Upload folder using huggingface_hub

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Streaming Filter Unified Main
-emoji: 🌖
-colorFrom: red
-colorTo: green
+title: streaming_filter_unified_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
+sdk_version: 5.0.0
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,4 @@
+gradio-client @ git+https://github.com/gradio-app/gradio@bbf9ba7e997022960c621f72baa891185bd03732#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/bbf9ba7e997022960c621f72baa891185bd03732/gradio-5.0.0-py3-none-any.whl
+opencv-python
+numpy

run.ipynb

@@ -0,0 +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\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}

run.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import numpy as np
+import cv2
+
+def transform_cv2(frame, transform):
+    if transform == "cartoon":
+        # prepare color
+        img_color = cv2.pyrDown(cv2.pyrDown(frame))
+        for _ in range(6):
+            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
+        img_color = cv2.pyrUp(cv2.pyrUp(img_color))
+
+        # prepare edges
+        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        img_edges = cv2.adaptiveThreshold(
+            cv2.medianBlur(img_edges, 7),
+            255,
+            cv2.ADAPTIVE_THRESH_MEAN_C,
+            cv2.THRESH_BINARY,
+            9,
+            2,
+        )
+        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
+        # combine color and edges
+        img = cv2.bitwise_and(img_color, img_edges)
+        return img
+    elif transform == "edges":
+        # perform edge detection
+        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
+        return img
+    else:
+        return np.flipud(frame)
+
+
+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.Column(elem_classes=["my-column"]):
+        with gr.Group(elem_classes=["my-group"]):
+            transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
+                                    value="flip", label="Transformation")
+            input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
+    input_img.stream(transform_cv2, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)
+
+
+if __name__ == "__main__":
+    demo.launch()