text stringlengths 1 93.6k |
|---|
img_size = proc_parm['img_size'][0]
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# undo_scale = np.array(proc_parm['scale'])#1. / np.array(proc_parm['scale'])
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undo_scale = img_size / constants.IMG_RES
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cam_pos = cam[1:]
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principal_pt = np.array([img_size, img_size]) / 2.
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flength = constants.FOCAL_LENGTH
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# tz = flength / (0.5 * constants.IMG_RES * cam_s)
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tz = (2 * flength) / (constants.IMG_RES * cam[0] + 1e-9)
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trans = np.hstack([cam_pos, tz])
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vert_shifted = (verts + trans)
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start_pt = proc_parm['start_pt']
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final_principal_pt = principal_pt+ start_pt
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cam_for_render = np.hstack(
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[np.mean(flength * undo_scale), final_principal_pt[1], final_principal_pt[0]])
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# This is in padded image.
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# kp_original = (joints + proc_param['start_pt']) * undo_scale
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# Subtract padding from joints.
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# import ipdb; ipdb.set_trace()
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# kp_original = (joints + proc_parm['start_pt']) * undo_scale
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kp_original = (joints) * undo_scale + proc_parm['start_pt'][::-1]- (proc_parm['pad_x'][0],proc_parm['pad_y'][0])
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return cam_for_render, vert_shifted, kp_original
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# def get_original(proc_parm, verts, cam, joints):
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#
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# img_size = np.array(224)
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# undo_scale = 1.
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# print('und scale',undo_scale)
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# # undo_scale = img_size / constants.IMG_RES
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#
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#
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# cam_pos = cam[1:]
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# principal_pt = np.array([img_size, img_size]) / 2.
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# flength = 500
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# # tz = flength / (0.5 * constants.IMG_RES * cam_s)
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# tz = (2 * flength) / (constants.IMG_RES * cam[ 0] + 1e-9)
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# trans = np.hstack([cam_pos, tz])
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# vert_shifted = (verts + trans)
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# print('img size', img_size)
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# start_pt = proc_parm['start_pt']
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# print('start pt', proc_parm['start_pt'] )
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# print('principal pt', principal_pt)
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# final_principal_pt = (principal_pt + start_pt)*undo_scale
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# cam_for_render = np.hstack(
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# [np.mean(flength * undo_scale), 1280/2,720/2])
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#
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# # This is in padded image.
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# # kp_original = (joints + proc_param['start_pt']) * undo_scale
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# # Subtract padding from joints.
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# # import ipdb; ipdb.set_trace()
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# # kp_original = (joints + proc_parm['start_pt']) * undo_scale
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#
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# kp_original = (joints ) * undo_scale + proc_parm['start_pt'][::-1] - (proc_parm['pad_x'][0],proc_parm['pad_y'][0])
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#
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# return cam_for_render, vert_shifted, kp_original
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# <FILESEP>
|
import argparse
|
import datetime
|
import pandas as pd
|
from collections import defaultdict
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import re
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from pathlib import Path
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GENERAL_WA_MULTI_SEARCH_PATTERN = r'\d?\d\/\d?\d\/\d?\d?\d\d, \d\d:\d\d:?\d?\d?\s?-? (.*?):(.*)'
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LINE_SPLIT_DELIMITER = "\n"
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def text_to_dictionary(text, prompt, response):
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"""
|
We convert a whatsapp chat into a prompt and
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response dataframe for the purposes of finetuning.
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:param text:
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:param prompt:
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:param response:
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:return:
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"""
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# convert from bytes to string
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if isinstance(text, (bytes, bytearray)):
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text = text.decode()
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text_list = text.split('\n')[1:]
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result_dict, count, prev_author = defaultdict(dict), 0, ''
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for ix, line in enumerate(text_list):
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search_pattern = re.search(GENERAL_WA_MULTI_SEARCH_PATTERN, line)
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if search_pattern is not None:
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author = search_pattern.group(1)
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message = search_pattern.group(2).replace('"','')
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else:
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continue
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if author == prompt:
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if author == prev_author:
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prev = result_dict[count]['prompt'][:-7]
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result_dict[count]['prompt'] = f"{prev}. {message}\n\n###\n\n"
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else:
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count += 1
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result_dict[count].update({'prompt': message + "\n\n###\n\n"})
|
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