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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 6.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: streaming_filter"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import cv2  # type: ignore\n", "\n", "def transform_cv2(frame, transform):\n", "    if transform == \"cartoon\":\n", "        # prepare color\n", "        img_color = cv2.pyrDown(cv2.pyrDown(frame))\n", "        for _ in range(6):\n", "            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)\n", "        img_color = cv2.pyrUp(cv2.pyrUp(img_color))\n", "\n", "        # prepare edges\n", "        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n", "        img_edges = cv2.adaptiveThreshold(\n", "            cv2.medianBlur(img_edges, 7),\n", "            255,\n", "            cv2.ADAPTIVE_THRESH_MEAN_C,\n", "            cv2.THRESH_BINARY,\n", "            9,\n", "            2,\n", "        )\n", "        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)\n", "        # combine color and edges\n", "        img = cv2.bitwise_and(img_color, img_edges)\n", "        return img\n", "    elif transform == \"edges\":\n", "        # perform edge detection\n", "        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)\n", "        return img\n", "    else:\n", "        return np.flipud(frame)\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            transform = gr.Dropdown(choices=[\"cartoon\", \"edges\", \"flip\"],\n", "                                    value=\"flip\", label=\"Transformation\")\n", "            input_img = gr.Image(sources=[\"webcam\"], type=\"numpy\")\n", "        with gr.Column():\n", "            output_img = gr.Image(streaming=True)\n", "        dep = input_img.stream(transform_cv2, [input_img, transform], [output_img],\n", "                                time_limit=30, stream_every=0.1, concurrency_limit=30)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: streaming_filter"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import cv2  # type: ignore\n", "\n", "\n", "def transform_cv2(frame, transform):\n", "    if transform == \"cartoon\":\n", "        # prepare color\n", "        img_color = cv2.pyrDown(cv2.pyrDown(frame))\n", "        for _ in range(6):\n", "            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)\n", "        img_color = cv2.pyrUp(cv2.pyrUp(img_color))\n", "\n", "        # prepare edges\n", "        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n", "        img_edges = cv2.adaptiveThreshold(\n", "            cv2.medianBlur(img_edges, 7),\n", "            255,\n", "            cv2.ADAPTIVE_THRESH_MEAN_C,\n", "            cv2.THRESH_BINARY,\n", "            9,\n", "            2,\n", "        )\n", "        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)\n", "        # combine color and edges\n", "        img = cv2.bitwise_and(img_color, img_edges)\n", "        return img\n", "    elif transform == \"edges\":\n", "        # perform edge detection\n", "        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)\n", "        return img\n", "    else:\n", "        return np.flipud(frame)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            transform = gr.Dropdown(\n", "                choices=[\"cartoon\", \"edges\", \"flip\"],\n", "                value=\"flip\",\n", "                label=\"Transformation\",\n", "            )\n", "            input_img = gr.Image(sources=[\"webcam\"], type=\"numpy\")\n", "        with gr.Column():\n", "            output_img = gr.Image(streaming=True)\n", "        dep = input_img.stream(\n", "            transform_cv2,\n", "            [input_img, transform],\n", "            [output_img],\n", "            time_limit=30,\n", "            stream_every=0.1,\n", "            concurrency_limit=30,\n", "        )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -2,6 +2,7 @@ import gradio as gr
 import numpy as np
 import cv2  # type: ignore
 
+
 def transform_cv2(frame, transform):
     if transform == "cartoon":
         # prepare color
@@ -31,16 +32,26 @@ def transform_cv2(frame, transform):
     else:
         return np.flipud(frame)
 
+
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
-            transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
-                                    value="flip", label="Transformation")
+            transform = gr.Dropdown(
+                choices=["cartoon", "edges", "flip"],
+                value="flip",
+                label="Transformation",
+            )
             input_img = gr.Image(sources=["webcam"], type="numpy")
         with gr.Column():
             output_img = gr.Image(streaming=True)
-        dep = input_img.stream(transform_cv2, [input_img, transform], [output_img],
-                                time_limit=30, stream_every=0.1, concurrency_limit=30)
+        dep = input_img.stream(
+            transform_cv2,
+            [input_img, transform],
+            [output_img],
+            time_limit=30,
+            stream_every=0.1,
+            concurrency_limit=30,
+        )
 
 if __name__ == "__main__":
     demo.launch()