initial commit

README.md

@@ -1,5 +1,5 @@
 ---
-title: DeepPrivacy
+title: DeepPrivacy - A Generative Adversarial Network for Face Anonymization
 emoji: 🌍
 colorFrom: gray
 colorTo: blue

app.py

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+import os
+import gradio
+import numpy as np
+import torch
+import hashlib
+from PIL import Image
+import gradio.inputs
+from deep_privacy.build import build_anonymizer
+from deep_privacy.detection import ImageAnnotation
+from typing import List
+anonymizer = build_anonymizer()
+
+cached_detections = {}
+def anonymize(im: Image, truncation_value: float):
+    anonymizer.truncation_level = truncation_value
+    im = np.array(im.convert("RGB"))
+    md5_ = hashlib.md5(im.tobytes()).hexdigest()
+    if md5_ in cached_detections:
+        detections = cached_detections[md5_]
+    else:
+        detections: List[ImageAnnotation]  = anonymizer.detector.get_detections([im])
+        cached_detections[md5_] = detections
+    im = anonymizer.anonymize_images([im], detections)[0]
+    im = Image.fromarray(im)
+    return im
+
+
+iface = gradio.Interface(
+    anonymize, [gradio.inputs.Image(type="pil", label="Upload your image or try the example below!"), gradio.inputs.Slider(minimum=0, maximum=8, step=0.01, default=0.5, label="Truncation value (set to >0 to generate different bodies between runs)")],
+    examples=[["coco_val2017_000000001000.jpg", 0], ["turing-2018-bengio-hinton-lecun.jpg", 0]],
+    outputs="image",
+    title="DeepPrivacy: A Generative Adversarial Network for Face Anonymization",
+    description="A live demo of face anonymization with generative adversarial networks. See paper/code at: github.com/hukkelas/DeepPrivacy",
+    live=True)
+iface.launch()

requirements.txt

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+torch
+torchvision
+numpy
+cython
+matplotlib
+tqdm
+tflib
+autopep8
+opencv-python
+requests
+pyyaml
+addict
+scikit-image
+kornia
+torch_fidelity
+ninja
+moviepy