blakeblackshear.frigate/frigate/object_detection.py

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")