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