script/tools/CameraTools.py

@@ -0,0 +1,128 @@
+import json
+import java.math.BigInteger as BigInteger
+import org.python.core.PyLong as PyLong
+import org.python.core.PyFloat as PyFloat
+import traceback
+import datetime
+
+run("Devices/Elements")
+
+def get_processing_parameters(stream_value):
+    return json.loads(stream_value.getValue("processing_parameters"))  
+
+def _create_tables(paths, stream_value, data_type, shape, images):
+    root = paths["root"]
+    create_dataset(paths["image"], data_type, dimensions = [images, shape[0], shape[1]])
+    create_dataset(paths["pid"], 'l', dimensions = [images])
+    create_dataset(paths["timestamp_str"], 's', dimensions = [images])
+    for id in stream_value.identifiers:
+        val = stream_value.getValue(id)
+        if id == "image":
+            pass
+        elif id == "processing_parameters":                                                                     
+            val = json.loads(val)                
+            for key in val.keys():
+                if val[key] is not None and isinstance(val[key], dict):    
+                    for k in val[key].keys():
+                        set_attribute(paths["image"], key + " " + k , "" if val[key][k] is None else val[key][k] )               
+                else:  
+                    set_attribute(paths["image"], key, "" if val[key] is None else val[key] )               
+        elif isinstance(val, PyArray):
+            create_dataset(root + id, 'd', dimensions = [images, len(val)])                                
+        elif isinstance(val, PyLong):
+            create_dataset(root + id, 'l', dimensions = [images])                
+        elif isinstance(val, PyFloat):
+            create_dataset(root + id, 'd', dimensions = [images])                
+        else:   
+            print "Unmanaged stream type: ", val, type(val)
+            pass
+    
+
+def _append_frame(paths, stream_value, index, data_type, shape):
+    print "Saving frame :", index
+    #append_dataset(paths["image"], data, index, type = data_type)    
+    root = paths["root"]
+    append_dataset(paths["image"], stream_value.getValue("image"),[index,0,0], type = data_type,  shape=[1, shape[0], shape[1]])      
+    append_dataset(paths["pid"], stream_value.getPulseId(), index)
+    append_dataset(paths["timestamp_str"], datetime.datetime.fromtimestamp(stream_value.timestampNanos/1e9).strftime('%Y-%m-%d %H:%M:%S.%f'), index)   
+
+    for id in stream_value.identifiers:
+        try:
+            val = stream_value.getValue(id)
+            if id == "image":
+                pass
+            elif isinstance(val, PyArray):
+                append_dataset(root + id, val, index)
+            elif isinstance(val, PyLong):
+                append_dataset(root + id, int(val), index)
+            elif isinstance(val, PyFloat):
+                append_dataset(root + id, float(val), index)
+            else:      
+                pass
+        except:
+            print id, val
+            traceback.print_exc()
+       
+    #print "Saved frame: ", index
+
+
+def _write_metadata(paths, camera, images, interval):
+    root = paths["root"]    
+    set_attribute(root, "Camera", camera)
+    set_attribute(root, "Images", images)
+    set_attribute(root, "Interval", interval)
+    cam_type = get_camera_type(camera)
+    set_attribute(root, "Type", cam_type)
+    if cam_type=="ELECTRONS":
+        set_attribute(root, "Screen", caget(camera + (":POSITION" if (camera.startswith("DSRM")) else ":GET_SCREEN1_POS"), 's'))
+        set_attribute(root, "Filter", caget(camera + ":GET_FILTER", 's'))
+
+    
+def save_camera_data(server, images = 1, interval = -1, root = "/camera1", parallel = True, pause = False):
+    stream_value = server.stream.take()
+    if stream_value is None:
+        server.waitNext(10000)
+        stream_value = server.stream.take()
+    if not root.endswith('/'):
+        root = root + "/"
+    camera = get_processing_parameters(stream_value)["camera_name"]
+    paths = {"root": root, "image":root+"image", "pid":root+"/pulse_id", "timestamp_str":root+"/timestamp_str"}    
+    shape = [stream_value.getValue("height"),stream_value.getValue("width")]
+    data_type = stream_value.getValue("image").typecode    
+    tasks = []  
+    if pause:
+        server.paused = True
+    try:    
+        for i in range(images):    
+            #print i
+            if i==0:                                
+                _create_tables(paths, stream_value, data_type, shape, images)                
+                _write_metadata(paths, camera, images, interval)            
+            start = time.time()
+            stream_value = server.stream.take()
+            if parallel:                                
+                tasks.extend( fork((_append_frame,(paths, stream_value, i, data_type, shape)),) )
+            else:
+                print "append"
+                _append_frame(paths, stream_value, i, data_type, shape)
+            print "OK"
+            if i< (images-1):
+                if interval<=0:
+                    print "WAITING"
+                    server.stream.waitCacheChange(10000)                            
+                else:
+                    print "Other"
+                    sleep_time = float(interval)/1000.0 - (time.time()-start)
+                    time.sleep(max(sleep_time,0))    
+    finally:      
+        if pause:
+            server.paused = False
+        pass
+    data_file = get_exec_pars().path
+    #print "Waiting finish : ", data_file
+    join(tasks)
+    #print "Done"
+    return data_file
+
+def save_camera_snapshot(server, file, format = "png"):
+    ImageBuffer.saveImage(server.output, file, "png")