GIF and Video Processing

app.py

@@ -1,76 +1,53 @@
 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
 
-model = hub.Module(name='U2Net')
+warnings.filterwarnings("ignore")
+
+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 GIF(fname,pixel_size):
- gif = Image.open(fname)
- frames = [] 
- for i in range(gif.n_frames): #First Step: Splitting the GIF into frames
- gif.seek(i)
- frame = Image.new('RGB', gif.size)
- frame.paste(gif)
- frame = np.array(frame)
- frames.append(frame)
- result = pixL().toThePixL(frames, pixel_size)
- for frame in result: #Second Step: Adding Cartoon Effect to each frame
- frame = Image.fromarray(frame)
- frame = cv2.cvtColor(np.asarray(face2paint(model, frame)), cv2.COLOR_BGR2RGB)
- frames = []
-<<<<<<< HEAD
- for frame in result: #Third Step: Combining the frames into a GIF
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- frame = Image.fromarray(frame) 
- frames.append(frame)
- frames[0].save('cache.gif', append_images=frames, save_all=True, loop=1)
- cache = Image.open('cache.gif')
- return cache
-=======
- for frame in result:
- 
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- frame = Image.fromarray(frame) 
- frames.append(frame)
- print(type(frames), len(frames), type(frames[0]), frames[0].size)
- frames[0].save('cache.gif', append_images=frames, save_all=True, loop=1)
- return Image.open('cache.gif')
->>>>>>> a2e31df8118400de0782a9feb55255620b93cd83
 
-def initilize(image,pixel_size,checkbox1):
- if image.name.endswith('.gif'):
- GIF(image.name,pixel_size)
+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)
  else:
- image = Image.open(image.name).convert("RGB")
- image = cv2.cvtColor(np.asarray(face2paint(model, image)), cv2.COLOR_BGR2RGB)
+ media = Image.open(media.name).convert("RGB")
+ media = cv2.cvtColor(np.asarray(face2paint(model, media)), cv2.COLOR_BGR2RGB)
  if checkbox1:
- result = model.Segmentation(
- images=[image],
- paths=None,
- batch_size=1,
- input_size=320, 
- output_dir='output',
- visualization=True)
+ 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 = image)
+ background_image = media)
  else:
- result = pixL().toThePixL([image], pixel_size)
- return Image.fromarray(result)
+ result = pixL().toThePixL([media], pixel_size)
+ result.save('cache.png')
+ return [Image.fromarray(result), 'cache.png']
 
-inputs = ["file",
+inputs = [,
  gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"),
  gr.Checkbox(label="Object-Oriented Inference", value=False)]
-outputs = [gr.Image(type="pil",label="Front")]
-
+outputs = [gr.Video(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).launch()

methods/img2pixl.py

@@ -1,71 +1,61 @@
+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
 
-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
+warnings.filterwarnings("ignore")
 
- def preprocess(self):
- for image in self.images:
+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()
 
- 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:
- 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)
+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)
+ 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 background

methods/media.py

@@ -0,0 +1,32 @@
+import cv2
+import torch
+import imageio
+from methods.img2pixl import pixL
+
+
+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()
+
+class Media:
+ #Author: Alican Akca
+ def __init__(self,fname = None,pixel_size = None):
+ self.fname = fname
+ self.pixel_size = pixel_size
+
+ def split(self,fname,pixel_size):
+ media = cv2.VideoCapture(fname)
+ frames = []
+ while True:
+ ret, cv2Image = media.read()
+ if not ret:
+ break
+ frames.append(cv2Image)
+ frames = pixL().toThePixL(frames, pixel_size)
+ imageio.mimsave('cache.gif', frames)
+ output_file = "cache.mp4"
+ out = cv2.VideoWriter(output_file,cv2.VideoWriter_fourcc(*'h264'), 15, (frames[0].shape[1],frames[0].shape[0]))
+ for i in range(len(frames)):
+ out.write(frames[i])
+ out.release()
+ return [output_file, None, 'cache.gif']

requirements.txt

@@ -1,16 +1,7 @@
 pip
 torch
-tensorflow
-Flask
-gunicorn
+gradio
 Pillow
-opencv_python
-google-cloud-storage
-algorithmia
-scikit-video
-tf_slim
-PyYaml
-flask-ngrok
-paddlepaddle
+imageio
 paddlehub
-numpy==1.19.5
+opencv_python