move embedding logic to separate file
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DepFA
GitHub
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1eaad95533
commit
c080f52cea
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@ -0,0 +1,234 @@
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import base64
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import json
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import numpy as np
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import zlib
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from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
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from fonts.ttf import Roboto
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import torch
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class EmbeddingEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, torch.Tensor):
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return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
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return json.JSONEncoder.default(self, obj)
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class EmbeddingDecoder(json.JSONDecoder):
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def __init__(self, *args, **kwargs):
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json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
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def object_hook(self, d):
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if 'TORCHTENSOR' in d:
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return torch.from_numpy(np.array(d['TORCHTENSOR']))
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return d
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def embedding_to_b64(data):
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d = json.dumps(data,cls=EmbeddingEncoder)
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return base64.b64encode(d.encode())
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def embedding_from_b64(data):
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d = base64.b64decode(data)
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return json.loads(d,cls=EmbeddingDecoder)
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def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
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while True:
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seed = (a * seed + c) % m
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yield seed%255
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def xor_block(block):
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g = lcg()
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randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
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return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F)
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def style_block(block,sequence):
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im = Image.new('RGB',(block.shape[1],block.shape[0]))
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draw = ImageDraw.Draw(im)
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i=0
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for x in range(-6,im.size[0],8):
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for yi,y in enumerate(range(-6,im.size[1],8)):
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offset=0
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if yi%2==0:
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offset=4
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shade = sequence[i%len(sequence)]
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i+=1
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draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
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fg = np.array(im).astype(np.uint8) & 0xF0
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return block ^ fg
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def insert_image_data_embed(image,data):
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d = 3
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data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
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data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
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data_np_high = data_np_ >> 4
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data_np_low = data_np_ & 0x0F
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h = image.size[1]
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next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
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next_size = next_size + ((h*d)-(next_size%(h*d)))
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data_np_low.resize(next_size)
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data_np_low = data_np_low.reshape((h,-1,d))
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data_np_high.resize(next_size)
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data_np_high = data_np_high.reshape((h,-1,d))
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edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
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edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
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data_np_low = style_block(data_np_low,sequence=edge_style)
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data_np_low = xor_block(data_np_low)
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data_np_high = style_block(data_np_high,sequence=edge_style[::-1])
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data_np_high = xor_block(data_np_high)
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im_low = Image.fromarray(data_np_low,mode='RGB')
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im_high = Image.fromarray(data_np_high,mode='RGB')
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background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
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background.paste(im_low,(0,0))
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background.paste(image,(im_low.size[0]+1,0))
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background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
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return background
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def crop_black(img,tol=0):
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mask = (img>tol).all(2)
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mask0,mask1 = mask.any(0),mask.any(1)
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col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
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row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
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return img[row_start:row_end,col_start:col_end]
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def extract_image_data_embed(image):
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d=3
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outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
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black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
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if black_cols[0].shape[0] < 2:
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print('No Image data blocks found.')
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return None
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data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
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data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
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data_block_lower = xor_block(data_block_lower)
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data_block_upper = xor_block(data_block_upper)
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data_block = (data_block_upper << 4) | (data_block_lower)
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data_block = data_block.flatten().tobytes()
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data = zlib.decompress(data_block)
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return json.loads(data,cls=EmbeddingDecoder)
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def addCaptionLines(lines,image,initialx,textfont):
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draw = ImageDraw.Draw(image)
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hstart =initialx
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for fill,line in lines:
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fontsize = 32
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font = ImageFont.truetype(textfont, fontsize)
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_,_,w, h = draw.textbbox((0,0),line,font=font)
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fontsize = min( int(fontsize * ((image.size[0]-35)/w) ), 28)
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font = ImageFont.truetype(textfont, fontsize)
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_,_,w,h = draw.textbbox((0,0),line,font=font)
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draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
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hstart += h
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return hstart
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def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
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if font is None:
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try:
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font = ImageFont.truetype(opts.font or Roboto, fontsize)
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font = opts.font or Roboto
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except Exception:
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font = Roboto
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sample_image = image
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background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
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hoffset = addCaptionLines(prelines,background,5,font)+16
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background.paste(sample_image,(0,hoffset))
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hoffset = hoffset+sample_image.size[1]+8
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hoffset = addCaptionLines(postlines,background,hoffset,font)
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background = background.crop((0,0,sample_image.size[0],hoffset+8))
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return background
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def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
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from math import cos
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image = srcimage.copy()
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if textfont is None:
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try:
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textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
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textfont = opts.font or Roboto
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except Exception:
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textfont = Roboto
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factor = 1.5
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gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
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for y in range(image.size[1]):
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mag = 1-cos(y/image.size[1]*factor)
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mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
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gradient.putpixel((0, y), (0,0,0,int(mag*255)))
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image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
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draw = ImageDraw.Draw(image)
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fontsize = 32
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font = ImageFont.truetype(textfont, fontsize)
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padding = 10
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_,_,w, h = draw.textbbox((0,0),title,font=font)
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fontsize = min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
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font = ImageFont.truetype(textfont, fontsize)
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_,_,w,h = draw.textbbox((0,0),title,font=font)
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draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
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_,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
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fontsize_left = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
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_,_,w, h = draw.textbbox((0,0),footerMid,font=font)
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fontsize_mid = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
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_,_,w, h = draw.textbbox((0,0),footerRight,font=font)
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fontsize_right = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
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font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
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draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
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draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230))
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draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
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return image
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if __name__ == '__main__':
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image = Image.new('RGBA',(512,512),(255,255,200,255))
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caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
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[((255,255,255),'line c'),((255,255,255),'line d')])
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image = Image.new('RGBA',(512,512),(255,255,200,255))
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cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
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test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
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embedded_image = insert_image_data_embed(cap_image, test_embed)
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retrived_embed = extract_image_data_embed(embedded_image)
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assert str(retrived_embed) == str(test_embed)
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embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
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assert embedded_image == embedded_image2
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g = lcg()
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shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
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reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
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95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
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160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
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38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
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30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
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41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
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66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
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204, 86, 73, 222, 44, 198, 118, 240, 97]
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assert shared_random == reference_random
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hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
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assert 12731374 == hunna_kay_random_sum
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