Add lama

.gitattributes

@@ -32,5 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-third_party filter=lfs diff=lfs merge=lfs -text
-pretrained_models filter=lfs diff=lfs merge=lfs -text
+**.png filter=lfs diff=lfs merge=lfs -text
+**.gif filter=lfs diff=lfs merge=lfs -text
+**.jpg filter=lfs diff=lfs merge=lfs -text
+third_party/** filter=lfs diff=lfs merge=lfs -text
+pretrained_models/** filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,15 @@
+# Python byte code, etc.
+__pycache__/
+
+# C/C++ object files/libraries
+*.o
+*.so
+
+# macOS
+**/.DS_Store
+
+# tmp
+~*
+
+# third_party git
+third_party/**.git

app.py

@@ -1,25 +1,84 @@
 import gradio as gr
 import numpy as np
-
+from pathlib import Path
+from matplotlib import pyplot as plt
+import torch
+import tempfile
+import os
 from sam_segment import predict_masks_with_sam
 from lama_inpaint import inpaint_img_with_lama
 from utils import load_img_to_array, save_array_to_img, dilate_mask, \
     show_mask, show_points
 
 
+def mkstemp(suffix, dir=None):
+    fd, path = tempfile.mkstemp(suffix=f"{suffix}", dir=dir)
+    os.close(fd)
+    return Path(path)
+
+
+def get_masked_img(img, point_coords):
+    point_labels = [1]
+    dilate_kernel_size = 15
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    masks, _, _ = predict_masks_with_sam(
+        img,
+        [point_coords],
+        point_labels,
+        model_type="vit_h",
+        ckpt_p="pretrained_models/sam_vit_h_4b8939.pth",
+        device=device,
+    )
+    masks = masks.astype(np.uint8) * 255
+
+    # dilate mask to avoid unmasked edge effect
+    if dilate_kernel_size is not None:
+        masks = [dilate_mask(mask, dilate_kernel_size) for mask in masks]
+
+    figs = []
+    for idx, mask in enumerate(masks):
+        # save the pointed and masked image
+        tmp_p = mkstemp(".png")
+        dpi = plt.rcParams['figure.dpi']
+        height, width = img.shape[:2]
+        fig = plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
+        plt.imshow(img)
+        plt.axis('off')
+        # show_points(plt.gca(), [point_coords], point_labels,
+        #             size=(width*0.04)**2)
+        # plt.savefig(tmp_p, bbox_inches='tight', pad_inches=0)
+        show_mask(plt.gca(), mask, random_color=False)
+        plt.savefig(tmp_p, bbox_inches='tight', pad_inches=0)
+        figs.append(fig)
+        plt.close()
+    return figs
+
+
+
 with gr.Blocks() as demo:
     with gr.Row():
-        input_img = gr.Image(label="Input")
-        output_img = gr.Image(label="Selected Segment")
-
+        img = gr.Image(label="Image")
+        with gr.Row(label="Image with Segmentation Mask"):
+            img_with_mask_0 = gr.Plot()
+            img_with_mask_1 = gr.Plot()
+            img_with_mask_2 = gr.Plot()
     with gr.Row():
-        h = gr.Number()
         w = gr.Number()
+        h = gr.Number()
+
+    predict_mask = gr.Button("Predict Mask Using SAM")
+
+
+    def get_select_coords(evt: gr.SelectData):
+        return evt.index[0], evt.index[1]
 
-    def get_select_coords(img, evt: gr.SelectData):
-        return evt.index[1], evt.index[0]
+    img.select(get_select_coords, [], [w, h])
+    predict_mask.click(
+        get_masked_img,
+        [img, [w, h]],
+        [img_with_mask_0, img_with_mask_1, img_with_mask_2]
+    )
 
-    input_img.select(get_select_coords, [input_img,], [h, w])
 
 if __name__ == "__main__":
     demo.launch()

third_party/lama

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