yawc

identify_behavior.py

@@ -0,0 +1,64 @@
+import os
+
+rtdir = '/srv/ftp/hummingbird/2021/07/21'
+#rtdir = os.getcwd()
+
+
+files_read = list()
+for cdir, _,  files in os.walk(rtdir):
+    for f in files:
+        if f.endswith('.json'):
+            files_read.append(os.path.join(cdir, f))
+
+
+
+import json
+
+fcontents = dict()
+
+for f in files_read:
+    with open(f) as ff:
+        fcontents[f] = json.load(ff)
+
+def compute_2d_scores(cbbs):
+    locs = list()
+    scores = list()
+    n_scores = 0
+    for cbb in cbbs:
+        if len(cbb['scores']) > 0:
+            n_scores+=1
+            first_box = cbb['boxes'][0]
+            first_score = cbb['scores'][0]
+
+            x_cent = np.mean(first_box[0::2])
+            y_cent = np.mean(first_box[1::2])
+            locs.append([x_cent, y_cent])
+            scores.append(first_score)
+
+    center = np.average(locs, weights=scores, axis=0)
+    stddev = np.average(np.linalg.norm(center - locs,axis=1),weights=scores)
+
+    return stddev, scores
+
+
+
+
+
+data = {'file':list(), 'stddev':list(),'weights':list()}
+for fname, contents in fcontents.items():
+    if len(contents) > 0:
+        data['file'].append(fname)
+        sdev, sc = compute_2d_scores(contents)
+        data['stddev'].append(sdev)
+        data['weights'].append(np.average(sc))
+
+
+import pandas as pd
+
+
+df = pd.DataFrame(data)
+fil = df.loc[(df['weights'] > 0.4) & (df['stddev'] > 400) ]
+
+
+
+