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test_pipe_type_cloud.py
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test_pipe_type_cloud.py
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#!/usr/bin/env python
import os
import json
import torch
import pprint
import argparse
import matplotlib
matplotlib.use("Agg")
import cv2
from tqdm import tqdm
from config import system_configs
from nnet.py_factory import NetworkFactory
from db.datasets import datasets
import importlib
import json
from RuleGroup.Cls import GroupCls
from RuleGroup.Bar import GroupBarRaw
from RuleGroup.LineQuiry import GroupQuiryRaw
from RuleGroup.LIneMatch import GroupLineRaw
from RuleGroup.Pie import GroupPie
import math
from PIL import Image, ImageDraw, ImageFont
torch.backends.cudnn.benchmark = False
import requests
import time
import re
def make_dirs(directories):
for directory in directories:
if not os.path.exists(directory):
os.makedirs(directory)
def load_net(testiter, cfg_name, data_dir, cache_dir, cuda_id=0):
cfg_file = os.path.join(system_configs.config_dir, cfg_name + ".json")
with open(cfg_file, "r") as f:
configs = json.load(f)
configs["system"]["snapshot_name"] = cfg_name
configs["system"]["data_dir"] = data_dir
configs["system"]["cache_dir"] = cache_dir
configs["system"]["result_dir"] = 'result_dir'
configs["system"]["tar_data_dir"] = "Cls"
system_configs.update_config(configs["system"])
train_split = system_configs.train_split
val_split = system_configs.val_split
test_split = system_configs.test_split
split = {
"training": train_split,
"validation": val_split,
"testing": test_split
}["validation"]
test_iter = system_configs.max_iter if testiter is None else testiter
print("loading parameters at iteration: {}".format(test_iter))
dataset = system_configs.dataset
db = datasets[dataset](configs["db"], split)
print("building neural network...")
nnet = NetworkFactory(db)
print("loading parameters...")
nnet.load_params(test_iter)
if torch.cuda.is_available():
nnet.cuda(cuda_id)
nnet.eval_mode()
return db, nnet
def Pre_load_nets(type, id_cuda, data_dir, cache_dir):
methods = {}
if type == "Bar":
db_bar, nnet_bar = load_net(50000, "CornerNetPureBar", data_dir, cache_dir,
id_cuda)
path = 'testfile.test_%s' % "CornerNetPureBar"
testing_bar = importlib.import_module(path).testing
methods['Bar'] = [db_bar, nnet_bar, testing_bar]
if type == "Pie":
db_pie, nnet_pie = load_net(50000, "CornerNetPurePie", data_dir, cache_dir,
id_cuda)
path = 'testfile.test_%s' % "CornerNetPurePie"
testing_pie = importlib.import_module(path).testing
methods['Pie'] = [db_pie, nnet_pie, testing_pie]
if type == "Line":
db_line, nnet_line = load_net(50000, "CornerNetLine", data_dir, cache_dir,
id_cuda)
path = 'testfile.test_%s' % "CornerNetLine"
testing_line = importlib.import_module(path).testing
methods['Line'] = [db_line, nnet_line, testing_line]
db_line_cls, nnet_line_cls = load_net(20000, "CornerNetLineClsReal", data_dir, cache_dir,
id_cuda)
path = 'testfile.test_%s' % "CornerNetLineCls"
testing_line_cls = importlib.import_module(path).testing
methods['LineCls'] = [db_line_cls, nnet_line_cls, testing_line_cls]
return methods
def ocr_result(image_path):
subscription_key = "ad143190288d40b79483aa0d5c532724"
vision_base_url = "https://westus2.api.cognitive.microsoft.com/vision/v2.0/"
ocr_url = vision_base_url + "read/core/asyncBatchAnalyze"
headers = {'Ocp-Apim-Subscription-Key': subscription_key, 'Content-Type': 'application/octet-stream'}
params = {'language': 'unk', 'detectOrientation': 'true'}
image_data = open(image_path, "rb").read()
response = requests.post(ocr_url, headers=headers, params=params, data=image_data)
response.raise_for_status()
op_location = response.headers['Operation-Location']
analysis = {}
while "recognitionResults" not in analysis.keys():
time.sleep(3)
binary_content = requests.get(op_location, headers=headers, params=params).content
analysis = json.loads(binary_content.decode('ascii'))
line_infos = [region["lines"] for region in analysis["recognitionResults"]]
word_infos = []
for line in line_infos:
for word_metadata in line:
for word_info in word_metadata["words"]:
word_infos.append(word_info)
return word_infos
def check_intersection(box1, box2):
if (box1[2] - box1[0]) + ((box2[2] - box2[0])) > max(box2[2], box1[2]) - min(box2[0], box1[0]) \
and (box1[3] - box1[1]) + ((box2[3] - box2[1])) > max(box2[3], box1[3]) - min(box2[1], box1[1]):
Xc1 = max(box1[0], box2[0])
Yc1 = max(box1[1], box2[1])
Xc2 = min(box1[2], box2[2])
Yc2 = min(box1[3], box2[3])
intersection_area = (Xc2-Xc1)*(Yc2-Yc1)
return intersection_area/((box2[3]-box2[1])*(box2[2]-box2[0]))
else:
return 0
def try_math(image_path, cls_info):
title_list = [1, 2, 3]
title2string = {}
max_value = 1
min_value = 0
max_y = 0
min_y = 1
word_infos = ocr_result(image_path)
for id in title_list:
if id in cls_info.keys():
predicted_box = cls_info[id]
words = []
for word_info in word_infos:
word_bbox = [word_info["boundingBox"][0], word_info["boundingBox"][1], word_info["boundingBox"][4], word_info["boundingBox"][5]]
if check_intersection(predicted_box, word_bbox) > 0.5:
words.append([word_info["text"], word_bbox[0], word_bbox[1]])
words.sort(key=lambda x: x[1]+10*x[2])
word_string = ""
for word in words:
word_string = word_string + word[0] + ' '
title2string[id] = word_string
if 5 in cls_info.keys():
plot_area = cls_info[5]
y_max = plot_area[1]
y_min = plot_area[3]
x_board = plot_area[0]
dis_max = 10000000000000000
dis_min = 10000000000000000
for word_info in word_infos:
word_bbox = [word_info["boundingBox"][0], word_info["boundingBox"][1], word_info["boundingBox"][4], word_info["boundingBox"][5]]
word_text = word_info["text"]
word_text = re.sub('[^-+0123456789.]', '', word_text)
word_text_num = re.sub('[^0123456789]', '', word_text)
word_text_pure = re.sub('[^0123456789.]', '', word_text)
if len(word_text_num) > 0 and word_bbox[2] <= x_board+4:
dis2max = math.sqrt(math.pow((word_bbox[0]+word_bbox[2])/2-x_board, 2)+math.pow((word_bbox[1]+word_bbox[3])/2-y_max, 2))
dis2min = math.sqrt(math.pow((word_bbox[0] + word_bbox[2]) / 2 - x_board, 2) + math.pow(
(word_bbox[1] + word_bbox[3]) / 2 - y_min, 2))
y_mid = (word_bbox[1]+word_bbox[3])/2
if dis2max <= dis_max:
dis_max = dis2max
max_y = y_mid
max_value = float(word_text_pure)
if word_text[0] == '-':
max_value = -max_value
if dis2min <= dis_min:
dis_min = dis2min
min_y = y_mid
min_value = float(word_text_pure)
if word_text[0] == '-':
min_value = -min_value
delta_min_max = max_value-min_value
delta_mark = min_y - max_y
delta_plot_y = y_min - y_max
delta = delta_min_max/delta_mark
if abs(min_y-y_min)/delta_plot_y > 0.1:
print(abs(min_y-y_min)/delta_plot_y)
print("Predict the lower bar")
min_value = int(min_value + (min_y-y_min)*delta)
return title2string, round(min_value, 2), round(max_value, 2)
def test(image_path, data_type=0, debug=False, suffix=None, min_value_official=None, max_value_official=None):
image_cls = Image.open(image_path)
image = cv2.imread(image_path)
with torch.no_grad():
#results = methods['Cls'][2](image, methods['Cls'][0], methods['Cls'][1], debug=False)
#info = results[0]
#tls = results[1]
#brs = results[2]
#plot_area = []
#image_painted, cls_info = GroupCls(image_cls, tls, brs)
#title2string, min_value, max_value = try_math(image_path, cls_info)
if data_type == 0:
print("Predicted as BarChart")
results = methods['Bar'][2](image, methods['Bar'][0], methods['Bar'][1], debug=False)
tls = results[0]
brs = results[1]
bar_data = GroupBarRaw(image, tls, brs)
return bar_data
if data_type == 2:
print("Predicted as PieChart")
results = methods['Pie'][2](image, methods['Pie'][0], methods['Pie'][1], debug=False)
cens = results[0]
keys = results[1]
pie_data = GroupPie(image, cens, keys)
return pie_data
if data_type== 1:
print("Predicted as LineChart")
results = methods['Line'][2](image, methods['Line'][0], methods['Line'][1], debug=False, cuda_id=1)
keys = results[0]
hybrids = results[1]
image_painted, quiry, keys, hybrids = GroupQuiryRaw(image, keys, hybrids)
results = methods['LineCls'][2](image, methods['LineCls'][0], quiry, methods['LineCls'][1], debug=False, cuda_id=1)
line_data = GroupLineRaw(image_painted, keys, hybrids, results)
return line_data
def parse_args():
parser = argparse.ArgumentParser(description="Test DeepRule")
parser.add_argument("--image_path", dest="image_path", help="test images", default="test", type=str)
parser.add_argument("--save_path", dest="save_path", help="where to save results", default="save", type=str)
parser.add_argument("--type", dest="type", help="type of images", default="Bar", type=str)
parser.add_argument("--data_dir", dest="data_dir", default="data/linedata(1028)", type=str)
parser.add_argument('--cache_path', dest="cache_path", type=str)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
methods = Pre_load_nets(args.type, 0, args.data_dir, args.cache_path)
target_dir = args.result_path
tar_path = args.image_path
save_path = args.save_path
rs_dict = {}
images = os.listdir(tar_path)
for image in tqdm(images):
path = os.path.join(tar_path, image)
data = test(path)
rs_dict[image] = data
with open(save_path, "w") as f:
json.dump(rs_dict, f)