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README.md

@@ -1,3 +1,15 @@
----
-license: mit
----
+# Dataset for Reading Analog Dial
+
+dataset found at [Synanthropic/reading-analog-dial](https://huggingface.co/datasets/Synanthropic/reading-analog-dial)
+
+This is the dataset used to train the model for reading analog dial found at: [demo](https://huggingface.co/spaces/Synanthropic/reading-analog-dial)
+
+# Setup dataset for model training
+- there are two datasets, corner and keypoint
+1. download dataset
+2. unzip one of (corner, keypoint)
+3. edit make-dataset.py and set variable *srcdir* to one of corner or keypoint
+    - run script
+4. train your model
+
+

make-dataset.py

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+# 1.unzip file
+# 2. change *srcdir* 
+# 3. run this script
+# do training
+
+import numpy as np
+from pathlib import Path
+import os
+
+outdir = "datasets/data/"
+
+# setwhich one
+# srcdir = "corners"
+# srcdir = "keypoints"
+
+if srcdir=="keypoints":
+    _KEYS = "start_kp center end_kp tip".split()
+    DIM = 640
+elif srcdir=="corners":
+    _KEYS = "topLeft topRight bottomRight bottomLeft".split()
+    DIM = 1280
+
+F = os.listdir(srcdir) 
+
+import random
+random.seed(4)
+random.shuffle(F)
+F[0]
+
+# 80% training
+n = int(0.8*len(F))
+n
+train,val = F[:n], F[n:]
+
+import shutil
+
+for d in "images labels".split():
+    for dd in "train val test".split():
+        if not os.path.exists(f"{outdir}/{d}/{dd}"):
+            os.makedirs(f"{outdir}/{d}/{dd}")
+
+for trgd,F in [(f"{outdir}/images/train", train), (f"{outdir}/images/val", val), (f"{outdir}/images/test", val[:100])]:
+    for srcf in F:
+        shutil.move(os.path.join(srcdir, srcf), os.path.join(trgd, srcf))
+
+
+
+
+def filename2keypoints(fn:str):
+    kp = {}
+    i = 0
+    for sfn in fn.split("_"):
+        if "-" in sfn:
+            a,b = sfn.split("-")
+            kp[_KEYS[i]] = (int(a),int(b))
+            i+=1
+    return kp
+
+
+# <class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
+def formatLabel(cx,cy, w,h, kp):
+    pxs =""
+    for k in _KEYS:
+        x,y = kp[k]
+        pxs += f"{x/DIM} {y/DIM} "
+    return f"0 {cx/DIM} {cy/DIM} {w/DIM} {h/DIM} " +pxs.strip()
+
+if srcdir=="keypoints":
+    def toCenterCoordinates(kp,dim = DIM):
+        # trying to determine bounding box via cv isnt reliable so we assume, dial face will always appears nearly same dim. and determine box from center point
+        w, h = 560,560
+        x,y = kp["center"]
+        wh = w/2
+        hh = h/2
+
+        cx =  x-wh
+        cy = y - hh # left top corner
+
+        cx,cy, x,y
+        _w,_h = 0,0
+        # though this is not really correct, we will adjust dim. if bounding box is outside viewport
+        if cx<0:
+            _w = cx
+        elif x+wh > dim:
+            _w = (x+wh) - dim
+
+        if cy < 0:
+            _h = cy
+        elif (y+hh)>dim:
+            _h = (y+hh) - dim
+
+        return x,y, w-_w,h-_h
+else:
+    def toCenterCoordinates(kp,dim = DIM):
+        # bounding corners
+        c = np.array(list(kp.values()))
+        margin = 10
+        topLeft,bottomRight = c.min(axis=0)-margin, c.max(axis=0)+margin
+        topLeft,bottomRight =  np.clip(topLeft, 0, dim),np.clip(bottomRight, 0, dim)
+        cx,cy = 0.5*(bottomRight + topLeft)
+        w,h = bottomRight-topLeft
+        return cx,cy, w,h
+
+
+# make annotation file
+for d,F in [(f"{outdir}/images/train", train), (f"{outdir}/images/val", val), (f"{outdir}/images/test", val[:100])]:
+    for f in F:
+        _,ext = os.path.splitext(f)
+        antfn = os.path.join(outdir, d.replace("images/", "labels/"), _+".txt")
+        kp = filename2keypoints(f)
+        cx,cy,w,h = toCenterCoordinates(kp)
+        assert cx>0
+        assert cy>0
+        L = formatLabel(cx,cy,w,h, kp)
+        with open(antfn, "w") as fn:
+            fn.write(L+"\n")