_match[index_dt] = True
# gt_str = strQ2B(gts[index_gt][8]).replace(" ", "")
# dt_str = strQ2B(dts[index_dt][8]).replace(" ", "")
gt_str = strQ2B(gts[index_gt][8])
dt_str = strQ2B(dts[index_dt][8])
if ignore_masks[index_gt] == '0':
ed_sum += ed(gt_str, dt_str)
num_gt_chars += len(gt_str)
if gt_str == dt_str:
hit += 1
gt_count += 1
dt_count += 1
# unmatched dt
for tindex, dt_match_flag in enumerate(dt_match):
if dt_match_flag == False:
dt_str = dts[tindex][8]
gt_str = ''
ed_sum += ed(dt_str, gt_str)
dt_count += 1
# unmatched gt
for tindex, gt_match_flag in enumerate(gt_match):
if gt_match_flag == False and ignore_masks[tindex] == '0':
dt_str = ''
gt_str = gts[tindex][8]
ed_sum += ed(gt_str, dt_str)
num_gt_chars += len(gt_str)
gt_count += 1
eps = 1e-9
print('hit, dt_count, gt_count', hit, dt_count, gt_count)
precision = hit / (dt_count + eps)
recall = hit / (gt_count + eps)
fmeasure = 2.0 * precision * recall / (precision + recall + eps)
avg_edit_dist_img = ed_sum / len(val_names)
avg_edit_dist_field = ed_sum / (gt_count + eps)
character_acc = 1 - ed_sum / (num_gt_chars + eps)
print('character_acc: %.2f' % (character_acc * 100) + "%")
print('avg_edit_dist_field: %.2f' % (avg_edit_dist_field))
print('avg_edit_dist_img: %.2f' % (avg_edit_dist_img))
print('precision: %.2f' % (precision * 100) + "%")
print('recall: %.2f' % (recall * 100) + "%")
print('fmeasure: %.2f' % (fmeasure * 100) + "%")
if __name__ == '__main__':
# if len(sys.argv) != 3:
# print("python3 ocr_e2e_eval.py gt_dir res_dir")
# exit(-1)
# gt_folder = sys.argv[1]
# pred_folder = sys.argv[2]
gt_folder = sys.argv[1]
pred_folder = sys.argv[2]
e2e_eval(gt_folder, pred_folder)
tools/end2end/readme.md 0 → 100644
浏览文件 @ dbaab10e
# 简介
`tools/end2end`目录下存放了文本检测+文本识别pipeline串联预测的指标评测代码以及可视化工具。本节介绍文本检测+文本识别的端对端指标评估方式。
## 端对端评测步骤
**步骤一：**
运行`tools/infer/predict_system.py`，得到保存的结果:
```
python3 tools/infer/predict_system.py --det_model_dir=./ch_PP-OCRv2_det_infer/ --rec_model_dir=./ch_PP-OCRv2_rec_infer/ --image_dir=./datasets/img_dir/ --draw_img_save_dir=./ch_PP-OCRv2_results/ --is_visualize=True
```
文本检测识别可视化图默认保存在`./ch_PP-OCRv2_results/`目录下，预测结果默认保存在`./ch_PP-OCRv2_results/results.txt`中，格式如下：
```
all-sum-510/00224225.jpg [{"transcription": "超赞", "points": [[8.0, 48.0], [157.0, 44.0], [159.0, 115.0], [10.0, 119.0]], "score": "0.99396634"}, {"transcription": "中", "points": [[202.0, 152.0], [230.0, 152.0], [230.0, 163.0], [202.0, 163.0]], "score": "0.09310734"}, {"transcription": "58.0m", "points": [[196.0, 192.0], [444.0, 192.0], [444.0, 240.0], [196.0, 240.0]], "score": "0.44041982"}, {"transcription": "汽配", "points": [[55.0, 263.0], [95.0, 263.0], [95.0, 281.0], [55.0, 281.0]], "score": "0.9986651"}, {"transcription": "成总店", "points": [[120.0, 262.0], [176.0, 262.0], [176.0, 283.0], [120.0, 283.0]], "score": "0.9929402"}, {"transcription": "K", "points": [[237.0, 286.0], [311.0, 286.0], [311.0, 345.0], [237.0, 345.0]], "score": "0.6074794"}, {"transcription": "88：-8", "points": [[203.0, 405.0], [477.0, 414.0], [475.0, 459.0], [201.0, 450.0]], "score": "0.7106863"}]
```
**步骤二：**
将步骤一保存的数据转换为端对端评测需要的数据格式：
修改 `tools/convert_ppocr_label.py`中的代码，convert_label函数中设置输入标签路径，Mode，保存标签路径等，对预测数据的GTlabel和预测结果的label格式进行转换。
```
ppocr_label_gt = "gt_label.txt"
convert_label(ppocr_label_gt, "gt", "./save_gt_label/")
ppocr_label_gt = "./infer_results/ch_PPOCRV2_infer.txt"
convert_label(ppocr_label_gt_en, "pred", "./save_PPOCRV2_infer/")
```
运行`convert_ppocr_label.py`:
```
python3 tools/convert_ppocr_label.py
```
得到如下结果：
```
├── ./save_gt_label/
├── ./save_PPOCRV2_infer/
```
**步骤三：**
执行端对端评测，运行`tools/eval_end2end.py`计算端对端指标，运行方式如下：
```
python3 tools/eval_end2end.py "gt_label_dir" "predict_label_dir"
```
比如：
```
python3 tools/eval_end2end.py ./save_gt_label/ ./save_PPOCRV2_infer/
```
将得到如下结果，fmeasure为主要关注的指标：
```
hit, dt_count, gt_count 1557 2693 3283
character_acc: 61.77%
avg_edit_dist_field: 3.08
avg_edit_dist_img: 51.82
precision: 57.82%
recall: 47.43%
fmeasure: 52.11%
```
tools/infer/predict_system.py
浏览文件 @ dbaab10e
......@@ -27,6 +27,7 @@ import numpy as np
import time
import logging
from PIL import Image
import json
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
......@@ -121,11 +122,31 @@ def sorted_boxes(dt_boxes):
return _boxes
def save_results_to_txt(results, path):
if os.path.isdir(path):
if not os.path.exists(path):
os.makedirs(path)
with open(os.path.join(path, "results.txt"), 'w') as f:
f.writelines(results)
f.close()
logger.info("The results will be saved in {}".format(
os.path.join(path, "results.txt")))
else:
draw_img_save = os.path.dirname(path)
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
with open(path, 'w') as f:
f.writelines(results)
f.close()
logger.info("The results will be saved in {}".format(path))
def main(args):
image_file_list = get_image_file_list(args.image_dir)
image_file_list = image_file_list[args.process_id::args.total_process_num]
text_sys = TextSystem(args)
is_visualize = True
is_visualize = args.is_visualize
font_path = args.vis_font_path
drop_score = args.drop_score
......@@ -139,6 +160,7 @@ def main(args):
cpu_mem, gpu_mem, gpu_util = 0, 0, 0
_st = time.time()
count = 0
save_res = []
for idx, image_file in enumerate(image_file_list):
img, flag = check_and_read_gif(image_file)
......@@ -152,6 +174,21 @@ def main(args):
elapse = time.time() - starttime
total_time += elapse
# save results
preds = []
dt_num = len(dt_boxes)
for dno in range(dt_num):
text, score = rec_res[dno]
if score >= drop_score:
preds.append({
"transcription": text,
"points": np.array(dt_boxes[dno]).tolist()
})
text_str = "%s, %.3f" % (text, score)
save_res.append(image_file + '\t' + json.dumps(
preds, ensure_ascii=False) + '\n')
# print predicted results
logger.debug(
str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse))
for text, score in rec_res:
......@@ -180,6 +217,9 @@ def main(args):
logger.debug("The visualized image saved in {}".format(
os.path.join(draw_img_save_dir, os.path.basename(image_file))))
# The predicted results will be saved in os.path.join(os.draw_img_save_dir, "results.txt")
save_results_to_txt(save_res, args.draw_img_save_dir)
logger.info("The predict total time is {}".format(time.time() - _st))
if args.benchmark:
text_sys.text_detector.autolog.report()
......
tools/infer/utility.py
浏览文件 @ dbaab10e
......@@ -114,6 +114,7 @@ def init_args():
#
parser.add_argument(
"--draw_img_save_dir", type=str, default="./inference_results")
parser.add_argument("--is_visualize", type=str2bool, default=True)
parser.add_argument("--save_crop_res", type=str2bool, default=False)
parser.add_argument("--crop_res_save_dir", type=str, default="./output")
......
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