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Update app.py
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app.py
CHANGED
@@ -35,16 +35,6 @@ def load_model():
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detection_model = tf.saved_model.load(saved_model_dir)
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return detection_model
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# def load_model2():
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# wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
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# tarfile.open("balloon_model.tar.gz").extractall()
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# model_dir = 'saved_model'
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# detection_model = tf.saved_model.load(str(model_dir))
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# return detection_model
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# samples_folder = 'test_samples
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# image_path = 'test_samples/sample_balloon.jpeg
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#
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def predict(pilimg):
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@@ -116,6 +106,7 @@ def detect_video(video):
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# Release resources
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cap.release()
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REPO_ID = "apailang/mytfodmodel"
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detection_model = load_model()
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# pil_image = Image.open(image_path)
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@@ -124,21 +115,39 @@ detection_model = load_model()
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# predicted_img = predict(image_arr)
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# predicted_img.save('predicted.jpg')
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a = os.path.join(os.path.dirname(__file__), "data/a.mp4") # Video
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b = os.path.join(os.path.dirname(__file__), "data/b.mp4") # Video
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c = os.path.join(os.path.dirname(__file__), "data/c.mp4") # Video
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demo = gr.Interface(
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fn=detect_video
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inputs=gr.Video(),
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outputs=gr.Video(),
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examples=[
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@@ -150,6 +159,4 @@ demo = gr.Interface(
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)
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if __name__ == "__main__":
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demo.launch()
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detection_model = tf.saved_model.load(saved_model_dir)
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return detection_model
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def predict(pilimg):
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# Release resources
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cap.release()
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REPO_ID = "apailang/mytfodmodel"
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detection_model = load_model()
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# pil_image = Image.open(image_path)
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# predicted_img = predict(image_arr)
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# predicted_img.save('predicted.jpg')
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test1 = os.path.join(os.path.dirname(__file__), "data/test1.jpeg")
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test2 = os.path.join(os.path.dirname(__file__), "data/test2.jpeg")
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test3 = os.path.join(os.path.dirname(__file__), "data/test3.jpeg")
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test4 = os.path.join(os.path.dirname(__file__), "data/test4.jpeg")
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test5 = os.path.join(os.path.dirname(__file__), "data/test5.jpeg")
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test6 = os.path.join(os.path.dirname(__file__), "data/test6.jpeg")
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test7 = os.path.join(os.path.dirname(__file__), "data/test7.jpeg")
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test8 = os.path.join(os.path.dirname(__file__), "data/test8.jpeg")
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test9 = os.path.join(os.path.dirname(__file__), "data/test9.jpeg")
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test10 = os.path.join(os.path.dirname(__file__), "data/test10.jpeg")
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test11 = os.path.join(os.path.dirname(__file__), "data/test11.jpeg")
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test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg")
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gr.Interface(fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=gr.Image(type="pil"),
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title="Image Prediction Interface",
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description="Upload a Image for prediction",
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examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],],
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cache_examples=True
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).launch(share=True)
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a = os.path.join(os.path.dirname(__file__), "data/a.mp4") # Video
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b = os.path.join(os.path.dirname(__file__), "data/b.mp4") # Video
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c = os.path.join(os.path.dirname(__file__), "data/c.mp4") # Video
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basename = Path(video_in_file).stem
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video_out_file = os.path.join('data/detected' + '.mp4')
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samples_folder = 'test_samples'
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demo = gr.Interface(
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fn=lambda x: x, #detect_video
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inputs=gr.Video(),
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outputs=gr.Video(),
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examples=[
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)
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if __name__ == "__main__":
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demo.launch()
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