GIF and Video Processing

app.py

@@ -57,4 +57,5 @@ gr.Interface(fn = initilize,
                     inputs = inputs,
                     outputs = outputs,
                     title=title,
-                    description=description).launch()
+                    description=description).launch()
+

methods/img2pixl.py

@@ -1,61 +1,71 @@
-import os
 import cv2
-import torch
-import warnings
 import numpy as np
-import gradio as gr
-import paddlehub as hub
 from PIL import Image
-from methods.img2pixl import pixL
-from examples.pixelArt.combine import combine
-from methods.media import Media
 
-warnings.filterwarnings("ignore")
+class pixL:
+  #Author: Alican Akca
+  def __init__(self,numOfSquaresW = None, numOfSquaresH= None, size = [False, (512,512)],square = 6,ImgH = None,ImgW = None,images = [],background_image = None):
+    self.images = images
+    self.size = size
+    self.ImgH = ImgH
+    self.ImgW = ImgW
+    self.square = square
+    self.numOfSquaresW = numOfSquaresW
+    self.numOfSquaresH = numOfSquaresH
 
-U2Net = hub.Module(name='U2Net')
-device = "cuda" if torch.cuda.is_available() else "cpu"
-face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device, size=512)
-model = torch.hub.load("bryandlee/animegan2-pytorch", "generator", device=device).eval()
+  def preprocess(self):
+    for image in self.images:
 
-
-def initilize(media,pixel_size,checkbox1):
-    #Author:  Alican Akca
-    if media.name.endswith('.gif'):
-      return Media().split(media.name,pixel_size)
-    elif media.name.endswith('.mp4'):
-      return Media().split(media.name,pixel_size)
+      size = (image.shape[0] - (image.shape[0] % 4), image.shape[1] - (image.shape[1] % 4))
+      image = cv2.resize(image, size)
+      image = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB)
+    
+    if len(self.images) == 1:
+      return self.images[0]
     else:
-      media = Image.open(media.name).convert("RGB")
-      media = cv2.cvtColor(np.asarray(face2paint(model, media)), cv2.COLOR_BGR2RGB)
-      if checkbox1:
-        result = U2Net.Segmentation(images=[media],
-                                    paths=None,
-                                    batch_size=1,
-                                    input_size=320,  
-                                    output_dir='output',
-                                    visualization=True)
-        result = combine.combiner(images = pixL().toThePixL([result[0]['front'][:,:,::-1], result[0]['mask']], 
-                                                        pixel_size),
-                                background_image = media)
-      else:
-        result = pixL().toThePixL([media], pixel_size)
-      result = Image.fromarray(result)
-      result.save('cache.png')
-      return [None, result, 'cache.png']
-
-inputs = [gr.File(label="Media"),
-               gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"),
-               gr.Checkbox(label="Object-Oriented Inference", value=False)]
-outputs = [gr.Video(label="Pixed Media"),
-           gr.Image(label="Pixed Media"),
-           gr.File(label="Download")]
-
-title = "Pixera: Create your own Pixel Art"
-description = """Mobile applications will have released soon ^^ """
-
-gr.Interface(fn = initilize,
-                    inputs = inputs,
-                    outputs = outputs,
-                    title=title,
-                    description=description).launch()
+      return self.images
+
+  def toThePixL(self,images, pixel_size):
+    self.images = []
+    self.square = pixel_size
+    for image in images:
+      image = Image.fromarray(image)
+      image = image.convert("RGB")
+      self.ImgW, self.ImgH = image.size
+      self.images.append(pixL.epicAlgorithm(self, image))
+      
+    return pixL.preprocess(self)
+
+  def numOfSquaresFunc(self):
+    self.numOfSquaresW = round((self.ImgW / self.square) + 1)
+    self.numOfSquaresH = round((self.ImgH / self.square) + 1)
+
+  def epicAlgorithm(self, image):
+    pixValues = []
+    pixL.numOfSquaresFunc(self)
+
+    for j in range(1,self.numOfSquaresH):
+
+      for i in range(1,self.numOfSquaresW):
+        
+        pixValues.append((image.getpixel((
+              i * self.square - self.square//2,
+              j * self.square - self.square//2)),
+              (i * self.square - self.square//2,
+              j * self.square - self.square//2)))
+    
+    background = 255 * np.ones(shape=[self.ImgH - self.square, 
+                                      self.ImgW - self.square*2, 3], 
+                                      dtype=np.uint8)                
+    
+    for pen in range(len(pixValues)):
+
+      cv2.rectangle(background, 
+                    pt1=(pixValues[pen][1][0] - self.square,pixValues[pen][1][1] - self.square), 
+                    pt2=(pixValues[pen][1][0] + self.square,pixValues[pen][1][1] + self.square), 
+                    color=(pixValues[pen][0][2],pixValues[pen][0][1],pixValues[pen][0][0]), 
+                    thickness=-1)
+    background = np.array(background).astype(np.uint8)
+    background = cv2.resize(background, (self.ImgW,self.ImgH), interpolation = cv2.INTER_AREA)
 
+    return background