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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

requirements.txt

@@ -1,4 +1,4 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@e05eb8df38a4ca20993e94ca4e209cf8110bb677#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/e05eb8df38a4ca20993e94ca4e209cf8110bb677/gradio-5.49.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@d007e6cf617baba5c62e49ec2b7ce278aa863a79#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/d007e6cf617baba5c62e49ec2b7ce278aa863a79/gradio-6.0.0-py3-none-any.whl
 opencv-python
 numpy

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: streaming_filter_unified"]}, {"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", "\n", "css=\"\"\".my-group {max-width: 500px !important; max-height: 500px !important;}\n", "            .my-column {display: flex !important; justify-content: center !important; align-items: center !important};\"\"\"\n", "\n", "with gr.Blocks(css=css) as demo:\n", "    with gr.Column(elem_classes=[\"my-column\"]):\n", "        with gr.Group(elem_classes=[\"my-group\"]):\n", "            transform = gr.Dropdown(choices=[\"cartoon\", \"edges\", \"flip\"],\n", "                                    value=\"flip\", label=\"Transformation\")\n", "            input_img = gr.Image(sources=[\"webcam\"], type=\"numpy\", streaming=True)\n", "    input_img.stream(transform_cv2, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)\n", "\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_unified"]}, {"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", "\n", "css=\"\"\".my-group {max-width: 500px !important; max-height: 500px !important;}\n", "            .my-column {display: flex !important; justify-content: center !important; align-items: center !important};\"\"\"\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Column(elem_classes=[\"my-column\"]):\n", "        with gr.Group(elem_classes=[\"my-group\"]):\n", "            transform = gr.Dropdown(choices=[\"cartoon\", \"edges\", \"flip\"],\n", "                                    value=\"flip\", label=\"Transformation\")\n", "            input_img = gr.Image(sources=[\"webcam\"], type=\"numpy\", streaming=True)\n", "    input_img.stream(transform_cv2, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(css=css)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -35,7 +35,7 @@ def transform_cv2(frame, transform):
 css=""".my-group {max-width: 500px !important; max-height: 500px !important;}
             .my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
 
-with gr.Blocks(css=css) as demo:
+with gr.Blocks() as demo:
     with gr.Column(elem_classes=["my-column"]):
         with gr.Group(elem_classes=["my-group"]):
             transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
@@ -45,4 +45,4 @@ with gr.Blocks(css=css) as demo:
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(css=css)