v0.1 release

.gitattributes

@@ -2,7 +2,6 @@
 *.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
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
@@ -32,3 +31,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db filter=lfs diff=lfs merge=lfs -text
+face.json filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,3 @@
+venv
+flagged
+__pycache__

app.py

@@ -0,0 +1,88 @@
+import base64
+import gradio as gr
+
+from fastcore.all import *
+from fastai.vision.all import *
+import numpy as np
+import timm
+
+
+def parent_labels(o):
+    "Label `item` with the parent folder name."
+    return Path(o).parent.name.split(",")
+
+class LabelSmoothingBCEWithLogitsLossFlat(BCEWithLogitsLossFlat):
+    def __init__(self, eps:float=0.1, **kwargs):
+        self.eps = eps
+        super().__init__(thresh=0.1, **kwargs)
+    
+    def __call__(self, inp, targ, **kwargs):
+        targ_smooth = targ.float() * (1. - self.eps) + 0.5 * self.eps
+        return super().__call__(inp, targ_smooth, **kwargs)
+
+
+learn = load_learner('models.pkl')
+# set a new loss function with a threshold of 0.4 to remove more false positives
+learn.loss_func = BCEWithLogitsLossFlat(thresh=0.4)
+
+
+def predict(image, vtt):
+    vtt = base64.b64decode(vtt.replace("data:text/vtt;base64,", ""))
+    sprite = PILImage.create(image)
+    offsets = []
+    images = []
+    for left, top, right, bottom in getVTToffsets(vtt):
+        offsets.append((left, top, right, bottom))
+        cut_frame = sprite.crop((left, top, left + right, top + bottom))
+        images.append(PILImage.create(np.asarray(cut_frame)))
+
+    # create dataset
+    test_dl = learn.dls.test_dl(images, bs=64)
+    # get predictions
+    probabilities, _, activations = learn.get_preds(dl=test_dl, with_decoded=True)
+    # swivel into tags list from activations
+    tags = {}
+    for x, activation in enumerate(activations):
+        for idx, i in enumerate(activation):
+            if i:
+                tag = learn.dls.vocab[idx]
+                tag = tag.replace("_", " ")
+                if tag not in tags:
+                    tags[tag] = {'prob': 0, 'offset': ()}
+                prob = float(probabilities[x][idx])
+                if tags[tag]['prob'] < prob:
+                    tags[tag]['prob'] = prob
+                    tags[tag]['offset'] = offsets[x]
+
+    return tags
+
+
+def getVTToffsets(vtt):
+    left = top = right = bottom = None
+    for line in vtt.decode("utf-8").split("\n"):
+        line = line.strip()
+        if "xywh=" in line:
+            left, top, right, bottom = line.split("xywh=")[-1].split(",")
+            left, top, right, bottom = (
+                int(left),
+                int(top),
+                int(right),
+                int(bottom),
+            )
+        else:
+            continue
+
+        if not left:
+            continue
+
+        yield left, top, right, bottom
+
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(),
+        gr.Textbox(label="VTT file"),
+    ],
+    outputs=gr.JSON(label=""),
+).launch(enable_queue=True, server_name="0.0.0.0")

models.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b56270c11a4aad0963d314f20806e72b911440771777044dbc1ad5ad9de48e5e
+size 22972595

requirements.txt

@@ -0,0 +1,4 @@
+fastai==2.7.7
+numpy==1.21.5
+timm==0.6.7
+gradio==3.18