Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity

Mostly working detection in a separate process

Browse Source
This commit is contained in:
Blake Blackshear 2020-01-30 20:12:29 -06:00
parent 3f34c57e31
commit 24cb3508e8
2 changed files with 100 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
Dockerfile
View File

@ -23,6 +23,7 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
# python-prctl \
numpy \
imutils \
SharedArray \
# Flask \
# paho-mqtt \
# PyYAML \
@ -50,7 +51,6 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
WORKDIR /opt/frigate/
ADD frigate frigate/
COPY detect_objects.py .
COPY start.py .
COPY benchmark.py .
CMD ["python3", "-u", "start.py"]
CMD ["python3", "-u", "benchmark.py"]

123
start_no_thread.py
View File

@ -9,6 +9,8 @@ import cv2
import imutils
import numpy as np
import subprocess as sp
import multiprocessing as mp
import SharedArray as sa
from scipy.spatial import distance as dist
import tflite_runtime.interpreter as tflite
from tflite_runtime.interpreter import load_delegate
@ -245,6 +247,19 @@ class ObjectDetector():
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
def detect_raw(self, tensor_input):
self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
self.interpreter.invoke()
boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
detections = np.zeros((20,6), np.float32)
for i, score in enumerate(scores):
detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
return detections
def detect(self, tensor_input, threshold=.4):
self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
@ -268,6 +283,63 @@ class ObjectDetector():
return detections
class RemoteObjectDetector():
def __init__(self, model, labels):
self.labels = load_labels(labels)
try:
sa.delete("frame")
except:
pass
try:
sa.delete("detections")
except:
pass
self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
self.detect_lock = mp.Lock()
self.detect_ready = mp.Event()
self.frame_ready = mp.Event()
def run_detector(model, labels, detect_ready, frame_ready):
object_detector = ObjectDetector(model, labels)
input_frame = sa.attach("frame")
detections = sa.attach("detections")
while True:
# signal that the process is ready to detect
detect_ready.set()
# wait until a frame is ready
frame_ready.wait()
# signal that the process is busy
detect_ready.clear()
frame_ready.clear()
detections[:] = object_detector.detect_raw(input_frame)
self.detect_process = mp.Process(target=run_detector, args=(model, labels, self.detect_ready, self.frame_ready))
self.detect_process.daemon = True
self.detect_process.start()
def detect(self, tensor_input, threshold=.4):
detections = []
with self.detect_lock:
self.input_frame[:] = tensor_input
# signal that a frame is ready
self.frame_ready.set()
# wait until the detection process is finished,
self.detect_ready.wait()
for d in self.detections:
if d[1] < threshold:
break
detections.append((
self.labels[int(d[0])],
float(d[1]),
(d[2], d[3], d[4], d[5])
))
return detections
class ObjectTracker():
def __init__(self, max_disappeared):
self.tracked_objects = {}
@ -421,6 +493,7 @@ def main():
frame = np.zeros(frame_shape, np.uint8)
motion_detector = MotionDetector(frame_shape, resize_factor=6)
object_detector = ObjectDetector('/lab/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite', '/lab/labelmap.txt')
# object_detector = RemoteObjectDetector('/lab/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite', '/lab/labelmap.txt')
# object_detector = ObjectDetector('/lab/detect.tflite', '/lab/labelmap.txt')
object_tracker = ObjectTracker(10)
@ -432,8 +505,8 @@ def main():
ffmpeg_cmd = (['ffmpeg'] +
['-hide_banner','-loglevel','panic'] +
['-hwaccel','vaapi','-hwaccel_device','/dev/dri/renderD129','-hwaccel_output_format','yuv420p'] +
['-i', '/debug/input/output.mp4'] +
# ['-i', '/debug/back-ali-jake.mp4'] +
# ['-i', '/debug/input/output.mp4'] +
['-i', '/debug/back-ali-jake.mp4'] +
['-f','rawvideo','-pix_fmt','rgb24'] +
['pipe:'])
@ -606,29 +679,29 @@ def main():
# if (frames >= 700 and frames <= 1635) or (frames >= 2500):
# if (frames >= 700 and frames <= 1000):
# if (frames >= 0):
# # row1 = cv2.hconcat([gray, cv2.convertScaleAbs(avg_frame)])
# # row2 = cv2.hconcat([frameDelta, thresh])
# # cv2.imwrite(f"/lab/debug/output/{frames}.jpg", cv2.vconcat([row1, row2]))
# # # cv2.imwrite(f"/lab/debug/output/resized-frame-{frames}.jpg", resized_frame)
# # for region in motion_regions:
# # cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (255,128,0), 2)
# # for region in object_regions:
# # cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,128,255), 2)
# for region in merged_regions:
# cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 2)
# for box in motion_boxes:
# cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255,0,0), 2)
# for detection in detections:
# box = detection[2]
# draw_box_with_label(frame, box[0], box[1], box[2], box[3], detection[0], f"{detection[1]*100}%")
# for obj in object_tracker.tracked_objects.values():
# box = obj['box']
# draw_box_with_label(frame, box[0], box[1], box[2], box[3], obj['label'], obj['id'], thickness=1, color=(0,0,255), position='bl')
# cv2.putText(frame, str(total_detections), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2)
# cv2.putText(frame, str(frame_detections), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2)
# cv2.imwrite(f"/lab/debug/output/frame-{frames}.jpg", frame)
# break
if (frames >= 0):
# row1 = cv2.hconcat([gray, cv2.convertScaleAbs(avg_frame)])
# row2 = cv2.hconcat([frameDelta, thresh])
# cv2.imwrite(f"/lab/debug/output/{frames}.jpg", cv2.vconcat([row1, row2]))
# # cv2.imwrite(f"/lab/debug/output/resized-frame-{frames}.jpg", resized_frame)
# for region in motion_regions:
# cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (255,128,0), 2)
# for region in object_regions:
# cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,128,255), 2)
for region in merged_regions:
cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 2)
for box in motion_boxes:
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255,0,0), 2)
for detection in detections:
box = detection[2]
draw_box_with_label(frame, box[0], box[1], box[2], box[3], detection[0], f"{detection[1]*100}%")
for obj in object_tracker.tracked_objects.values():
box = obj['box']
draw_box_with_label(frame, box[0], box[1], box[2], box[3], obj['label'], obj['id'], thickness=1, color=(0,0,255), position='bl')
cv2.putText(frame, str(total_detections), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2)
cv2.putText(frame, str(frame_detections), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2)
cv2.imwrite(f"/lab/debug/output/frame-{frames}.jpg", frame)
# break
duration = datetime.datetime.now().timestamp()-start
print(f"Processed {frames} frames for {duration:.2f} seconds and {(frames/duration):.2f} FPS.")