inital commit

app.py

@@ -0,0 +1,151 @@
+from torchvision.models.detection import maskrcnn_resnet50_fpn_v2, MaskRCNN_ResNet50_FPN_V2_Weights
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from torchvision.utils import draw_segmentation_masks, draw_bounding_boxes
+import random
+import gradio as gr
+import numpy as np
+
+
+def random_color_gen(n):
+    return [tuple(random.randint(0,255) for i in range(3)) for i in range(n)]
+
+import math
+
+def get_gaussian_kernel(kernel_size=15, sigma=20, channels=3):
+    # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+    x_coord = torch.arange(kernel_size)
+    x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
+    y_grid = x_grid.t()
+    xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
+
+    mean = (kernel_size - 1)/2.
+    variance = sigma**2.
+
+    # Calculate the 2-dimensional gaussian kernel which is
+    # the product of two gaussian distributions for two different
+    # variables (in this case called x and y)
+    gaussian_kernel = (1./(2.*math.pi*variance)) *\
+                      torch.exp(
+                          -torch.sum((xy_grid - mean)**2., dim=-1) /\
+                          (2*variance)
+                      )
+
+    # Make sure sum of values in gaussian kernel equals 1.
+    gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+    # Reshape to 2d depthwise convolutional weight
+    gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
+    gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1)
+
+    gaussian_filter = nn.Conv2d(in_channels=channels, out_channels=channels,
+                                kernel_size=kernel_size, padding='same', groups=channels, bias=False)
+
+    gaussian_filter.weight.data = gaussian_kernel
+    gaussian_filter.weight.requires_grad = False
+    
+    return gaussian_filter
+
+
+output_dict = {} # this dict is shared between segment and blur_background functions
+pred_label_unq = []
+
+def segment(input_image):
+    
+    # prepare image for display
+    display_img = torch.tensor(np.asarray(input_image)).unsqueeze(0)
+    display_img =  display_img.permute(0, 3, 1, 2).squeeze(0)
+    
+    # Prepare the RCNN model
+    weights = MaskRCNN_ResNet50_FPN_V2_Weights.COCO_V1
+    transforms = weights.transforms()
+    model = maskrcnn_resnet50_fpn_v2(weights=weights)
+    model = model.eval();
+    
+    # Prepare the input image
+    input_tensor = transforms(input_image).unsqueeze(0)
+    
+    # Get the predictions
+    output = model(input_tensor)[0] # idx 0 to get the first dictionary of the returned list
+    
+    
+    # Filter by threshold
+    score_threshold = 0.75
+    mask_threshold = 0.5
+    masks = output['masks'][output['scores'] > score_threshold] > mask_threshold;
+    boxes = output['boxes'][output['scores'] > score_threshold]
+    masks = masks.squeeze(1)
+    boxes = boxes.squeeze(1)
+    
+    pred_labels = [weights.meta["categories"][label] for label in output['labels'][output['scores'] > score_threshold]]
+    n_pred = len(pred_labels)
+    
+    # give unique id to all the predicitons
+    pred_label_unq = [pred_labels[i] + str(pred_labels[:i].count(pred_labels[i]) + 1) for i in range(n_pred)]
+    
+    colors = random_color_gen(n_pred)
+    
+    # Prepare output_dict
+    for i in range(n_pred):
+        output_dict[pred_label_unq[i]] = {'mask': masks[i].tolist(), 'color': colors[i]}
+        
+    
+    masked_img = draw_segmentation_masks(display_img, masks, alpha=0.9, colors=colors)
+    bounding_box_img = draw_bounding_boxes(masked_img, boxes, labels=pred_label_unq, colors='white')
+    masked_img = T.ToPILImage()(masked_img)
+    bounding_box_img = T.ToPILImage()(bounding_box_img)
+    
+    return bounding_box_img;
+
+def blur_background(input_image, label_name):
+    mask = output_dict[label_name]['mask']
+    mask = torch.tensor(mask).unsqueeze(0)
+    
+    input_tensor = T.ToTensor()(input_image).unsqueeze(0)
+    blur = get_gaussian_kernel()
+    blurred_tensor = blur(input_tensor)
+    
+    final_img = blurred_tensor
+    final_img[:, :, mask.squeeze(0)] = input_tensor[:, :, mask.squeeze(0)];
+    
+    final_img = T.ToPILImage()(final_img.squeeze(0))
+    
+    return final_img;
+    
+    
+    
+
+    
+    
+with gr.Blocks() as app:
+    
+    gr.Markdown("# Blur an objects background with AI")
+    
+    gr.Markdown("First segment the image and create bounding boxes")
+    with gr.Column():
+        input_image = gr.Image(type='pil')
+        b1 = gr.Button("Segment Image")
+        
+    
+    
+    with gr.Row():
+#         masked_image = gr.Image();
+        bounding_box_image = gr.Image();
+    
+    
+    gr.Markdown("Now choose a label (eg: person1) from the above image of your desired object and input it below")
+    with gr.Column():
+        label_name = gr.Textbox()
+        b2 = gr.Button("Blur Backbround")
+        result = gr.Image()
+    
+    b1.click(segment, inputs=input_image, outputs=bounding_box_image)
+    b2.click(blur_background, inputs=[input_image, label_name], outputs=result)
+    
+    
+    
+    
+#     instance_segmentation = gr.Interface(segment, inputs=input_image, outputs=['json', 'image'])
+
+app.launch(debug=True)