Modifications

Create a connection to ApertureDB

from aperturedb import Connector
from aperturedb import NotebookHelpers as nh

# ApertureDB Server Info for establishing connection
db_host  = "aperturedb.local"                       # assuming local installation as provided
user     = "admin"                                  # requires authentication
password = input("Please insert your password: ")   # use the password provided for the instance or "admin" by default

db = Connector.Connector(db_host, user=user, password=password)
print("Connected to ApertureDB server...")

# Pick an example video guid for use in subsequent queries
my_guid = "cc1c685b1feb6dc27931ce62d78838a6"   #fc8bd7bac259eb598b7b81410c0fbf3 #cc1c685b1feb6dc27931ce62d78838a6

Find a specific video

query = [{
    "FindVideo": {
        "constraints": {
            "guid": ["==", my_guid]
        },
        "blobs": True,
        "results": {
            "all_properties": True
        }
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())
nh.display_video_mp4(blobs[0])

Add new properties, modify the video itself

query = [{
    "FindVideo": {
        "_ref": 1,
        "constraints": {
            "guid": ["==", my_guid]
        },
        "results": {
            "all_properties": True
        },
        "blobs": False
    }
},
{
    "UpdateVideo": {                   # the speed of this operation depends on the size of the video itself
        "ref": 1,
        "properties": {
            "activity": "walking",       # this could actually be inferred with an activity recognition model
            "event": "street",
        },
        "operations": [{
            "type": "resize",
            "scale": 0.5
        }]
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())

# Verify it was updated
query = [{
    "FindVideo": {
        "constraints": {
            "guid": ["==", my_guid]
        },
        "blobs": True,
        "results": {
            "all_properties": True
        }
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())
nh.display_video_mp4(blobs[0])

Restore the updates

query = [{
    "FindVideo": {
        "_ref": 1,
        "constraints": {
            "guid": ["==", my_guid]
        },
        "results": {
            "all_properties": True
        },
        "blobs": False
    }
},
{
    "UpdateVideo": {
        "ref": 1,
        "remove_props": ["activity", "event"],
        "operations": [{
            "type": "resize",
            "scale": 2
        }]
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())

# Verify it is back as it was
query = [{
    "FindVideo": {
        "constraints": {
            "guid": ["==", my_guid]
        },
        "blobs": True,
        "results": {
            "all_properties": True
        }
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())
nh.display_video_mp4(blobs[0])

Upload a new video with associated metadata and connections

query = [{
    "FindEntity": {
        "_ref": 1,
        "with_class": "Camera",
        "constraints": {
            "id": ["==", 1234]                                         # id of Nikon camera
        },
        "results": {
            "all_properties": True
        }
    }
},{
    "AddVideo": {        
        "properties": {                                                # video metadata
            "guid": "e869b2342bd7ba30a49cba61d3de9dbnz",
            "frame_rate": 30,
            "start_timestamp": {"_date": "2021-08-30T00:00:00+00:00"}, # this application always captures videos at fixed time every day
            "end_timestamp": {"_date": "2021-08-30T23:59:59+00:00"},
            "activity": "launching",
            "event": "nasa-launch"
        },
        "connect": {                                                   # video object is connected to Camera object
            "ref": 1,
            "class": "captured_by"
        },
        "if_not_found": {                                              # conditional add based on guid check 
            "guid": ["==", "e869b2342bd7ba30a49cba61d3de9dbnz"]
        }
    }
}]

# Read the video itself to supply with AddVideo command
fd = open("sample_data/videos/SpaceShip3.mp4", 'rb')
video_arr = [ fd.read() ]
fd.close()

response, blobs = db.query(query, video_arr)

print(db.get_last_response_str())

Find the video that was captured by given camera during a given interval

query = [{
    "FindEntity": {                     # Find the given camera
        "_ref": 1,
        "with_class": "Camera",
        "constraints": {
            "id": ["==", 1234]
        },
        "results": {
            "list": ["brand", "model", "resolution"]
        }
    }
}, {
    "FindVideo": {                      # Find the video connected to the cameras in the given timeframe
        "_ref": 2,
        "is_connected_to": {
            "ref": 1
        },
        "blobs": True,
        "constraints": {
            "start_timestamp": [">=", {"_date": "2021-08-30T00:00:00+00:00"}], 
            "end_timestamp":   ["<=", {"_date": "2021-08-30T23:59:59+00:00"}]
        },
        "results": {
            "list": ["guid", "frame_rate", "start_timestamp"],
            "limit": 1
        }
    }
}]

response, blobs = db.query(query)
print(db.get_last_response_str())

nh.display_video_mp4(blobs[0])

Extract a specific clip from the video above

video_guid = response[1]["FindVideo"]["entities"][0]["guid"]
print(video_guid)
frame_rate = response[1]["FindVideo"]["entities"][0]["frame_rate"]
print(frame_rate)
start_ts = response[1]["FindVideo"]["entities"][0]["start_timestamp"]
print(start_ts)

# The API currently supports intervals using frame numbers which can be derived from certain parameters
# We are looking for a clip of 1 min, 2 mins after start of video
# As of now, interval operation on videos supports frame numbers. So we translate timing into frame numbers

query = [{
    "FindVideo": {
        "constraints": {
            "guid": ["==", "e869b2342bd7ba30a49cba61d3de9dbnz"]
        },
        "blobs": True,
        "results": {
            "all_properties": True,
            "limit": 1
        },
        "operations": [{        # Original videos are longer. We just want to sample and prepare them
            "type": "resize",   # performance depends on the video complexity
            "height": 300,
            "width":  300
        },{
            "type": "interval", # Interval Operation, performance depends on the video complexity
            "start": 1800,      # Start from 1 min (30fps * 60)
            "stop": 3600,       # Continue for a 1 min clip
            "step": 5           # Read every 5th frame
        }]
    }
}]

response, blobs = db.query(query)

print(db.get_last_response_str())

nh.display_video_mp4(blobs[0])