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FacePP.py
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FacePP.py
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# -*- coding: utf-8 -*-
import urllib.request
import urllib.error
import time
import json
import glob
import os
from skimage import io
import numpy as np
import dlib
import face_recognition
import socket
from PIL import Image, ImageFont, ImageDraw, ImageColor
from multiprocessing import Pool
import multiprocessing
import pandas as pd
import networkx as nx
import math
import json
import pickle
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import matplotlib.patches as patches
import networkx as nx
import pandas as pd
import csv
import Twts_download as twts_dl
import Twts_img_download as img_dl
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
http_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect'
key = "g06BZoz_VFEJxvzaLPTlQI-ZsbhCbRbU"
secret = "EHoHJiGyJn-IoeHNBMGNlUFBKhoLmyci"
filepath = r"C:\Temp\1106767324818755585.jpg"
#image_url =r'https://pbs.twimg.com/ext_tw_video_thumb/1110391114991714305/pu/img/VN5DdIA3_jYhvxOw.jpg'
all_files = glob.glob(r'E:\Tweets\NoGEO\t2\*.jpg')
all_files = glob.glob(r'E:\Tweets\NoGEO\test\Images\20190327\*.jpg')
# https://pbs.twimg.com/ext_tw_video_thumb/1110391114991714305/pu/img/VN5DdIA3_jYhvxOw.jpg;
#minimum_size = 1600 # pixel besides profile
minimum_area = 1600 # pixel besides profile
minimum_dimenssion = 400 # pixel besides profile
def face2csv(filepath, face):
face_list = []
file_name = os.path.basename(filepath).replace('.jpg', '')
image = Image.open(filepath)
img_w = image.size[0]
img_h = image.size[1]
img_pixels = image.size[0] * image.size[1]
del image
try:
attributes = face['attributes']
except Exception as e:
print('Error in getting face attributes: ', str(e), face)
return None
try:
tid = file_name.split('-')[0]
face_list.append(tid)
try:
img_index = file_name.split('-')[1]
except:
img_index = 1
face_list.append(img_index)
#
face_token = face['face_token']
face_list.append(face_token)
ethnicity = face['attributes']['ethnicity']['value']
face_list.append(ethnicity)
gender = face['attributes']['gender']['value']
face_list.append(gender)
age = face['attributes']['age']['value']
face_list.append(age)
glass = face['attributes']['glass']['value']
face_list.append(glass)
smile = face['attributes']['smile']['value']
face_list.append(smile)
# e:emotion
e_sadness = face['attributes']['emotion']['sadness']
e_neutral = face['attributes']['emotion']['neutral']
e_disgust = face['attributes']['emotion']['disgust']
e_anger = face['attributes']['emotion']['anger']
e_surprise = face['attributes']['emotion']['surprise']
e_fear = face['attributes']['emotion']['fear']
e_happiness = face['attributes']['emotion']['happiness']
face_list.append(e_sadness)
face_list.append(e_neutral)
face_list.append(e_disgust)
face_list.append(e_anger)
face_list.append(e_surprise)
face_list.append(e_fear)
face_list.append(e_happiness)
# b: beauty
b_female = face['attributes']['beauty']['female_score']
b_male = face['attributes']['beauty']['male_score']
face_list.append(b_female)
face_list.append(b_male)
# m: mouthstatus
m_close = face['attributes']['mouthstatus']['close']
m_mask = face['attributes']['mouthstatus']['surgical_mask_or_respirator']
m_open = face['attributes']['mouthstatus']['open']
m_other = face['attributes']['mouthstatus']['other_occlusion']
face_list.append(m_close)
face_list.append(m_mask)
face_list.append(m_open)
face_list.append(m_other)
# s: skinstatus
s_dark = face['attributes']['skinstatus']['dark_circle']
s_stain = face['attributes']['skinstatus']['stain']
s_acne = face['attributes']['skinstatus']['acne']
s_health = face['attributes']['skinstatus']['health']
face_list.append(s_dark)
face_list.append(s_stain)
face_list.append(s_acne)
face_list.append(s_health)
# h: headpose
h_yaw = face['attributes']['headpose']['yaw_angle']
h_pitch = face['attributes']['headpose']['pitch_angle']
h_roll = face['attributes']['headpose']['roll_angle']
face_list.append(h_yaw)
face_list.append(h_pitch)
face_list.append(h_roll)
face_quality = face['attributes']['facequality']['value']
face_list.append(face_quality)
# r: face rectangle
r_width = face['face_rectangle']['width']
r_top = face['face_rectangle']['top']
r_left = face['face_rectangle']['left']
r_height = face['face_rectangle']['height']
face_list.append(r_width)
face_list.append(r_top)
face_list.append(r_left)
face_list.append(r_height)
# now it is area (pixel), will be changed outside this function.
area_pixel = r_width * r_height
face_list.append(area_pixel)
try:
eyegaze = face['eyegaze']
print('Face detection, eyegaze: ', eyegaze)
except:
pass
try:
blur = face['attributes']['blur']['blurness']['value']
#print('Face detection, blur: ', blur)
except:
blur = 0
pass
face_list.append(blur)
portion = round(area_pixel / img_pixels, 3)
face_list.append(portion)
except Exception as e:
print('Face detection result error：', str(e))
print('')
return None
face_str = ''
for item in face_list:
face_str = face_str + str(item) + ','
#print('face_str: ', face_str[:-1])
return face_str[:-1]
def getFace(filepath, isDrawDox=True):
isDrawBox = isDrawDox
image = Image.open(filepath)
# if image.size[0] < minimum_size or image.size[1] < minimum_size:
# print("Face size less than {} size. ".format(minimum_area))
# return None
boundary = '----------%s' % hex(int(time.time() * 1000))
data = []
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'api_key')
data.append(key)
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'api_secret')
data.append(secret)
data.append('--%s' % boundary)
fr = open(filepath, 'rb')
data.append('Content-Disposition: form-data; name="%s"; filename=" "' % 'image_file')
data.append('Content-Type: %s\r\n' % 'application/octet-stream')
data.append(fr.read())
fr.close()
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'return_landmark')
data.append('1')
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'return_attributes')
data.append("gender,smiling,age,emotion,ethnicity,beauty,mouthstatus,skinstatus")
data.append('--%s--\r\n' % boundary)
for i, d in enumerate(data):
if isinstance(d, str):
data[i] = d.encode('utf-8')
http_body = b'\r\n'.join(data)
# build http request
req = urllib.request.Request(url=http_url, data=http_body)
# header
req.add_header('Content-Type', 'multipart/form-data; boundary=%s' % boundary)
data.clear()
try:
# post data to server
resp = urllib.request.urlopen(req, timeout=20)
# get response
qrcont = resp.read()
# if you want to load as json, you should decode first,
# for example: json.loads(qrount.decode('utf-8'))
faces = json.loads(qrcont.decode('utf-8'))['faces']
#print('len of faces before faces[:5]: ', len(faces))
faces = faces[:5] # Free API only return 5 face.
#print('len of faces: ', len(faces), filepath)
face_csv = ''
if len(faces) == 0:
return None
#print('faces:', faces)
# if len(faces) == 0:
# return None
# #print('faces:', faces)
else:
i = 0
if isDrawBox:
font = ImageFont.truetype(font=r'D:\YOLO\keras-yolo3-master\font\FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
draw = ImageDraw.Draw(image)
image1 = face_recognition.load_image_file(filepath)
face_locations = face_recognition.face_locations(image1)
for face in faces:
i += 1
# if i > 4:
# #print('face index: ', i)
# continue
result = face2csv(filepath, face)
if result is not None:
face_csv = face_csv + result + '\n'
#print(face_list)
# print(face['attributes']['ethnicity']['value'], face['attributes']['gender']['value'], face['attributes']['age']['value'])
# print(face['face_rectangle'])
if isDrawBox:
#for i, c in reversed(list(enumerate(out_classes))):
ethnicity = face['attributes']['ethnicity']['value']
gender = face['attributes']['gender']['value']
age = face['attributes']['age']['value']
label = '{} {} {}'.format(ethnicity, gender, age)
#draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
#
#top, left, bottom, right = box
top = max(0, face['face_rectangle']['top'])
left = max(0, face['face_rectangle']['left'])
bottom = face['face_rectangle']['top'] + face['face_rectangle']['height']
right = face['face_rectangle']['left'] + face['face_rectangle']['width']
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
color = getColor(ethnicity)
# My kingdom for a good redistributable image drawing library.
# draw rectangle for the face_recognization lib
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=color)
# if df.ix[i, 'top'] - label_size[1] >= 0:
# text_origin = np.array([df.ix[i, 'left'], df.ix[i, 'top'] - label_size[1]])
# else:
# text_origin = np.array([df.ix[i, 'left'], df.ix[i, 'top'] + 1])
#
# # My kingdom for a good redistributable image drawing library.
# #print(label, (left, top), (right, bottom))
# for i in range(thickness):
# draw.rectangle(
# [df.ix[i, 'left'] + i, df.ix[i, 'top'] + i, df.ix[i, 'right'] - i, df.ix[i, 'bottom'] - i],
# outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=color)
if gender == 'Female':
color = (255,20,147)
else:
color = (0,191,255)
draw.text(text_origin, label, fill=color, font=font)
saved_path = os.path.join(os.path.dirname(filepath), 'Face')
#isDrawBox = False
# for face in face_locations:
#
# if isDrawBox:
#
# top = max(0, face[0])
# left = max(0, face[3])
# bottom = face[2]
# right = face[1]
# for i in range(thickness):
# draw.rectangle(
# [left + i, top + i, right - i, bottom - i],
# outline=(255, 0, 0))
if isDrawBox:
if not os.path.exists(saved_path):
os.mkdir(saved_path)
saved_filepath = os.path.join(saved_path, os.path.basename(filepath))
image.save(saved_filepath)
del draw
return face_csv
except urllib.error.HTTPError as e:
print(e.read().decode('utf-8'))
except socket.timeout as e:
print('Face detection Error in: ', type(e)) #catched
def getColor(ethnicity):
#print('ethnicity in getColor():', ethnicity)
ethnicity_color ={
'WHITE': (255,255,255),
'BLACK': (204,204,0),
'ASIAN': (255,228,181),
'INDIA': (210,105,30)}
try:
color = ethnicity_color[ethnicity]
except:
color = '(255,0,0)'
return color
def getFacefrom_list(file_list):
csv_header = r'tid,img_index,face_token,ethnicity,gender,age,glass,smile,e_sadness,e_neutral,e_disgust,e_anger,e_surprise,e_fear,e_happiness,b_female,b_male,m_close,m_mask,m_open,m_other,s_dark,s_stain,s_acne,s_health,h_yaw,h_pitch,h_roll,face_quality,r_width,r_top,r_left,r_height,area_pixel,blurness,portion'
csv_header = csv_header + '\n'
all_files = file_list
for filepath in all_files:
#print(filepath)
try:
result = getFace(filepath)
if result is not None:
csv_header = csv_header + result
except Exception as e:
print('Face detection error in getFacefrom_list(): ', str(e), filepath)
print('')
continue
return csv_header
def EqualSplit(length, n): # return a list of [start_index, end_index] . Has bug hare.
n = math.ceil(n)
if n > length:
print("Error in EqualSplit(): Length is shorter than n! ")
return
if n == 0:
n = 1
part_len = length / n
# length = (length)
start = []
# lst.append(0)
for i in range(n):
start.append(int(round(part_len * i, 0)))
# lst.append(len(df) - 1)
# start_ends = []
#print(start)
return start
def local_face_recog_1_process(all_files, results):
# results: dictionary
#print(all_files)
#PID = os.getpid()
#time0 = time.time()
for idx, filepath in enumerate(all_files):
try:
img = io.imread(filepath)
shape = img.shape
if shape[0] < minimum_dimenssion or shape[1] < minimum_dimenssion:
if "Profile" not in filepath:
continue
# print(filepath)
image = face_recognition.load_image_file(filepath)
img_name = os.path.basename(filepath)
face_location0 = face_recognition.face_locations(image)
face_locations = []
for location in face_location0:
top, right, bottom, left = location
area = (bottom - top) * (right - left)
if area > minimum_area:
face_locations.append(location)
elif "Profile" in filepath:
face_locations.append(location)
#encodings = face_recognition.face_encodings(image, face_locations)
#face_locations = face_recognition.face_locations(image, model='cnn')
face_cnt = len(face_locations)
if face_cnt > 0:
encodings = face_recognition.face_encodings(image, face_locations)
results[filepath] = {'locations': face_locations, 'encodings': encodings}
finished = "{} / {}".format(idx, len(all_files))
#msg = f"PID {PID:6d} found {face_cnt:2d} face(s) in: {img_name:>22}, has finished {finished}."
#print(msg)
#face_locations_cnn = face_recognition.face_locations(image, model='cnn')
#encodings_cnn = face_recognition.face_encodings(image, face_locations_cnn)
# face_recognition.compare_faces()
# i = 0
# for encoding in encodings:
# #print(encoding.tolist())
# #print(i)
# #print(encodings_cnn[0].tolist())
# #results[filepath] = encoding
# #print('Compared result: ', face_recognition.compare_faces([encoding], encodings_cnn[0]))
# i += 1
# print(encoding.size)
except Exception as e:
print('Error in local_face_recog_1_process() ', str(e), filepath)
continue
#print("results:", results)
#time1 = time.time()
return results
def getEncoding_mp(list_photos, Process_Cnt):
starts = EqualSplit(len(all_files), Process_Cnt)
i = 0
pools = Pool(processes=Process_Cnt)
results = multiprocessing.Manager().dict()
frames = []
for i in range(Process_Cnt):
if i < Process_Cnt-1:
pools.apply_async(local_face_recog_1_process, args=(list_photos[starts[i]:starts[i+1]], results))
else:
pools.apply_async(local_face_recog_1_process, args=(list_photos[starts[i]:], results))
pools.close()
pools.join()
pools.terminate()
print('len of results:', len(results))
#print(results)
return results
def encoding_face_folder(img_path=None, Process_Cnt=8):
all_files = glob.glob(os.path.join(img_path, r'*.jpg'))
save_file = os.path.join(img_path, os.path.basename(img_path) + '.fcs')
results = {}
time0 = time.time()
if len(all_files) < 1:
print("No photos in: {}", img_path)
with open(save_file, 'wb') as f:
f.write(pickle.dumps(results.copy()))
return
#msg = f'Found {}'
#print("Found {} images in folder {}.".format(str(len(all_files)), img_path))
#Process_Cnt = 8
Process_Cnt = min(Process_Cnt, len(all_files))
save_file = os.path.join(img_path, os.path.basename(img_path) + '.fcs')
#print("Result will be stored in: ", save_file)
#results = getEncoding_mp(all_files, Process_Cnt)
starts = EqualSplit(len(all_files), Process_Cnt)
if Process_Cnt == 1:
local_face_recog_1_process(all_files, results)
else:
results = multiprocessing.Manager().dict()
pools = Pool(processes=Process_Cnt)
for i in range(Process_Cnt):
if i < Process_Cnt-1:
pools.apply_async(local_face_recog_1_process, args=(all_files[starts[i]:starts[i+1]], results))
else:
pools.apply_async(local_face_recog_1_process, args=(all_files[starts[i]:], results))
pools.close()
pools.join()
pools.terminate()
time1 = time.time()
print('PID:{} used {:0.1f} second to find {} faces in folder: {}, stored in {}'.format(os.getpid(), time1 - time0, len(results), img_path, save_file))
with open(save_file, 'wb') as f:
f.write(pickle.dumps(results.copy()))
def face_compare(start, end, faces, results, threshold=0.6):
#print(faces.keys())
try:
images = list(faces.keys())
images.sort(reverse=False)
#print(images[start:end])
tasks = images[start:end]
#print(start, end)
task_cnt = end - start
for i in range(start, end): # compare photos
#print(faces[images[i]]['encodings'])
# print("PID: {:6d} is processing {} / {}: {} .".format(os.getpid(), i - start + 1, task_cnt, images[i]))
for j in range(i + 1, len(images)):
img1_name = os.path.basename(images[i]).split('.')[0]
img2_name = os.path.basename(images[j]).split('.')[0]
# img1 = mpimg.imread(images[i])
# img2 = mpimg.imread(images[j])
# plt.close('all')
# plt.figure(figsize=(15, 10)) # 设置窗口大小
#
# ax1 = plt.subplot(1, 2, 1)
# ax1.imshow(img1)
#
# plt.title(str(i) + ' ' + img1_name)
#
# ax2 = plt.subplot(1, 2, 2)
# ax2.imshow(img2)
#
# plt.title(str(j) + ' ' + img2_name)
img2_matched_cnt = 0
encoding1_idx = 0
for encoding1, location1 in zip(faces[images[i]]['encodings'], faces[images[i]]['locations']):
encoding2_idx = 0
for encoding2, location2 in zip(faces[images[j]]['encodings'], faces[images[j]]['locations']):
cmp = (face_recognition.compare_faces([encoding1], encoding2, threshold))
#threshold:",threshold)
if cmp[0]:
img2_matched_cnt = img2_matched_cnt + 1
#print("PID: ", os.getpid(), 'found', img1_name, 'has', img2_matched_cnt, 'faces in', img2_name)
# top, right, bottom, left = location1
# rect = patches.Rectangle((left, top), right - left, bottom - top, linewidth=1, edgecolor='r',
# facecolor='none')
# ax1.add_patch(rect)
# top, right, bottom, left = location2
# rect = patches.Rectangle((left, top), right - left, bottom - top, linewidth=1, edgecolor='r',
# facecolor='none')
# ax2.add_patch(rect)
results.append((str(img1_name), str(encoding1_idx), str(img2_name), str((encoding2_idx))))
encoding2_idx += 1
encoding1_idx += 1
# if img2_matched_cnt > 0:
# #pass
# # if img2_matched_cnt > 1:
# # plt.title(str(i) + " " + str(j))
# #plt.show()
# plt.show(block=False)
# plt.pause(3)
# plt.close('all')
except Exception as e:
print("Error in face_compare: ", str(e))
return results
def faces_compare(faces_file, Process_Cnt=8, threshold=0.35):
#print(faces_file)
results = []
faces = pickle.loads(open(faces_file, 'rb').read())
images = list(faces.keys())
if len(images) == 0:
print('No images in: {}'.format(faces_file))
return results
#print(images)
Process_Cnt = min(Process_Cnt, len(images))
starts = EqualSplit(len(images), Process_Cnt)
#print(starts)
if Process_Cnt == 1:
face_compare(faces, )
else:
results = multiprocessing.Manager().list()
pools = Pool(processes=Process_Cnt)
for i in range(Process_Cnt):
if i < Process_Cnt-1:
pools.apply_async(face_compare, args=(starts[i], starts[i + 1], faces, results, threshold))
else:
pools.apply_async(face_compare, args=(starts[i], len(images), faces, results, threshold))
pools.close()
pools.join()
pools.terminate()
#print('len of matches:', len(results))
return results
def face_compare_single_process(user_folders, threshold=0.6):
if not isinstance(user_folders, list):
user_folders = [user_folders]
try:
for idx, img_path in enumerate(user_folders):
try:
#img_path = os.path.join(root_folder, img_path)
time0 = time.time()
#print("PID: {} is processing faces in folder: # {} {}".format(os.getpid(), idx, img_path))
faces_path = os.path.join(img_path, os.path.basename(img_path) + '.fcs')
mth_path = os.path.join(img_path, os.path.basename(img_path) + '.mth')
# if not os.path.exists(faces_path):
# continue
all_files = glob.glob(os.path.join(img_path, '*.jpg'))
faces = {}
if len(all_files) < 1:
print("No photos in: {}", img_path)
with open(faces_path, 'wb') as f:
f.write(pickle.dumps(faces.copy()))
continue
time1 = time.time()
faces = local_face_recog_1_process(all_files, faces)
time2 = time.time()
print("PID: {} used {:0.0f} seconds to find faces.".format(os.getpid(), time2 - time1))
if not faces:
print("No face in: ", img_path)
continue
with open(faces_path, 'wb') as f:
f.write(pickle.dumps(faces.copy()))
images = list(faces.keys())
matches = []
matches = face_compare(0, len(images), faces, matches, threshold)
with open(mth_path, 'w') as w:
for line in matches:
# print(','.join(line))
w.writelines(','.join(line) + '\n')
face_attr = getUserFace_From_Matches(mth_path)
time3 = time.time()
print("PID: {} used {:0.0f} second to find user's face.".format(os.getpid(), time3 - time2))
print(face_attr)
except Exception as ee:
print('Error in face_compare_single_process() loop: ', str(ee))
continue
except Exception as e:
print("Error in face_compare_single_process: ", str(e))
def getArea(location):
top, right, bottom, left = location
w = right - left
h = bottom - top
return w * h
def getUserFace_From_Matches(mth_path):
faces_path = mth_path[:-3] + 'fcs'
userid = os.path.basename(mth_path).replace('.mth', '')
faces = pickle.loads(open(faces_path, 'rb').read())
df = pd.read_csv(mth_path, names=('img1', 'idx1', 'img2', 'idx2'))
profile_path = os.path.join(os.path.dirname(faces_path), 'Profile_' + userid + '.jpg')
img_path = profile_path
best = img_path
#print('img_path: ', img_path)
profile_face = faces.get(profile_path, '')
#print("Profile_face: ", profile_face)
if len(df) < 1 and faces.get(profile_path, True):
print("No match in ", mth_path)
if profile_face == '':
print('No face in the profile: ', mth_path)
return
df['node1'] = df['img1'].astype(str) + '_' + df['idx1'].astype(str)
df['node2'] = df['img2'].astype(str) + '_' + df['idx2'].astype(str)
#print(len(df))
if len(df) > 0:
G = nx.Graph()
for idx, row in df.iterrows():
G.add_edge(row['node1'], row['node2'])
cc = nx.algorithms.connected_components(G)
lens = [len(cc) for cc in sorted(nx.connected_components(G), key=len, reverse=True)]
largest_cc = max(nx.connected_components(G), key=len)
cc = sorted(nx.connected_components(G), key=len, reverse=True)
degrees = dict(G.degree())
c = cc[0] # keep the largest one..
# print("cc: ", cc)
folder = os.path.dirname(mth_path)
list_c = list(c)
#print('c: ', c)
c_degrees = [degrees[x] for x in list_c]
indecies = list(map(lambda node: int(node.split('_')[-1]), list_c))
images = list(map(lambda node: os.path.join(folder, '_'.join(node.split('_')[0:-1]) + '.jpg'), list_c))
locs = list(map(lambda img_path, idx: faces[img_path]['locations'][idx], images, indecies))
c_areas = list(map(getArea, locs))
largest_area_idx = c_areas.index(max(c_areas))
c_locations = [faces[x]['locations'] for x in images]
max_degree_idx = c_degrees.index(max(c_degrees))
best = list_c[max_degree_idx]
largest = list_c[largest_area_idx]
img_path = os.path.join(folder, best.split('_')[0] + '.jpg')
if "Profile_" in best:
# print(node)
# print('_'.join(node.split('_')[:-1]))
img_path = os.path.join(folder, best + '.jpg')
#print('img_path: ', img_path)
locations = faces[img_path]['locations']
try:
face_idx = int(best.split('_')[-1])
except: # the profile
c_areas = list(map(getArea, locations))
largest_area_idx = c_areas.index(max(c_areas))
face_idx = largest_area_idx
top, right, bottom, left = locations[face_idx]
w = right - left
h = bottom - top
r = 0.4
img = mpimg.imread(img_path)
shape = img.shape
ex_top = int(max(0, top - h * r))
ex_bottom = int(min(shape[0], bottom + h * r))
ex_right = int(min(shape[1], right + w * r))
ex_left = int(max(0, left - w * r))
cropped = img[ex_top:ex_bottom, ex_left:ex_right]
cropped_file = os.path.join(os.path.dirname(img_path), userid + '_face.png')
#print('img_path:', img_path)
#print('Cropped user face file: ', cropped_file)
mpimg.imsave(cropped_file, cropped)
face_attr = getFacefrom_list([cropped_file])
#print(face_attr)
face_attr_file = os.path.join(os.path.dirname(img_path), userid + '_face.csv')
with open(face_attr_file, 'w') as f:
f.writelines(face_attr)
return face_attr
# img_path = os.path.join(folder, largest.split('_')[0] + '.jpg')
# if largest.split('_')[0] + '.jpg' == "Profile.jpg":
# # print(node)
# # print('_'.join(node.split('_')[:-1]))
# img_path = os.path.join(folder, '_'.join(largest.split('_')[:-1]) + '.jpg')
#
# locations = faces[img_path]['locations']
# face_idx = int(largest.split('_')[-1])
#
# top, right, bottom, left = locations[face_idx]
# w = right - left
# h = bottom - top
# rect = patches.Rectangle((left, top), right - left, bottom - top, linewidth=1, edgecolor='r', facecolor='none')
#
#
# for idx, node in enumerate(c):
# img_path = os.path.join(folder, node.split('_')[0] + '.jpg')
# if node.split('_')[0] + '.jpg' == "Profile.jpg":
# # print(node)
# # print('_'.join(node.split('_')[:-1]))
# img_path = os.path.join(folder, '_'.join(node.split('_')[:-1]) + '.jpg')
#
# locations = faces[img_path]['locations']
# face_idx = int(node.split('_')[-1])
#
#
# top, right, bottom, left = locations[face_idx]
# w = right - left
# h = bottom - top
# r = 0.2
#
# shape = img.shape
#
# ex_top = int(max(0, top - h * r))
# ex_bottom = int(min(shape[0], bottom + h * r))
# ex_right = int(min(shape[1], right + w * r))
# ex_left = int(max(0, left - w * r))
#
# cropped = img[ex_top:ex_bottom, ex_left:ex_right]
def getUserFaceAttrFromIds(ids, face_attrs, saved_path):
# face_attrs: a shared list
# ids: a list or a single user id
try:
start = time.time()
if not isinstance(ids, list):
ids = [ids]
twts_dl.auth_tweepy_api()
print('len(ids): ', len(ids))
for idx, id in enumerate(ids):
try:
time0 = time.time()
username = ''
try:
user = twts_dl.tweepy_api.get_user(id) # will consume rate
username = user.screen_name
except Exception as eee:
print("Error in get user name: ", id, str(eee))
pass
# twts_csv = twts_dl.getUsersTwts([(username, id)])
# df_tweets = pd.read_csv(StringIO(twts_csv), quoting=csv.QUOTE_NONE, dtype={'tweetID': str},
# delimiter=',', error_bad_lines=False, low_memory=False)
# if len(df_tweets) == 0:
# continue
# username = df_tweets.iloc[0]['username']
user_folder = os.path.join(saved_path, id + "_" + username)
img_dl.getUserImages(username, id, saved_path)
#encoding_face_folder(user_folder, Process_Cnt=1)
#faces_path = os.path.join(user_folder, id + "_" + username + '.fcs')
face_compare_single_process(user_folder, threshold=0.45)
# print('faces_path: ', faces_path) # good
#results = faces_compare(faces_path, Process_Cnt=1, threshold=0.45)
# with open(mth_path, 'w') as w:
# for line in results:
# #print(','.join(line))
# w.writelines(','.join(line) + '\n')
# if not os.path.exists(folder):
# os.mkdir(folder)
#
# csv_path = os.path.join(saved_path, id + "_" + username, id + "_" + username + '.csv')
# with open(csv_path, 'w', encoding='utf-8') as f:
# f.writelines(twts_csv)
#print('username: ', results)
#print(len(df_tweets))
time1 = time.time()
time_cost = timedelta(seconds=int(time.time() - start))
print(
"PID: {} used {:0.1f} seconds in total to finish user {}, id:{}. Processed {} users in {}\n".format(os.getpid(), time1 - time0, username, id, idx + 1, time_cost))
except Exception as ee:
print("Error in process user id: ", id, str(ee))
continue
except Exception as e:
print("Error in getUserFaceAttrFromIds():", str(e))
#print("PID: {} used {:0.1f} seconds in total to finish {} folders.".format(os.getpid(), time1 - time0, ))
return face_attrs
def getUserFaceAttrFromFile(ids_csv, Process_Cnt=1, p_offset=0): # input the ids list csv file, p_offset is the offset of each process, not the file.
df = pd.read_csv(ids_csv, dtype=str, names=['userid'], header=None)
#print(df[:10])
user_ids = df['userid'].tolist()
saved_path = os.path.dirname(ids_csv)
starts = EqualSplit(len(df), Process_Cnt)
pools = Pool(processes=Process_Cnt)
face_attrs = multiprocessing.Manager().list()
for i in range(Process_Cnt):
try:
if i < Process_Cnt - 1:
# print(i, i + 1)
# print(starts[i], starts[i + 1])
inputs = user_ids[starts[i] + p_offset:starts[i + 1]]
pools.apply_async(getUserFaceAttrFromIds, args=(inputs, face_attrs, saved_path))
else:
inputs = user_ids[starts[i] + p_offset:]
pools.apply_async(getUserFaceAttrFromIds, args=(inputs, face_attrs, saved_path))
#print(inputs)
except Exception as e:
print("Error in pools.apply_async: ", str(e))
pools.close()
pools.join()
pools.terminate()
print("Finished! ")
if __name__ == '__main__':
getUserFaceAttrFromFile(r'F:\Tweets\User20190420_images\userID20190420.csv', Process_Cnt=10)
#all_files = glob.glob(r'F:\Tweets\images\Flood_users\profiles\*.png')
# getUserFace_From_Matches(r'F:\Tweets\images\user2018\203613767_Eric_Lee_822\203613767_Eric_Lee_822.mth')
#
# img_path = r'F:\Tweets\images\users\3127881897_lalalahannahh'
# root_folder = r'F:\Tweets\images\user2018'
# threshold = 0.45
# user_folders = os.listdir(root_folder)
# user_folders = [os.path.join(root_folder, folder) for folder in user_folders]
# #print(user_folders)
# #user_folders = user_folders[200:]
# user_folders = user_folders[590:]
# print(len(user_folders))
# #user_folders = [r'F:\Tweets\images\users\17983161_tweedledeedumm1']
#
# Process_Cnt = 5
# starts = EqualSplit(len(user_folders), Process_Cnt)
# print(starts)
# pools = Pool(processes=Process_Cnt)
# results = multiprocessing.Manager().list()
# for i in range(Process_Cnt):
# if i < Process_Cnt-1:
# pools.apply_async(face_compare_single_process, args=(user_folders[starts[i]:starts[i + 1]], results, threshold))
# else:
# pools.apply_async(face_compare_single_process, args=(user_folders[starts[i]:len(user_folders)], results, threshold))
# pools.close()
# pools.join()
# pools.terminate()
##########################################
##############################
#
# for idx, img_path in enumerate(user_folders):
# img_path = os.path.join(root_folder, img_path)
# print("Processing folder: #", idx, img_path)
# faces_path = os.path.join(img_path, os.path.basename(img_path) + '.fcs')
# mth_path = os.path.join(img_path, os.path.basename(img_path) + '.mth')
# # if not os.path.exists(faces_path):
# # continue
# encoding_face_folder(img_path, 8)
# results = faces_compare(faces_path, 8, threshold)
# with open(mth_path, 'w') as w:
# for line in results:
# #print(','.join(line))
# w.writelines(','.join(line) + '\n')
#
# ####################################################################################
# faces = pickle.loads(open(faces_path, 'rb').read())
# results = pd.read_csv(mth_path)
# df = pd.read_csv(mth_path, names=('img1', 'idx1', 'img2', 'idx2'))
# df['node1'] = df['img1'].astype(str) + '_' + df['idx1'].astype(str)
# df['node2'] = df['img2'].astype(str) + '_' + df['idx2'].astype(str)
# G = nx.Graph()
# for idx, row in df.iterrows():
# G.add_edge(row['node1'], row['node2'])
# cc = nx.algorithms.connected_components(G)
# lens = [len(cc) for cc in sorted(nx.connected_components(G), key=len, reverse=True)]
# largest_cc = max(nx.connected_components(G), key=len)
# cc = sorted(nx.connected_components(G), key=len, reverse=True)
#
# for c in cc:
# col = math.ceil(math.sqrt(len(c)))
# row = math.ceil(len(c)/col)
# folder = os.path.dirname(mth_path)
# fig, ax = plt.subplots(nrows=row, ncols=col, squeeze=True)
# plt.figure(figsize=(15, 10)) # 设置窗口大小
#
# for idx, node in enumerate(c):
# ax = plt.subplot(row, col, idx + 1)
# img_path = os.path.join(folder, node.split('_')[0] + '.jpg')