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spanner3003 2024-07-13 19:37:59 +01:00
parent 78046c00cd
commit e9b0dc1040
1 changed files with 82 additions and 37 deletions
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119
frigate/detectors/plugins/hailo8l.py
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@ -1,7 +1,6 @@
import logging import logging
import numpy as np import numpy as np
import cv2 # Ensure you have OpenCV installed for resizing and color space conversion
from hailo_platform import ( from hailo_platform import (
HEF, HEF,
ConfigureParams, ConfigureParams,
@ -13,12 +12,12 @@ from hailo_platform import (
OutputVStreamParams, OutputVStreamParams,
VDevice, VDevice,
) )
from frigate.detectors.util import preprocess # Assuming this function is available
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from typing_extensions import Literal from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.detectors.util import preprocess # Assuming this function is available
# Set up logging # Set up logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -26,15 +25,18 @@ logger = logging.getLogger(__name__)
# Define the detector key for Hailo # Define the detector key for Hailo
DETECTOR_KEY = "hailo8l" DETECTOR_KEY = "hailo8l"
# Configuration class for model settings # Configuration class for model settings
class ModelConfig(BaseModel): class ModelConfig(BaseModel):
path: str = Field(default=None, title="Model Path") # Path to the HEF file path: str = Field(default=None, title="Model Path") # Path to the HEF file
# Configuration class for Hailo detector # Configuration class for Hailo detector
class HailoDetectorConfig(BaseDetectorConfig): class HailoDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY] # Type of the detector type: Literal[DETECTOR_KEY] # Type of the detector
device: str = Field(default="PCIe", title="Device Type") # Device type (e.g., PCIe) device: str = Field(default="PCIe", title="Device Type") # Device type (e.g., PCIe)
# Hailo detector class implementation # Hailo detector class implementation
class HailoDetector(DetectionApi): class HailoDetector(DetectionApi):
type_key = DETECTOR_KEY # Set the type key to the Hailo detector key type_key = DETECTOR_KEY # Set the type key to the Hailo detector key
@ -48,7 +50,7 @@ class HailoDetector(DetectionApi):
self.h8l_model_type = detector_config.model.model_type self.h8l_model_type = detector_config.model.model_type
self.h8l_tensor_format = detector_config.model.input_tensor self.h8l_tensor_format = detector_config.model.input_tensor
self.h8l_pixel_format = detector_config.model.input_pixel_format self.h8l_pixel_format = detector_config.model.input_pixel_format
output_type='FLOAT32' output_type = "FLOAT32"
logger.info(f"Initializing Hailo device as {self.h8l_device_type}") logger.info(f"Initializing Hailo device as {self.h8l_device_type}")
try: try:
@ -71,7 +73,8 @@ class HailoDetector(DetectionApi):
# Create input and output virtual stream parameters # Create input and output virtual stream parameters
self.input_vstreams_params = InputVStreamParams.make( self.input_vstreams_params = InputVStreamParams.make(
self.network_group, format_type=self.hef.get_input_vstream_infos()[0].format.type self.network_group,
format_type=self.hef.get_input_vstream_infos()[0].format.type,
) )
self.output_vstreams_params = OutputVStreamParams.make( self.output_vstreams_params = OutputVStreamParams.make(
self.network_group, format_type=getattr(FormatType, output_type) self.network_group, format_type=getattr(FormatType, output_type)
@ -86,7 +89,9 @@ class HailoDetector(DetectionApi):
logger.debug(f"[__init__] Input Tensor Format: {self.h8l_tensor_format}") logger.debug(f"[__init__] Input Tensor Format: {self.h8l_tensor_format}")
logger.debug(f"[__init__] Input Pixel Format: {self.h8l_pixel_format}") logger.debug(f"[__init__] Input Pixel Format: {self.h8l_pixel_format}")
logger.debug(f"[__init__] Input VStream Info: {self.input_vstream_info[0]}") logger.debug(f"[__init__] Input VStream Info: {self.input_vstream_info[0]}")
logger.debug(f"[__init__] Output VStream Info: {self.output_vstream_info[0]}") logger.debug(
f"[__init__] Output VStream Info: {self.output_vstream_info[0]}"
)
except HailoRTException as e: except HailoRTException as e:
logger.error(f"HailoRTException during initialization: {e}") logger.error(f"HailoRTException during initialization: {e}")
raise raise
@ -94,33 +99,43 @@ class HailoDetector(DetectionApi):
logger.error(f"Failed to initialize Hailo device: {e}") logger.error(f"Failed to initialize Hailo device: {e}")
raise raise
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
logger.debug("[detect_raw] Entering function") logger.debug("[detect_raw] Entering function")
logger.debug(f"[detect_raw] The `tensor_input` = {tensor_input} tensor_input shape = {tensor_input.shape}") logger.debug(
f"[detect_raw] The `tensor_input` = {tensor_input} tensor_input shape = {tensor_input.shape}"
)
if tensor_input is None: if tensor_input is None:
raise ValueError("[detect_raw] The 'tensor_input' argument must be provided") raise ValueError(
"[detect_raw] The 'tensor_input' argument must be provided"
)
# Ensure tensor_input is a numpy array # Ensure tensor_input is a numpy array
if isinstance(tensor_input, list): if isinstance(tensor_input, list):
tensor_input = np.array(tensor_input) tensor_input = np.array(tensor_input)
logger.debug(f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}") logger.debug(
f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}"
)
# Preprocess the tensor input using Frigate's preprocess function # Preprocess the tensor input using Frigate's preprocess function
processed_tensor = preprocess( processed_tensor = preprocess(
tensor_input, tensor_input, (1, self.h8l_model_height, self.h8l_model_width, 3), np.uint8
(1, self.h8l_model_height, self.h8l_model_width, 3), )
np.uint8 logger.debug(
f"[detect_raw] Tensor data and shape after preprocessing: {processed_tensor} {processed_tensor.shape}"
) )
logger.debug(f"[detect_raw] Tensor data and shape after preprocessing: {processed_tensor} {processed_tensor.shape}")
input_data = processed_tensor input_data = processed_tensor
logger.debug(f"[detect_raw] Input data for inference shape: {processed_tensor.shape}, dtype: {processed_tensor.dtype}") logger.debug(
f"[detect_raw] Input data for inference shape: {processed_tensor.shape}, dtype: {processed_tensor.dtype}"
)
try: try:
with InferVStreams(self.network_group, self.input_vstreams_params, self.output_vstreams_params) as infer_pipeline: with InferVStreams(
self.network_group,
self.input_vstreams_params,
self.output_vstreams_params,
) as infer_pipeline:
input_dict = {} input_dict = {}
if isinstance(input_data, dict): if isinstance(input_data, dict):
input_dict = input_data input_dict = input_data
@ -135,28 +150,40 @@ class HailoDetector(DetectionApi):
logger.debug("[detect_raw] converted from an array.") logger.debug("[detect_raw] converted from an array.")
input_dict[self.input_vstream_info[0].name] = input_data input_dict[self.input_vstream_info[0].name] = input_data
logger.debug(f"[detect_raw] Input dictionary for inference keys: {input_dict.keys()}") logger.debug(
f"[detect_raw] Input dictionary for inference keys: {input_dict.keys()}"
)
with self.network_group.activate(self.network_group_params): with self.network_group.activate(self.network_group_params):
raw_output = infer_pipeline.infer(input_dict) raw_output = infer_pipeline.infer(input_dict)
logger.debug(f"[detect_raw] Raw inference output: {raw_output}") logger.debug(f"[detect_raw] Raw inference output: {raw_output}")
if self.output_vstream_info[0].name not in raw_output: if self.output_vstream_info[0].name not in raw_output:
logger.error(f"[detect_raw] Missing output stream {self.output_vstream_info[0].name} in inference results") logger.error(
f"[detect_raw] Missing output stream {self.output_vstream_info[0].name} in inference results"
)
return np.zeros((20, 6), np.float32) return np.zeros((20, 6), np.float32)
raw_output = raw_output[self.output_vstream_info[0].name][0] raw_output = raw_output[self.output_vstream_info[0].name][0]
logger.debug(f"[detect_raw] Raw output for stream {self.output_vstream_info[0].name}: {raw_output}") logger.debug(
f"[detect_raw] Raw output for stream {self.output_vstream_info[0].name}: {raw_output}"
)
# Process the raw output # Process the raw output
detections = self.process_detections(raw_output) detections = self.process_detections(raw_output)
if len(detections) == 0: if len(detections) == 0:
logger.debug(f"[detect_raw] No detections found after processing. Setting default values.") logger.debug(
"[detect_raw] No detections found after processing. Setting default values."
)
return np.zeros((20, 6), np.float32) return np.zeros((20, 6), np.float32)
else: else:
formatted_detections = detections formatted_detections = detections
if formatted_detections.shape[1] != 6: # Ensure the formatted detections have 6 columns if (
logger.error(f"[detect_raw] Unexpected shape for formatted detections: {formatted_detections.shape}. Expected (20, 6).") formatted_detections.shape[1] != 6
): # Ensure the formatted detections have 6 columns
logger.error(
f"[detect_raw] Unexpected shape for formatted detections: {formatted_detections.shape}. Expected (20, 6)."
)
return np.zeros((20, 6), np.float32) return np.zeros((20, 6), np.float32)
return formatted_detections return formatted_detections
except HailoRTException as e: except HailoRTException as e:
@ -176,45 +203,63 @@ class HailoDetector(DetectionApi):
for i, detection_set in enumerate(raw_detections): for i, detection_set in enumerate(raw_detections):
if not isinstance(detection_set, np.ndarray) or detection_set.size == 0: if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
logger.debug(f"[process_detections] Detection set {i} is empty or not an array, skipping.") logger.debug(
f"[process_detections] Detection set {i} is empty or not an array, skipping."
)
continue continue
logger.debug(f"[process_detections] Detection set {i} shape: {detection_set.shape}") logger.debug(
f"[process_detections] Detection set {i} shape: {detection_set.shape}"
)
for detection in detection_set: for detection in detection_set:
if detection.shape[0] == 0: if detection.shape[0] == 0:
logger.debug(f"[process_detections] Detection in set {i} is empty, skipping.") logger.debug(
f"[process_detections] Detection in set {i} is empty, skipping."
)
continue continue
ymin, xmin, ymax, xmax = detection[:4] ymin, xmin, ymax, xmax = detection[:4]
score = np.clip(detection[4], 0, 1) # Use np.clip for clarity score = np.clip(detection[4], 0, 1) # Use np.clip for clarity
if score < threshold: if score < threshold:
logger.debug(f"[process_detections] Detection in set {i} has a score {score} below threshold {threshold}. Skipping.") logger.debug(
continue f"[process_detections] Detection in set {i} has a score {score} below threshold {threshold}. Skipping."
)
continue
logger.debug(f"[process_detections] Adding detection with coordinates: ({xmin}, {ymin}), ({xmax}, {ymax}) and score: {score}") logger.debug(
f"[process_detections] Adding detection with coordinates: ({xmin}, {ymin}), ({xmax}, {ymax}) and score: {score}"
)
boxes.append([ymin, xmin, ymax, xmax]) boxes.append([ymin, xmin, ymax, xmax])
scores.append(score) scores.append(score)
classes.append(i) classes.append(i)
num_detections += 1 num_detections += 1
logger.debug(f"[process_detections] Boxes: {boxes}, Scores: {scores}, Classes: {classes}, Num detections: {num_detections}") logger.debug(
f"[process_detections] Boxes: {boxes}, Scores: {scores}, Classes: {classes}, Num detections: {num_detections}"
)
if num_detections == 0: if num_detections == 0:
logger.debug("[process_detections] No valid detections found.") logger.debug("[process_detections] No valid detections found.")
return np.zeros((20, 6), np.float32) return np.zeros((20, 6), np.float32)
combined = np.hstack(( combined = np.hstack(
np.array(classes)[:, np.newaxis], (
np.array(scores)[:, np.newaxis], np.array(classes)[:, np.newaxis],
np.array(boxes) np.array(scores)[:, np.newaxis],
)) np.array(boxes),
)
)
if combined.shape[0] < 20: if combined.shape[0] < 20:
padding = np.zeros((20 - combined.shape[0], combined.shape[1]), dtype=combined.dtype) padding = np.zeros(
(20 - combined.shape[0], combined.shape[1]), dtype=combined.dtype
)
combined = np.vstack((combined, padding)) combined = np.vstack((combined, padding))
logger.debug(f"[process_detections] Combined detections (padded to 20 if necessary): {np.array_str(combined, precision=4, suppress_small=True)}") logger.debug(
f"[process_detections] Combined detections (padded to 20 if necessary): {np.array_str(combined, precision=4, suppress_small=True)}"
)
return combined[:20, :6] return combined[:20, :6]