import streamlit as st
import cv2
import numpy as np
from PIL import Image
import base64

color_step = st.slider('color_step', value=10, min_value=1, max_value=179, step=1)

#duration of each frame of the gif in milliseconds 
duration_parameter = st.slider('duration_parameter aka duration of each frame of the gif in milliseconds', value=10, min_value=1, max_value=2000, step=10)

#Loop parameter = number of times gif loops. 0 = loops infinitely. 
loop_parameter = st.slider('Loop parameter aka number of times gif loops', value=0, min_value=0, max_value=10, step=1)


if color_step == 0:
  my_hue_list = [0]
else:
  my_hue_list = list( range(0, 180, color_step) ) #Color step basically gives step range of this list, ie if color_step = 2 then it is [0,2,4,6,....,178]



user_image_object = st.file_uploader("upload your image", type=['png', 'jpg'], accept_multiple_files=False)

if user_image_object is not None:
  st.image(user_image_object )
  
  
  user_image_name = "input_image.png"
  
  #re-encode for streamlit interface
  #streamlit uploader encodes as a pillow img so we want to save to open in cv2 (converting directly is a pain)
  input_image = Image.open( user_image_object )
  input_image.save(user_image_name )

  # load image with alpha channel
  img = cv2.imread( user_image_name , cv2.IMREAD_UNCHANGED)
  
  # extract alpha channel  
  #alpha = img[:,:,3]
  
  # extract bgr channels
  bgr = img[:,:,0:3]
  
  # convert to HSV
  hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
  #h = hsv[:,:,0]
  #s = hsv[:,:,1]
  #v = hsv[:,:,2]
  h,s,v = cv2.split(hsv)
  
  
  if color_step == 0:
    my_hue_list = [0]
  else:
    my_hue_list = list( range(0, 180, color_step) ) #Color step basically gives step range of this list, ie if color_step = 2 then it is [0,2,4,6,....,178]
  #180 at end means highest it can go is 179 (same as hue param )
  #including 0 makes original image part of the outputs/gif 
  
  #H,S,V = Hue , Saturation, Value (ie color value) parameters
  #Hue has range [0,179] , Saturation [0,255] , Value [0,255]
  
  img_array = []
  output_filename_array = []
  for i in my_hue_list:
    # modify hue channel by adding difference and modulo 180 (modulo because hue parameter only goes up to index 180, shouldn't exceed that )
    hnew = np.mod(h + i, 180).astype(np.uint8)   #<<<<<<<<<<<<<<<< where the iter comes in 
  
    # recombine channels
    hsv_new = cv2.merge([hnew,s,v])
  
    # convert back to bgr
    bgr_new = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)
    
    img_array.append(bgr_new )
  
    # put alpha back into bgr_new
    #bgra = cv2.cvtColor(bgr_new, cv2.COLOR_BGR2BGRA)
    #bgra[:,:,3] = alpha
  
    # save output AS FILE LABELED BY ITERABLE 
    output_filename = 'output_bgr_new_' + str(i) +'.png'        #<<<<<<<<<<<<<<<< where the iter comes in 
    output_filename_array.append(output_filename)
    cv2.imwrite(output_filename, bgr_new)
  
  
  height, width, layers = bgr_new.shape
  size = (width,height)
  
  st.write("len(img_array) = ", len(img_array) )
  
  '''Show some demos: '''
  
  #Uncomment this if to show some sample images of the gif 
  if len(img_array) > 7:
    for ii in [1, 4, 7]:
      st.image( img_array[ii] )
  
  #HuggingFaces Spaces can create a video vile ephemerally but doesn't actually save one that we can access. 
  #So to show the video/gif we save it as a file then open that file to show it in streamlit
  
  st.text("Generating GIF, may take a minute")
  
  #Create GIF
  img, *imgs = [Image.open(f) for f in output_filename_array]
  img.save(fp="output_gif.gif", format='GIF', append_images=imgs,
         save_all=True, duration=duration_parameter, loop=loop_parameter)
  
  
  #Show gif using this script to show gifs in streamlit https://discuss.streamlit.io/t/how-to-show-local-gif-image/3408/2
  """### gif from local file"""
  file_ = open("output_gif.gif", "rb")
  contents = file_.read()
  data_url = base64.b64encode(contents).decode("utf-8")
  file_.close()
  
  st.markdown(
      f'<img src="data:image/gif;base64,{data_url}" alt="output gif">',
      unsafe_allow_html=True,
  )