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README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Image Selections Main
-emoji: ⚡
+title: image_selections_main 
+emoji: 🔥
 colorFrom: indigo
-colorTo: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
+sdk_version: 3.25.1b1
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,2 @@
+
+https://gradio-main-build.s3.amazonaws.com/9682bc82db93f33acb68c41d37d4fdd3c80176e8/gradio-3.25.1b1-py3-none-any.whl

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_selections"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "with gr.Blocks() as demo:\n", "    tolerance = gr.Slider(label=\"Tolerance\", info=\"How different colors can be in a segment.\", minimum=0, maximum=256*3, value=50)\n", "    with gr.Row():\n", "        input_img = gr.Image(label=\"Input\")\n", "        output_img = gr.Image(label=\"Selected Segment\")\n", "\n", "    def get_select_coords(img, tolerance, evt: gr.SelectData):\n", "        visited_pixels = set()\n", "        pixels_in_queue = set()\n", "        pixels_in_segment = set()\n", "        start_pixel = img[evt.index[1], evt.index[0]]\n", "        pixels_in_queue.add((evt.index[1], evt.index[0]))\n", "        while len(pixels_in_queue) > 0:\n", "            pixel = pixels_in_queue.pop()\n", "            visited_pixels.add(pixel)\n", "            neighbors = []\n", "            if pixel[0] > 0:\n", "                neighbors.append((pixel[0] - 1, pixel[1]))\n", "            if pixel[0] < img.shape[0] - 1:\n", "                neighbors.append((pixel[0] + 1, pixel[1]))\n", "            if pixel[1] > 0:\n", "                neighbors.append((pixel[0], pixel[1] - 1))\n", "            if pixel[1] < img.shape[1] - 1:\n", "                neighbors.append((pixel[0], pixel[1] + 1))\n", "            for neighbor in neighbors:\n", "                if neighbor in visited_pixels:\n", "                    continue\n", "                neighbor_pixel = img[neighbor[0], neighbor[1]]\n", "                if np.abs(neighbor_pixel - start_pixel).sum() < tolerance:\n", "                    pixels_in_queue.add(neighbor)\n", "                    pixels_in_segment.add(neighbor)\n", "\n", "        out = img.copy() * 0.2\n", "        out = out.astype(np.uint8)\n", "        for pixel in pixels_in_segment:\n", "            out[pixel[0], pixel[1]] = img[pixel[0], pixel[1]]\n", "        return out\n", "    \n", "    input_img.select(get_select_coords, [input_img, tolerance], output_img)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+import numpy as np
+
+with gr.Blocks() as demo:
+    tolerance = gr.Slider(label="Tolerance", info="How different colors can be in a segment.", minimum=0, maximum=256*3, value=50)
+    with gr.Row():
+        input_img = gr.Image(label="Input")
+        output_img = gr.Image(label="Selected Segment")
+
+    def get_select_coords(img, tolerance, evt: gr.SelectData):
+        visited_pixels = set()
+        pixels_in_queue = set()
+        pixels_in_segment = set()
+        start_pixel = img[evt.index[1], evt.index[0]]
+        pixels_in_queue.add((evt.index[1], evt.index[0]))
+        while len(pixels_in_queue) > 0:
+            pixel = pixels_in_queue.pop()
+            visited_pixels.add(pixel)
+            neighbors = []
+            if pixel[0] > 0:
+                neighbors.append((pixel[0] - 1, pixel[1]))
+            if pixel[0] < img.shape[0] - 1:
+                neighbors.append((pixel[0] + 1, pixel[1]))
+            if pixel[1] > 0:
+                neighbors.append((pixel[0], pixel[1] - 1))
+            if pixel[1] < img.shape[1] - 1:
+                neighbors.append((pixel[0], pixel[1] + 1))
+            for neighbor in neighbors:
+                if neighbor in visited_pixels:
+                    continue
+                neighbor_pixel = img[neighbor[0], neighbor[1]]
+                if np.abs(neighbor_pixel - start_pixel).sum() < tolerance:
+                    pixels_in_queue.add(neighbor)
+                    pixels_in_segment.add(neighbor)
+
+        out = img.copy() * 0.2
+        out = out.astype(np.uint8)
+        for pixel in pixels_in_segment:
+            out[pixel[0], pixel[1]] = img[pixel[0], pixel[1]]
+        return out
+    
+    input_img.select(get_select_coords, [input_img, tolerance], output_img)
+
+if __name__ == "__main__":
+    demo.launch()