get sequence has for one average frame per cut, fixes #1391

pandora/sequence/extract.py

@@ -3,6 +3,8 @@
 from __future__ import division
 import Image
 import os
+import numpy
+import math
 
 ZONE_INDEX = []
 for pixel_index in range(64):
@@ -79,3 +81,63 @@ def get_sequences(path, position=0):
             sequences[mode][-1]['out'] = position
     return sequences, position
 
+def get_cut_sequences(stream, position=0):
+    path = stream.timeline_prefix
+    cuts = list(stream.cuts) + [stream.duration]
+    modes = ['color', 'shape']
+    sequences = {}
+    for mode in modes:
+        sequences[mode] = []
+    position_start = position
+    fps = 25
+    file_names = filter(lambda x: 'timelinedata8p' in x, os.listdir(path))
+    file_names = sorted(file_names, key=lambda x: int(x[14:-4]))
+    file_names = map(lambda x: path + x, file_names)
+
+    def add_hash(cut, cut_data):
+        if sequences['color']:
+            start = sequences['color'][-1]['out']
+        else:
+            start = 0
+        end = position
+        frames = int(math.ceil((end - start) * fps))
+        #print 'add', start, end, frames
+        if frames:
+            cut_data /= frames
+            frame_image = Image.new('RGB', (8,8))
+            frame_image.putdata(list(tuple(pixel) for pixel in cut_data))
+            for mode in modes:
+                frame_hash = get_hash(frame_image, mode)
+                if sequences[mode] and sequences[mode][-1]['hash'] == frame_hash:
+                     sequences[mode][-1]['out'] = end
+                else:
+                    sequences[mode].append({
+                        'hash': frame_hash,
+                        'in': start,
+                        'out': end,
+                    })
+        else:
+            print 'fixme', cut_data
+
+    def next_cut():
+        if cuts:
+            cut = cuts.pop(0)
+        else:
+            cut = 0
+        return numpy.array(Image.new('RGB', (8,8)).getdata(), numpy.int64), cut
+
+    cut_data, cut = next_cut()
+
+    for file_name in file_names:
+        timeline_image = Image.open(file_name)
+        timeline_width = timeline_image.size[0]
+        for x in range(0, timeline_width, 8):
+            frame_image = timeline_image.crop((x, 0, x + 8, 8))
+            cut_data += numpy.array(frame_image.getdata(), numpy.int64)
+            position += 1 / fps
+            if cut and position > cut:
+                add_hash(cut, cut_data)
+                cut_data, cut = next_cut()
+    position += 1 / fps
+    add_hash(cut, cut_data)
+    return sequences, position

pandora/sequence/tasks.py

@@ -13,7 +13,8 @@ def get_sequences(itemId):
     models.Sequence.objects.filter(sort=i.sort).delete()
     position = 0
     for stream in i.streams():
-        data, position = extract.get_sequences(stream.timeline_prefix, position)
+        #data, position = extract.get_sequences(stream.timeline_prefix, position)
+        data, position = extract.get_cut_sequences(stream, position)
         keys = None
         values = []
         for mode in data: