Duplicate from asaderu/100-Sports_Classification

Co-authored-by: Asadel Ann <[email protected]>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+    MIT License
+
+    Copyright (c) Microsoft Corporation.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

README.md

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+---
+title: 100-Sports Classification
+emoji: 📈
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.15.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: asaderu/100-Sports_Classification
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+
+import gradio as gr
+import pathlib
+import numpy as np
+import tensorflow
+import PIL.Image
+from PIL import Image
+
+
+class Model:
+    def __init__(self, model_filepath):
+        self.graph_def = tensorflow.compat.v1.GraphDef()
+        self.graph_def.ParseFromString(model_filepath.read_bytes())
+
+        input_names, self.output_names = self._get_graph_inout(self.graph_def)
+        assert len(input_names) == 1
+        self.input_name = input_names[0]
+        self.input_shape = self._get_input_shape(self.graph_def, self.input_name)
+
+    def predict(self, image_filepath):
+        image = Image.fromarray(image_filepath).resize(self.input_shape)
+        input_array = np.array(image, dtype=np.float32)[np.newaxis, :, :, :]
+
+        with tensorflow.compat.v1.Session() as sess:
+            tensorflow.import_graph_def(self.graph_def, name='')
+            out_tensors = [sess.graph.get_tensor_by_name(o + ':0') for o in self.output_names]
+            outputs = sess.run(out_tensors, {self.input_name + ':0': input_array})
+
+        return {name: outputs[i] for i, name in enumerate(self.output_names)}
+
+    @staticmethod
+    def _get_graph_inout(graph_def):
+        input_names = []
+        inputs_set = set()
+        outputs_set = set()
+
+        for node in graph_def.node:
+            if node.op == 'Placeholder':
+                input_names.append(node.name)
+
+            for i in node.input:
+                inputs_set.add(i.split(':')[0])
+            outputs_set.add(node.name)
+
+        output_names = list(outputs_set - inputs_set)
+        return input_names, output_names
+
+    @staticmethod
+    def _get_input_shape(graph_def, input_name):
+        for node in graph_def.node:
+            if node.name == input_name:
+                return [dim.size for dim in node.attr['shape'].shape.dim][1:3]
+
+
+def print_outputs(outputs):
+  labelopen = open("labels.txt", 'r')
+  labels = [line.split(',') for line in labelopen.readlines()]
+  outputs = list(outputs.values())[0]
+  return str(labels[outputs[0].argmax()][0])
+
+
+def main(gambar):
+    m = pathlib.Path("model.pb")
+    #i = pathlib.Path(gambar)
+
+    model = Model(m)
+    outputs = model.predict(gambar)
+    return print_outputs(outputs)
+demo = gr.Interface(main, gr.Image(shape=(500, 500)), "text")
+demo.launch()

cvexport.manifest

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+{
+  "DomainType": "Classification",
+  "Platform": "TensorFlow",
+  "Flavor": null,
+  "ExporterVersion": "2.1",
+  "ExportedDate": "2023-01-03T08:46:55.194281Z",
+  "IterationId": "94a3200d-9b31-47ec-8e5a-078338a30274",
+  "ModelFileName": "model.pb",
+  "LabelFileName": "labels.txt",
+  "MetadataPropsFileName": "metadata_properties.json",
+  "ModelFileSHA1": "4d51fef7e4518d0762d87b4fc745c4014d54c83e",
+  "SchemaVersion": "1.0"
+}

labels.txt

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+Air Hockey
+American Football
+Ampute Football
+Archery
+Arm Wrestling
+Axe Throwing
+Balance Beam
+Barell Racing
+Baseball
+Basketball
+Baton Twirling
+Bike Polo
+Billiards
+BMX
+Bobsled
+Bowling
+Boxing
+Bull Riding
+Bungee Jumping
+Canoe Slamon
+CheerLeading
+Chuckwagon Racing
+Cricket
+Croquet
+Curling
+Disk Golf
+F1
+Fencing
+Field Hockey
+Figure Skating Men
+Figure Skating Pairs
+Figure Skating Women
+Fly Fishing
+Frisbee
+Gaga
+Giant Slalom
+Golf
+Hammer Throw
+hang gliding
+harness Racing
+High Jump
+Hockey
+Horse Jumping
+Horse Racing
+horsesoe pitching
+Hurdles
+Hydroplane Racing
+Ice Climbing
+Ice Yachting
+jai Alai
+javelin
+Jousting
+Judo
+Lacrosse
+Log Rolling
+Luge
+Motorcycle Racing
+mushing
+Nascar Racing
+Olympic Wrestling
+Parallel Bar
+Pole Climbing
+Pole Dancing
+Pole Vault
+Polo
+Pommel Horse
+Rings
+Rock Climbing
+Rolerblade racing
+Roller Derby
+rowing
+rugby
+Sailboat
+Shot Put
+Shuffleboard
+Sider Car Racing
+Ski Jumping
+Sky Surfing
+skydiving
+snow boarding
+snow mobile racing
+speed skating
+Steer Wrestling
+Sumo Wrestling
+Surfing
+Swimming
+Table Tennis
+Tennis
+Track Bicycle
+Trapeze
+Tug of War
+Ultimate
+Uneven Bars
+volley ball
+water cycling
+water polo
+weightlifting
+wheelchair basketball
+Wheelchair Racing
+wingsuit flying

metadata_properties.json

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+{
+    "CustomVision.Metadata.AdditionalModelInfo": "",
+    "CustomVision.Metadata.Version": "1.2",
+    "CustomVision.Postprocess.Method": "ClassificationMultiClass",
+    "CustomVision.Postprocess.Yolo.Biases": "[]",
+    "CustomVision.Postprocess.Yolo.NmsThreshold": "0.0",
+    "CustomVision.Preprocess.CropHeight": "0",
+    "CustomVision.Preprocess.CropMethod": "FullImageShorterSide",
+    "CustomVision.Preprocess.CropWidth": "0",
+    "CustomVision.Preprocess.MaxDimension": "0",
+    "CustomVision.Preprocess.MaxScale": "0.0",
+    "CustomVision.Preprocess.MinDimension": "0",
+    "CustomVision.Preprocess.MinScale": "0.0",
+    "CustomVision.Preprocess.NormalizeMean": "[0.0, 0.0, 0.0]",
+    "CustomVision.Preprocess.NormalizeStd": "[1.0, 1.0, 1.0]",
+    "CustomVision.Preprocess.ResizeMethod": "Stretch",
+    "CustomVision.Preprocess.TargetHeight": "300",
+    "CustomVision.Preprocess.TargetWidth": "300",
+    "Image.BitmapPixelFormat": "Rgb8",
+    "Image.ColorSpaceGamma": "SRGB",
+    "Image.NominalPixelRange": "Normalized_0_1"
+}

model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:422142a00891f51158e5b335ef9262e7a3f0b3d416526e30b797225e254ec52b
+size 45282442

requirements.txt

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+pillow
+gradio
+tensorflow
+numpy
+pathlib