for gif processing

app.py

@@ -1,53 +1,69 @@
 import cv2
 import numpy as np
 import gradio as gr
+from PIL import Image
 import paddlehub as hub
 from methods.img2pixl import pixL
-from methods.combine import combine
-from methods.white_box_cartoonizer.cartoonize import WB_Cartoonize
+from examples.pixelArt.combine import combine
+from examples.pixelArt.white_box_cartoonizer.cartoonize import WB_Cartoonize
 model = hub.Module(name='U2Net')
 pixl = pixL()
 combine = combine()
 
+def GIF(fname,pixel_size):
+    print(fname)
+    gif = Image.open(fname)
+    frames = []
+    for i in range(gif.n_frames):
+        gif.seek(i)
+        frame = Image.new('RGB', gif.size)
+        frame.paste(gif)
+        frame = np.array(frame)
+        frames.append(frame)
+    print(len(frames))
+    
+    result = pixl.toThePixL(frames, pixel_size)
+    print(len(result), result[0].shape)
+    frames = []
+    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('new.gif', append_images=frames, save_all=True, loop=1)
+    return Image.open('cache.gif')
+
 def func_tab1(image,pixel_size, checkbox1):
-  image = cv2.imread(image.name)
-  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-  image = WB_Cartoonize().infer(image)
-  image = np.array(image)
-  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-  if checkbox1:
-    result = model.Segmentation(
-        images=[image],
-        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)
+  if image.name.endswith('.gif'):
+    GIF(image.name,pixel_size)
   else:
-    result = pixl.toThePixL([image], pixel_size)
-  return result
-
-def func_tab2():
-  pass
+    image = cv2.imread(image.name)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = WB_Cartoonize().infer(image)
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    if checkbox1:
+      result = model.Segmentation(
+          images=[image],
+          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)
+    else:
+      result = pixl.toThePixL([image], pixel_size)
+    return result
 
 inputs_tab1 = [gr.inputs.Image(type='file', label="Image"),
                gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"),
                gr.Checkbox(label="Object-Oriented Inference", value=False)]
-outputs_tab1 = [gr.Image(type="numpy",label="Front")]
+outputs_tab1 = [gr.Image(type="file",label="Front")]
 
-inputs_tab2 = [gr.Video()]
-outputs_tab2 = [gr.Video()]
-
-tab1 = gr.Interface(fn = func_tab1,
+gr.Interface(fn = func_tab1,
                     inputs = inputs_tab1,
-                    outputs = outputs_tab1)
-#Pixera for Videos
-tab2 = gr.Interface(fn = func_tab2,
-                    inputs = inputs_tab2,
-                    outputs = outputs_tab2)
-
-gr.TabbedInterface([tab1], ["Pixera for Images"]).launch()
+                    outputs = outputs_tab1).launch()
 

methods/secondMethod.py

@@ -1,12 +0,0 @@
-import numpy as np
-import cv2 as cv
-from matplotlib import pyplot as plt
-import os
-
-os.chdir("C:\\Users\Alican Akca\\OneDrive - Izmir Universtiy of Economics\\Belgeler\\GitHub\\pixera")
-img = cv.imread(f'{os.getcwd()}\original\1.jpg',0)
-edges = cv.Canny(img,100,200)
-
-plt.subplot(122),plt.imshow(edges,cmap = 'gray')
-plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
-plt.show()

methods/white_box_cartoonizer/saved_models/model-33999.data-00000-of-00001

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1e2df1a5aa86faa4f979720bfc2436f79333a480876f8d6790b7671cf50fe75b
-size 5868300