Update app.py

app.py

@@ -201,51 +201,51 @@ def main_fun(image_pil, x_units, y_units, text_prompt):
     output = draw_image(image_pil, masks, boxes, alpha=0.4)
 
 
-    '''Get masked object and background as two separate images'''
-    mask = np.expand_dims(masks[0], axis=-1)
-    masked_object = image_pil * mask
-    background = image_pil * ~mask
-
-
-    '''Shifts image by x_units and y_units'''
-    M = np.float32([[1, 0, x_units], [0, 1, y_units]])
-    shifted_image = cv2.warpAffine(masked_object, M, (masked_object.shape[1] , masked_object.shape[0] ), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0))
-    masked_shifted_image = np.where(shifted_image[:, :, 0] != 0, True, False)
-
-    '''Load stable diffuser model at checkpoint finetuned for inpainting task'''
-    pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    #     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
-        "stabilityai/stable-diffusion-2-inpainting",torch_dtype=torch.float16)
-    pipe.to(CFG.device)
-
-
-    # With Dilation
-    structuring_element = np.ones((15, 15, 1), dtype=bool)
-    extrapolated_mask = binary_dilation(mask, structure=structuring_element)
-    mask_as_uint8 = extrapolated_mask.astype(np.uint8) * 255
-    pil_mask = Image.fromarray(mask_as_uint8.squeeze(), mode='L').resize((1024, 1024))
-
-    # # Without Dilation
-    # pil_background = Image.fromarray(background)
-    # mask_as_uint8 = mask.astype(np.uint8) * 255
-    # pil_mask = Image.fromarray(mask_as_uint8.squeeze(), mode='L')
-
-    '''Do inpainting on masked locations of original image'''
-    prompt = 'fill as per background and neighborhood'
-    inpainted_image = pipe(prompt=prompt, image=image_pil, mask_image=pil_mask).images[0]
-    # inpainted_image
-
-    '''Get composite of shifted object and background inpainted imaage'''
-    pil_shifted_image = Image.fromarray(shifted_image).resize(inpainted_image.size)
-    np_shifted_image = np.array(pil_shifted_image)
-    masked_shifted_image = np.where(np_shifted_image[:, :, 0] != 0, True, False)
-    masked_shifted_image = np.expand_dims(masked_shifted_image, axis=-1)
-    inpainted_shifted = np.array(inpainted_image) * ~masked_shifted_image
-
-    shifted_image = cv2.resize(shifted_image, inpainted_image.size)
-    output = inpainted_shifted + shifted_image
-    output = Image.fromarray(output)
-#     visualize_results(image_pil, output, 'shifted')
+#     '''Get masked object and background as two separate images'''
+#     mask = np.expand_dims(masks[0], axis=-1)
+#     masked_object = image_pil * mask
+#     background = image_pil * ~mask
+
+
+#     '''Shifts image by x_units and y_units'''
+#     M = np.float32([[1, 0, x_units], [0, 1, y_units]])
+#     shifted_image = cv2.warpAffine(masked_object, M, (masked_object.shape[1] , masked_object.shape[0] ), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0))
+#     masked_shifted_image = np.where(shifted_image[:, :, 0] != 0, True, False)
+
+#     '''Load stable diffuser model at checkpoint finetuned for inpainting task'''
+#     pipe = StableDiffusionInpaintPipeline.from_pretrained(
+#     #     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
+#         "stabilityai/stable-diffusion-2-inpainting",torch_dtype=torch.float16)
+#     pipe.to(CFG.device)
+
+
+#     # With Dilation
+#     structuring_element = np.ones((15, 15, 1), dtype=bool)
+#     extrapolated_mask = binary_dilation(mask, structure=structuring_element)
+#     mask_as_uint8 = extrapolated_mask.astype(np.uint8) * 255
+#     pil_mask = Image.fromarray(mask_as_uint8.squeeze(), mode='L').resize((1024, 1024))
+
+#     # # Without Dilation
+#     # pil_background = Image.fromarray(background)
+#     # mask_as_uint8 = mask.astype(np.uint8) * 255
+#     # pil_mask = Image.fromarray(mask_as_uint8.squeeze(), mode='L')
+
+#     '''Do inpainting on masked locations of original image'''
+#     prompt = 'fill as per background and neighborhood'
+#     inpainted_image = pipe(prompt=prompt, image=image_pil, mask_image=pil_mask).images[0]
+#     # inpainted_image
+
+#     '''Get composite of shifted object and background inpainted imaage'''
+#     pil_shifted_image = Image.fromarray(shifted_image).resize(inpainted_image.size)
+#     np_shifted_image = np.array(pil_shifted_image)
+#     masked_shifted_image = np.where(np_shifted_image[:, :, 0] != 0, True, False)
+#     masked_shifted_image = np.expand_dims(masked_shifted_image, axis=-1)
+#     inpainted_shifted = np.array(inpainted_image) * ~masked_shifted_image
+
+#     shifted_image = cv2.resize(shifted_image, inpainted_image.size)
+#     output = inpainted_shifted + shifted_image
+#     output = Image.fromarray(output)
+# #     visualize_results(image_pil, output, 'shifted')
     
     return output