robgon-art

import cv2
amount = 1.0
img = Image.open("input_image.png")
image_np = np.array(img, dtype = np.float32)
gaussian = cv2.GaussianBlur(image_np, (0, 0), 1.0)
unsharp_image = cv2.addWeighted(image_np, 2.0, gaussian, -1.0, 0)
unsharp_image = amount * unsharp_image + (1.0 - amount) * image_np
image_int = unsharp_image.round().clip(0, 255).astype(np.uint8)
image_pil = Image.fromarray(image_int)
image_pil.save("output_image.png")

robgon-art

import math
import numpy as np
from PIL import Image

img = Image.open("input_image.png")
image_np = np.array(img, dtype = np.float32)
dark_r = np.quantile(image_np[:,:,0:1], 0.1)
dark_g = np.quantile(image_np[:,:,1:2], 0.1)
dark_b = np.quantile(image_np[:,:,2:3], 0.1)

robgon-art

import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

response = openai.Completion.create(
  engine="davinci-instruct-beta-v3",
  prompt="Create prompts to render fictional people at different ages and nationalities.\n\n1. A watercolor painting",
  temperature=0.7,
  max_tokens=64,

robgon-art

import cv2
import numpy as np

def inpaint(input_file_path, output_path):
  !rm -r /content/input
  !mkdir /content/input
  !cp $input_file_path /content/input
  parts = input_file_path.split("/")
  mask_name = "/content/input/" + parts[-1]
  mask_name = mask_name[:-4]+ "_mask.png"

robgon-art

# This is a modified version of the find_faces code in DLIB, by Davis King
# The original version is here:
#  https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py

import dlib
from PIL import Image

def find_faces(face_file_path):
  # Load all the models we need: a detector to find the faces, a shape predictor
  # to find face landmarks so we can precisely localize the face

robgon-art

def rotate_points(x, y, x_axis_index, y_axis_index):
  num_points = len(x)
  x_rotated = []
  y_rotated = []
  x_axis_angle = math.atan2(y[x_axis_index], x[x_axis_index])
  y_axis_angle = math.atan2(y[y_axis_index], x[y_axis_index])
  if math.sin(y_axis_angle - x_axis_angle) < 0:
    flip_y = -1.0
  else:
    flip_y = 1.0

robgon-art

!python stylegan2/dataset_tool.py create_from_images 'datasets/paintings' 'paintings/'

!python stylegan2/run_training.py --config=config-e --metrics=none \
   --data-dir='/content/datasets' --dataset=paintings \
   --mirror-augment=true \
   --total-kimg=5000 \
   --result-dir='/content/drive/My Drive/results'

robgon-art

import random
index = random.randint(0, len(styles)-1)
style = styles[index].capitalize()
prompt = style + " painting"
r = random.random()
if r < 1.0/3.0:
  series = "shapes"
  index = random.randint(0, len(shapes)-1)
  subject = " with " + shapes[index]
  prompt += subject

robgon-art

for i, img in enumerate(all_images):
  pil_image = PIL.Image.fromarray(img)
  pil_image.save("/content/output/output_" + str(i) + ".png")

robgon-art

def get_warning_level(content_to_classify):
  content_filter_results = openai.Completion.create(
        engine="content-filter-alpha-c4",
        prompt = "<|endoftext|>"+content_to_classify+"\n--\nLabel:",
        temperature=0,
        max_tokens=1,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        logprobs=10)