import glob

import os

import sys

import cv2

import matplotlib.pyplot as plt

import numpy as np

from skimage import io

from tqdm import tqdm

import features

import tinder_api

# Get tokens

host = "https://api.gotinder.com" # thanks to this line you do not need to import config.py or tinder_config_ex.py

fb_access_token = tinder_api.config.fb_access_token

fb_user_id = tinder_api.config.fb_user_id

tinder_api.get_auth_token(fb_access_token, fb_user_id)

# Create user directory

user_dir = "data/" + features.config.fb_username.split("@")[0] + "/"

os.system("rm -rf " + user_dir + " && mkdir " + user_dir)

print("\nCreated User Directory: " + user_dir)

# Get your profile photos

plotFlag = False

print("\nYour profile photos:")

myself = tinder_api.get_self()

for index, p in enumerate(myself["photos"]):

print(p["url"])

if plotFlag:

img = io.imread(p["url"])[:, :, ::-1]

plt.figure(figsize=(12, 12)) if index == 0 else None

plt.subplot(3, 3, index + 1).imshow(img[:, :, ::-1])

plt.axis("off")

# Get your matches

match_info = features.get_match_info()

# Download all match images

download_match_images = True

if download_match_images:

print("\nDownloading match images...")

match_dir = user_dir + "match_images/"

yolo_dir = user_dir + "match_images_yolo/"

crop_dir = user_dir + "match_images_crop/"

os.makedirs(match_dir)

os.makedirs(crop_dir)

n = 5 # len(match_info)

for i in tqdm(range(n)):

name = match_info[i]["name"]

photos = match_info[i]["photos"]

for j, photo in enumerate(photos):

label = name + "_m" + str(i) + "_" + str(j)

os.system("wget " + photo + " -q -O " + match_dir + label + ".jpg")

# Pass images through yolov3

print("\nAnalyzing match images with YOLOv3...")

sys.path.append("../yolov3")

import detect as detect

detect.opt.conf_thres = 0.60

detect.opt.image_folder = sys.path[0] + "/" + match_dir

detect.opt.output_folder = sys.path[0] + "/" + yolo_dir

detect.opt.txt_out = True

detect.main(detect.opt)

# Remove images of none or multiple people

txt_files = sorted(glob.glob("%s/*.txt" % yolo_dir))

for txt_file in txt_files:

labels = np.loadtxt(txt_file, dtype=np.float32).reshape(-1, 6)

labels = labels[labels[:, 4] == 0]

if labels.shape[0] == 1: # if only 1 person in image

img_name = txt_file[:-4].split("/")[-1]

box = labels[0].astype("int")

h, w = box[3] - box[1], box[2] - box[0]

area = w * h

if (w > 150) and (h > 150) and (area > 30e3):

img = cv2.imread(match_dir + img_name)

cv2.imwrite(crop_dir + img_name, img[box[1] : box[3], box[0] : box[2]])

exit()

# Get Tinder Recommendations of people around you

# recommendations = tinder_api.get_recommendations()

recommendations = tinder_api.get_recs_v2()

# select one recommended individual

testid = recommendations["data"]["results"][0]["user"]["_id"]

print(testid)

# Retrieve profile from id

testperson = tinder_api.get_person(testid)

testperson

# Like a user

# tinder_api.like('5a10ae3c8802dc4401463712')

# message a match

tinder_api.send_msg("59ff7c30117d37c0572338d55a10ae3c8802dc4401463712", "Hi, boy! Gloria Tinder-Robot here")