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f1554a2 5700698 f1554a2 b3e6fcf f1554a2 60466f3 f1554a2 b3e6fcf f1554a2 1c42b13 f1554a2 1c42b13 b3e6fcf f1554a2 b3e6fcf f1554a2 8c793f7 f1554a2 1c42b13 f1554a2 b3e6fcf f1554a2 b3e6fcf f1554a2 b3e6fcf f1554a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | from os.path import join as pjoin
import cv2
import os
import shutil
import time
import json
import numpy as np
import CDM.detect_compo.ip_region_proposal as ip
import CDM.detect_classify.classification as clf
import pandas as pd
import openai
# def summarize_segment(segment):
# openai.api_key = os.environ.get('openai_key')
#
# prompt = f"Shorten this paragraph: \"{str(segment)}\"."
#
# response = openai.ChatCompletion.create(
# # engine="text-davinci-002",
# model="gpt-3.5-turbo",
# messages=[
# # {"role": "system", "content": "You are a helpful assistant."},
# {"role": "user", "content": prompt}
# ],
# max_tokens=400,
# n=1,
# stop=None,
# temperature=0,
# )
#
# shortened_segment = response.choices[0].message['content']
#
# return shortened_segment
# model = clf.get_clf_model("ViT")
def resize_height_by_longest_edge(img_path, resize_length=800):
org = cv2.imread(img_path)
height, width = org.shape[:2]
if height > width:
return resize_length
else:
return int(resize_length * (height / width))
def run_single_img(input_img, output_root, segment_root):
# input_img_root = "./input_examples/"
# output_root = "./result_classification"
# segment_root = '../scrutinizing_alexa/txt'
global output_boards
if os.path.exists(output_root):
shutil.rmtree(output_root)
os.makedirs(output_root)
# image_list = os.listdir(input_img_root)
#
# input_imgs = [input_img_root + image_name for image_name in image_list]
key_params = {'min-grad': 4, 'ffl-block': 5, 'min-ele-area': 50, 'merge-contained-ele': True,
'max-word-inline-gap': 10, 'max-line-ingraph-gap': 4, 'remove-top-bar': False}
is_ip = True
is_clf = False
is_ocr = True
is_merge = True
is_classification = True
# # Load deep learning models in advance
# compo_classifier = None
# if is_ip and is_clf:
# compo_classifier = {}
# from cnn.CNN import CNN
# # compo_classifier['Image'] = CNN('Image')
# compo_classifier['Elements'] = CNN('Elements')
# # compo_classifier['Noise'] = CNN('Noise')
# ocr_model = None
if is_ocr:
import CDM.detect_text.text_detection as text
# set the range of target inputs' indices
# num = 0
# start_index = 30800 # 61728
# end_index = 100000
img_time_cost_all = []
ocr_time_cost_all = []
ic_time_cost_all = []
ts_time_cost_all = []
cd_time_cost_all = []
resize_by_height = 800
# for input_img in input_imgs:
output_data = pd.DataFrame(columns=['screenshot', 'id', 'label', 'index', 'text', 'sentences'])
this_img_start_time = time.time()
resized_height = resize_height_by_longest_edge(input_img, resize_by_height)
index = input_img.split('/')[-1][:-4]
# if index != "1-1" and index != "1-2":
# continue
if is_ocr:
os.makedirs(pjoin(output_root, 'ocr'), exist_ok=True)
this_ocr_time_cost = text.text_detection(input_img, output_root, show=False, method='google') # pytesseract
ocr_time_cost_all.append(this_ocr_time_cost)
if is_ip:
os.makedirs(pjoin(output_root, 'ip'), exist_ok=True)
this_cd_time_cost = ip.compo_detection(input_img, output_root, key_params,
resize_by_height=resized_height, show=False)
cd_time_cost_all.append(this_cd_time_cost)
detection_cost = time.time() - this_img_start_time
if is_merge:
import CDM.detect_merge.merge as merge
os.makedirs(pjoin(output_root, 'merge'), exist_ok=True)
compo_path = pjoin(output_root, 'ip', str(index) + '.json')
ocr_path = pjoin(output_root, 'ocr', str(index) + '.json')
board_merge, components_merge = merge.merge(input_img, compo_path, ocr_path, pjoin(output_root, 'merge'),
is_remove_top_bar=key_params['remove-top-bar'], show=False)
# ic_time_cost_all.append(this_ic_time_cost)
# ts_time_cost_all.append(this_ts_time_cost)
if is_classification:
os.makedirs(pjoin(output_root, 'classification'), exist_ok=True)
merge_path = pjoin(output_root, 'merge', str(index) + '.json')
merge_json = json.load(open(merge_path, 'r'))
os.makedirs(pjoin(output_root, 'classification', 'GUI'), exist_ok=True)
this_time_cost_ic, this_time_cost_ts, output_data, output_boards, classification_cost = clf.compo_classification(input_img, output_root,
segment_root, merge_json,
output_data,
resize_by_height=resize_by_height, clf_model="ViT", model = clf.get_clf_model("ViT"))
ic_time_cost_all.append(this_time_cost_ic)
ts_time_cost_all.append(this_time_cost_ts)
this_img_time_cost = time.time() - this_img_start_time
img_time_cost_all.append(this_img_time_cost)
# print("time cost for this image: %2.2f s" % this_img_time_cost)
print("检测+分类共花费： %2.2f s" % (classification_cost + detection_cost))
if os.path.isfile(output_root + '/output.csv'):
output_data.to_csv(output_root + '/output.csv', index=False, mode='a', header=False)
else:
output_data.to_csv(output_root + '/output.csv', index=False, mode='w')
# avg_ocr_time_cost = sum(ocr_time_cost_all) / len(ocr_time_cost_all)
# avg_cd_time_cost = sum(cd_time_cost_all) / len(cd_time_cost_all)
# avg_ic_time_cost = sum(ic_time_cost_all) / len(ic_time_cost_all)
# avg_ts_time_cost = sum(ts_time_cost_all) / len(ts_time_cost_all)
# avg_time_cost = sum(img_time_cost_all) / len(img_time_cost_all)
# print("average text extraction time cost for this app: %2.2f s" % avg_ocr_time_cost)
# print("average widget detection time cost for this app: %2.2f s" % avg_cd_time_cost)
# print("average icon classification time cost for this app: %2.2f s" % avg_ic_time_cost)
# print("average text selection processing time cost for this app: %2.2f s" % avg_ts_time_cost)
# print("average screenshot processing time cost for this app: %2.2f s" % avg_time_cost)
# short_output_data = output_data[['id', 'label', 'text']].copy()
# short_output_data = short_output_data.rename(columns={'text': 'segment'})
# summarize segments:
# original_output_data = short_output_data.copy()
# retries = 3
# for index in range(1, len(short_output_data)):
# seg = short_output_data.loc[index, 'segment']
# for i in range(retries):
# try:
# shortened_seg = summarize_segment(seg)
# break
# except openai.error.RateLimitError as e:
# if "overloaded" in str(e):
# # Exponential backoff with jitter
# sleep_time = 2 * (2 ** i) + 0.1
# time.sleep(sleep_time)
# except Exception as e:
# # If you wish, you can print or log the exception details here without raising it
# print(e)
# else:
# # This part will be executed if the for loop doesn't hit 'break'
# shortened_seg = seg
#
# short_output_data.loc[index, 'segment'] = shortened_seg
# original_output = []
# retries = 3
# summarized_data = [] # List to hold summarized rows
# for index, row in short_output_data.iterrows():
# seg = row['segment']
# for i in range(retries):
# try:
# shortened_seg = summarize_segment(seg)
# break
# except openai.error.RateLimitError as e:
# if "overloaded" in str(e):
#
# sleep_time = 2 * (2 ** i) + 0.1
# # sleep_time = 3
# time.sleep(sleep_time)
# except Exception as e:
# # If you wish, you can print or log the exception details here without raising it
# print(e)
# else:
# # This part will be executed if the for loop doesn't hit 'break'
# shortened_seg = seg
#
# summarized_data.append({'id': row['id'], 'label': row['label'], 'segment': shortened_seg})
# original_output.append({'id': row['id'], 'label': row['label'], 'segment': seg[0].upper() + seg[1:]})
#
# summarized_output_data = pd.DataFrame(summarized_data)
# original_output_data = pd.DataFrame(original_output)
return output_boards
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