fixes
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matteo porta
parent
75abe24c54
commit
02cf4797eb
BIN
requirements.txt
BIN
requirements.txt
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@ -14,15 +14,16 @@ export QT_NO_WARNING_OUTPUT=0
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# export QT_QPA_PLATFORM=${XDG_SESSION_TYPE}
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python -B -u "./src/main.py" \
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--auto-login-admin \
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--auto-select \
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--no-autotest \
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--no-edgetpu \
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--no-gpu \
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--sim-camera \
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--sim-modbus \
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--sim-os-label-printer \
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--sim-serial-neo-pixels \
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--style windows \
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$* 2> >(sed $'s/.*/\e[31m&\e[m/' >&2) # &
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# --auto-select \
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# --about \
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# --archive \
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# --auto-login-user \
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@ -5,12 +5,11 @@ from configparser import ConfigParser
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from pathlib import Path
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import cv2
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import numpy
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import numpy as np
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import tensorflow as tf
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from lib.helpers.object_detection.utils import label_map_util
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from PyQt5.QtCore import (QFileSystemWatcher, QLineF, QMutex, QPointF, QRectF,
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Qt, QThread, QTimer, pyqtSignal)
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Qt, QThread, pyqtSignal)
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from PyQt5.QtGui import (QBrush, QColor, QFont, QImage, QPainter, QPainterPath,
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QPen, QPixmap)
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@ -20,27 +19,26 @@ from .consumer import Consumer
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if "--no-edgetpu" not in sys.argv:
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try:
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from pycoral.utils.edgetpu import make_interpreter
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except (ImportError, ModuleNotFoundError) as e:
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except (ImportError, ModuleNotFoundError):
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def make_interpreter(*args, **kwargs):
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raise ValueError(f"{e!r}")
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raise ValueError("the 'pycoral' module is not available")
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else:
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def make_interpreter(*args, **kwargs):
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raise ValueError("\"--no-edgetpu\" in sys.argv")
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if "--no-tflite" not in sys.argv:
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try:
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from pycoral.adapters import detect
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from tflite_runtime.interpreter import Interpreter
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except (ImportError, ModuleNotFoundError) as e:
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except (ImportError, ModuleNotFoundError):
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def Interpreter(*args, **kwargs):
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raise ValueError(f"{e!r}")
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raise ValueError("the 'tflite-runtime' module is not available")
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else:
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def Interpreter(*args, **kwargs):
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raise ValueError("\"--no-tflite\" in sys.argv")
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if "--no-gpu" in sys.argv:
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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#
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# # Patch the location of gfile
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# tf.gfile = tf.io.gfile
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@ -94,14 +92,14 @@ class Vision(Component):
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self.models_dir = Path(self.config[self.name].get("models_dir", "./data/neural_networks"))
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self.allowed_modes = dict.fromkeys([
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"edgetpu",
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"tflite_cpu",
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"tflite",
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"normal",
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])
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if "--no-edgetpu" in sys.argv:
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self.allowed_modes.pop("edgetpu", None)
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if "--no-tflite" in sys.argv:
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self.allowed_modes.pop("edgetpu", None)
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self.allowed_modes.pop("tflite_cpu", None)
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self.allowed_modes.pop("tflite", None)
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self.load_model(self.config[self.name].get("neural_network", None))
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# LOAD LABELS
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label_map = label_map_util.load_labelmap("./config/vision/labels/labels.pbtxt")
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@ -284,11 +282,11 @@ class Vision(Component):
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tf_mode = "edgetpu"
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except Exception:
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self.log.exception(traceback.format_exc())
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if tf_mode is None and "tflite_cpu" in self.allowed_modes:
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if tf_mode is None and "tflite" in self.allowed_modes:
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try:
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# create tflite cpu interpreter
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interpreter = Interpreter(str(self.models_dir / model_name / f"{model_name}.tflite"))
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tf_mode = "tflite_cpu"
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tf_mode = "tflite"
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except Exception:
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self.log.exception(traceback.format_exc())
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# reset tensorflow variables
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@ -306,19 +304,9 @@ class Vision(Component):
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self.tf_mode = tf_mode
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self.model_name = model_name
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if interpreter is not None:
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# if there is a new tflite interpreter initialize it and the related values
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self.tflite_input_details = interpreter.get_input_details()
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self.tflite_output_details = interpreter.get_output_details()
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self.inf_index = self.tflite_input_details[0]["index"]
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self.inf_shape = self.tflite_input_details[0]["shape"]
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# interpreter.resize_tensor_input(self.inf_index, self.inf_shape)
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# if there is a new tflite interpreter initialize it
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interpreter.allocate_tensors()
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interpreter.invoke() # warmup
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else:
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self.tflite_input_details = None
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self.tflite_output_details = None
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self.inf_index = None
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self.inf_shape = None
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self.interpreter = interpreter
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# if there is a new model to be used, remove previous model if present
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if model is not None and self.model is not None:
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@ -336,26 +324,46 @@ class Vision(Component):
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# "center": self.get_center(test_box),
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# "size": self.get_size(test_box),
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# }]
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if self.inf_shape is not None and frame.shape != self.inf_shape[1:3]:
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tensor = np.expand_dims(cv2.resize(frame, self.inf_shape[1:3], interpolation=cv2.INTER_LINEAR), axis=0)
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if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]:
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tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0)
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else:
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tensor = np.expand_dims(frame, axis=0)
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# Run inference
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if lock:
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self.lock.lock()
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if self.tf_mode in {"edgetpu", "tflite_cpu"}:
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self.interpreter.set_tensor(self.inf_index, tensor)
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if self.tf_mode in {"edgetpu", "tflite"}:
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i_d = self.interpreter.get_input_details()
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# print(i_d)
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o_d = self.interpreter.get_output_details()
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# print(o_d)
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self.interpreter.set_tensor(i_d[0]["index"], tensor)
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self.interpreter.invoke()
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objs = detect.get_objects(self.interpreter, self.detection_threshold, (1, 1))
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# PARSE TFLITE DETECTIONS
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# signature_list = self.interpreter._get_full_signature_list()
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# if signature_list:
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# if len(signature_list) > 1:
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# raise ValueError("Only support model with one signature.")
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# signature = signature_list[next(iter(signature_list))]
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# # count = int(self.interpreter.get_tensor(signature["outputs"]["output_0"])[0])
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# scores = self.interpreter.get_tensor(signature["outputs"]["output_1"])[0]
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# class_ids = self.interpreter.get_tensor(signature["outputs"]["output_2"])[0]
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# boxes = self.interpreter.get_tensor(signature["outputs"]["output_3"])[0]
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if self.interpreter.get_tensor(o_d[3]["index"]).size == 1:
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boxes = self.interpreter.get_tensor(o_d[0]["index"])[0]
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class_ids = self.interpreter.get_tensor(o_d[1]["index"])[0]
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scores = self.interpreter.get_tensor(o_d[2]["index"])[0]
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# count = int(self.interpreter.get_tensor(o_d[3]["index"])[0])
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else:
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scores = self.interpreter.get_tensor(o_d[0]["index"])[0]
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boxes = self.interpreter.get_tensor(o_d[1]["index"])[0]
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# count = int(self.interpreter.get_tensor(o_d[2]["index"])[0])
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class_ids = self.interpreter.get_tensor(o_d[3]["index"])[0]
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if lock:
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self.lock.unlock()
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boxes = [(obj.bbox.ymin / self.inf_shape[1], obj.bbox.xmin / self.inf_shape[2], obj.bbox.ymax / self.inf_shape[1], obj.bbox.xmax / self.inf_shape[2]) for obj in objs]
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classes = [obj.id + 1 for obj in objs]
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scores = [obj.score for obj in objs]
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detections = {
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"detection_boxes": [boxes],
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"detection_classes": [classes],
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"detection_scores": [scores],
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"detection_boxes": [boxes],
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"detection_classes": [map(lambda class_id: class_id + 1, class_ids)],
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}
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else:
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detections = self.model(tensor)
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@ -13,4 +13,5 @@ pyqt5
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pyserial
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requests
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tensorflow
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tflite-runtime
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zebra
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