--- a
+++ b/10x/batch_old.py
@@ -0,0 +1,65 @@
+import os
+
+import numpy as np
+
+import skimage as ski
+from skimage import io, filter, color, exposure, morphology, feature, measure, transform
+
+from sklearn.linear_model import BayesianRidge
+from sklearn.mixture import GMM
+
+from scipy import spatial, ndimage, signal, stats
+
+import matplotlib.pyplot as plt
+
+
+
+
+dirq = "data/"
+files = []
+for i in os.listdir(dirq) :
+    if i.endswith(".jpg") :
+        files.append(dirq + i)
+
+
+for f in files :
+
+	image = exposure.equalize_adapthist(io.imread(f))
+
+	binary = filter.threshold_adaptive(exposure.adjust_sigmoid(image[:, :, 0], cutoff=0.4, gain = 30), 301).astype(bool)
+	clean = morphology.binary_closing(binary, morphology.disk(3)).astype(bool)
+	clean = morphology.remove_small_objects(clean, 200)
+	clean = morphology.remove_small_objects( (1-clean).astype(bool), 200)
+
+	local_density = filter.gaussian_filter(clean, 61)
+
+	ent = filter.gaussian_filter(filter.rank.entropy(local_density, morphology.disk(3)), 75)
+
+	ent -= ent.min()
+	ent /= ent.max()
+
+	local_density -= local_density.min()
+	local_density /= local_density.max()
+
+	info = ent * (1 + local_density)
+
+	bw = (info) > filter.threshold_otsu(info)
+
+
+	C = measure.find_contours(bw, 0.5)
+	centroid = []
+	vals = []
+
+	for c in C :
+	    centroid.append(np.linalg.norm([c[:, 1].mean() - bw.shape[1] / 2, c[:, 0].mean() - bw.shape[0] / 2]))
+	    vals.append(local_density.T[c.astype(int)].sum())
+
+	cent = C[np.argmin(centroid / np.array(vals))]
+
+
+	fix, ax = plt.subplots()
+	plt.imshow(image)
+	ax.plot(cent[:, 1], cent[:, 0], lw=5, c="k", alpha = 0.7)
+
+	plt.savefig(f.split("Sheep")[1])
+	print ("Done " + f.split("Sheep")[1])