mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
import datetime
|
|
import time
|
|
import cv2
|
|
import threading
|
|
import numpy as np
|
|
from edgetpu.detection.engine import DetectionEngine
|
|
from . util import tonumpyarray
|
|
|
|
# Path to frozen detection graph. This is the actual model that is used for the object detection.
|
|
PATH_TO_CKPT = '/frozen_inference_graph.pb'
|
|
# List of the strings that is used to add correct label for each box.
|
|
PATH_TO_LABELS = '/label_map.pbtext'
|
|
|
|
# Function to read labels from text files.
|
|
def ReadLabelFile(file_path):
|
|
with open(file_path, 'r') as f:
|
|
lines = f.readlines()
|
|
ret = {}
|
|
for line in lines:
|
|
pair = line.strip().split(maxsplit=1)
|
|
ret[int(pair[0])] = pair[1].strip()
|
|
return ret
|
|
|
|
class PreppedQueueProcessor(threading.Thread):
|
|
def __init__(self, cameras, prepped_frame_queue):
|
|
|
|
threading.Thread.__init__(self)
|
|
self.cameras = cameras
|
|
self.prepped_frame_queue = prepped_frame_queue
|
|
|
|
# Load the edgetpu engine and labels
|
|
self.engine = DetectionEngine(PATH_TO_CKPT)
|
|
self.labels = ReadLabelFile(PATH_TO_LABELS)
|
|
|
|
def run(self):
|
|
# process queue...
|
|
while True:
|
|
frame = self.prepped_frame_queue.get()
|
|
|
|
# Actual detection.
|
|
objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=frame['region_threshold'], top_k=3)
|
|
# parse and pass detected objects back to the camera
|
|
parsed_objects = []
|
|
for obj in objects:
|
|
box = obj.bounding_box.flatten().tolist()
|
|
parsed_objects.append({
|
|
'frame_time': frame['frame_time'],
|
|
'name': str(self.labels[obj.label_id]),
|
|
'score': float(obj.score),
|
|
'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
|
|
'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
|
|
'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
|
|
'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
|
|
})
|
|
self.cameras[frame['camera_name']].add_objects(parsed_objects)
|
|
|
|
|
|
# should this be a region class?
|
|
class FramePrepper(threading.Thread):
|
|
def __init__(self, camera_name, shared_frame, frame_time, frame_ready,
|
|
frame_lock,
|
|
region_size, region_x_offset, region_y_offset, region_threshold,
|
|
prepped_frame_queue):
|
|
|
|
threading.Thread.__init__(self)
|
|
self.camera_name = camera_name
|
|
self.shared_frame = shared_frame
|
|
self.frame_time = frame_time
|
|
self.frame_ready = frame_ready
|
|
self.frame_lock = frame_lock
|
|
self.region_size = region_size
|
|
self.region_x_offset = region_x_offset
|
|
self.region_y_offset = region_y_offset
|
|
self.region_threshold = region_threshold
|
|
self.prepped_frame_queue = prepped_frame_queue
|
|
|
|
def run(self):
|
|
frame_time = 0.0
|
|
while True:
|
|
now = datetime.datetime.now().timestamp()
|
|
|
|
with self.frame_ready:
|
|
# if there isnt a frame ready for processing or it is old, wait for a new frame
|
|
if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
|
|
self.frame_ready.wait()
|
|
|
|
# make a copy of the cropped frame
|
|
with self.frame_lock:
|
|
cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
|
|
frame_time = self.frame_time.value
|
|
|
|
# Resize to 300x300 if needed
|
|
if cropped_frame.shape != (300, 300, 3):
|
|
cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
|
|
# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
|
|
frame_expanded = np.expand_dims(cropped_frame, axis=0)
|
|
|
|
# add the frame to the queue
|
|
if not self.prepped_frame_queue.full():
|
|
self.prepped_frame_queue.put({
|
|
'camera_name': self.camera_name,
|
|
'frame_time': frame_time,
|
|
'frame': frame_expanded.flatten().copy(),
|
|
'region_size': self.region_size,
|
|
'region_threshold': self.region_threshold,
|
|
'region_x_offset': self.region_x_offset,
|
|
'region_y_offset': self.region_y_offset
|
|
})
|
|
else:
|
|
print("queue full. moving on")
|