refactors into classes
- ImageMeta struct saves image metadata - DataHandler takes care of loading dataset and parsing the label information - YoloProcessing takes care of image preprocessing and yolo postprocessing - HailoHandler connects to Hailo device, pushes the desired network hef file and runs the dataset on the hailo chip
This commit is contained in:
raphael
554
inference.py
554
inference.py
@@ -1,317 +1,302 @@
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import os
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from multiprocessing import Process
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import json
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import os
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import time
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import numpy as np
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t
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from PIL import Image
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from detection_tools.utils.visualization_utils import \
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visualize_boxes_and_labels_on_image_array
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from hailo_platform import (ConfigureParams, FormatType, HEF,
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HailoStreamInterface, InferVStreams,
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InputVStreamParams, OutputVStreamParams,
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PcieDevice)
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import numpy as np
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import tensorflow as tf
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from tensorflow.image import combined_non_max_suppression
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from detection_tools.utils.visualization_utils import visualize_boxes_and_labels_on_image_array
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from hailo_platform import (HEF, PcieDevice, HailoStreamInterface, InferVStreams, ConfigureParams, \
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InputVStreamParams, OutputVStreamParams, InputVStreams, OutputVStreams, FormatType)
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# preprocess dataset for yolov5 size
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# yolov5 640x640
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# resnet18 320x320
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def preproc(image, output_height=640, output_width=640, resize_side=256):
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'''
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imagenet-standard: aspect-preserving resize to 256px smaller-side,
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then central-crop to 224px
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'''
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new_width = int(image.width/image.height*resize_side)
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new_height = resize_side
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x , y = (new_width-output_width)/2, 0
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# Select area to crop
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area = (x, y, x+output_width, y+output_height)
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# Crop, show, and save image
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cropped_img = image.resize((new_width, new_height)).crop(area)
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return cropped_img
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# Collect images from data files
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def dataset_read(hef):
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images_path = './minimal_data'
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names = []
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images_list = [img_name for img_name in os.listdir(images_path) if
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os.path.splitext(os.path.join(images_path, img_name))[1] == '.jpg']
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# Define dataset params
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input_vstream_info = hef.get_input_vstream_infos()[0]
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output_vstream_infos = hef.get_output_vstream_infos()
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image_height, image_width, channels = input_vstream_info.shape
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# dataset = np.zeros((len(images_list), image_height, image_width, channels),
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# dtype=np.float32)
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dataset = np.zeros((1, image_height, image_width, channels),
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dtype=np.float32)
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for idx, img_name in enumerate(images_list):
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img = Image.open(os.path.join(images_path, img_name))
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img_preproc = preproc(img)
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dataset[idx,:,:,:] = np.array(img_preproc)
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names.append(img_name)
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break
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return dataset, names
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# Generate random dataset
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def dataset_random(image_height, image_width, channels):
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num_of_images = 10
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low, high = 2, 20
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dataset = np.random.randint(low, high, (num_of_images, image_height,
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image_width, channels)).astype(np.float32)
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return dataset
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def init_hailo(model_name='yolov5m'):
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target = PcieDevice()
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hef_path = f'hef/{model_name}.hef'
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hef = HEF(hef_path)
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# Configure network groups
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configure_params = ConfigureParams.create_from_hef(hef=hef, interface=HailoStreamInterface.PCIe)
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network_groups = target.configure(hef, configure_params)
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network_group = network_groups[0]
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return hef, network_group
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class ImageMeta:
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def __init__(self, image_height, image_width, channels):
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self.image_height = image_height
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self.image_width = image_width
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self.channels = channels
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'''
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The target can be used as a context manager ("with" statement) to ensure it's released on time.
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Here it's avoided for the sake of simplicity
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'''
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def run_hailo(dataset, names, hef, network_group):
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# Create input and output virtual streams params
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# Quantized argument signifies whether or not the incoming data is already quantized.
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# Data is quantized by HailoRT if and only if quantized == False .
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input_vstreams_params = InputVStreamParams.make(network_group,
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quantized=False,
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format_type=FormatType.FLOAT32)
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# TODO: change to FLOAT32
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output_vstreams_params = OutputVStreamParams.make(network_group, quantized=False, format_type=FormatType.FLOAT32)
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# output_vstreams_params = OutputVStreamParams.make(network_group,
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# quantized=True,
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# format_type=FormatType.INT8)
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class DataHandler:
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def __init__(self, path, image_meta):
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self.images_path = path
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self.image_meta = image_meta
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def load_data(self, preprocess_fn):
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names = []
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images_list = [img_name for img_name in os.listdir(self.images_path)
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if os.path.splitext(os.path.join(self.images_path, img_name))[1] == '.jpg']
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dataset = np.zeros((1, self.image_meta.image_height,
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self.image_meta.image_width,
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self.image_meta.channels),
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dtype=np.float32)
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for idx, img_name in enumerate(images_list):
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img = Image.open(os.path.join(self.images_path, img_name))
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img_preproc = preprocess_fn(img)
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dataset[idx, :, :, :] = np.array(img_preproc)
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names.append(img_name)
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break
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self.dataset = dataset
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self.names = names
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input_vstream_info = hef.get_input_vstream_infos()[0]
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output_vstream_infos = hef.get_output_vstream_infos()
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input_data = {input_vstream_info.name: dataset}
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network_group_params = network_group.create_params()
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with InferVStreams(network_group, input_vstreams_params, output_vstreams_params) as infer_pipeline:
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with network_group.activate(network_group_params):
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infer_results = infer_pipeline.infer(input_data)
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out = [infer_results[i.name] for i in output_vstream_infos]
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return out, names, dataset, names
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def _get_coco_labels(self):
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coco_names = json.load(open(os.path.join(os.path.dirname(__file__), 'coco_names.json')))
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coco_names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in coco_names.items()}
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return coco_names
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# 20 x 20 -> 32
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# stride = 32
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def yolo_postprocess_numpy(net_out, anchors_for_stride, stride):
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"""
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net_out is shape: [N, 19, 19, 255] or [N, 38, 38, 255] or [N, 76, 76, 255]
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first we reshape it to be as in gluon and then follow gluon's shapes.
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output_ind = 0 for stride 32, 1 for stride 16, 2 for stride 8.
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"""
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def _get_labels(self, label_name):
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filename = os.path.join(os.path.dirname(__file__), label_name + '.json')
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names = json.load(open(filename))
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names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in names.items()}
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return names
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# net_out = net_out.astype(np.float32) / 256
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num_classes = 4
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BS = net_out.shape[0] # batch size
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H = net_out.shape[1]
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W = net_out.shape[2]
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num_anchors = anchors_for_stride.size // 2 # 2 params for each anchor.
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num_pred = 1 + 4 + num_classes # 2 box centers, 2 box scales, 1 objness, num_classes class scores
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alloc_size = (128, 128)
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grid_x = np.arange(alloc_size[1])
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grid_y = np.arange(alloc_size[0])
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grid_x, grid_y = np.meshgrid(grid_x, grid_y) # dims [128,128], [128,128]
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offsets = np.concatenate((grid_x[:, :, np.newaxis], grid_y[:, :, np.newaxis]), axis=-1) # dim [128,128,2]
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offsets = np.expand_dims(np.expand_dims(offsets, 0), 0) # dim [1,1,128,128,2]
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pred = net_out.transpose((0, 3, 1, 2)) # now dims are: [N,C,H,W] as in Gluon.
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pred = np.reshape(pred, (BS, num_anchors * num_pred, -1)) # dim [N, 255, HxW]
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# dim [N, 361, 255], we did it so that the 255 be the last dim and can be reshaped.
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pred = pred.transpose((0, 2, 1))
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pred = np.reshape(pred, (BS, -1, num_anchors, num_pred)) # dim [N, HxW, 3, 85]]
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raw_box_centers = pred[:, :, :, 0:2] # dim [N, HxW, 3, 2]
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raw_box_scales = pred[:, :, :, 2:4] # dim [N,HxW, 3, 2]
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objness = pred[:, :, :, 4:5] # dim [N, HxW, 3, 1]
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class_pred = pred[:, :, :, 5:] # dim [N, HxW, 3, 80]
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offsets = offsets[:, :, :H, :W, :] # dim [1, 1, H, W, 2]
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offsets = np.reshape(offsets, (1, -1, 1, 2)) # dim [1, HxW, 1, 2]
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box_centers, box_scales, confidence, class_pred = _yolo5_decode(
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raw_box_centers=raw_box_centers,
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raw_box_scales=raw_box_scales,
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objness=objness,
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class_pred=class_pred,
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anchors_for_stride=anchors_for_stride,
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offsets=offsets,
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stride=stride)
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class_score = class_pred * confidence # dim [N, HxW, 3, 80]
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wh = box_scales / 2.0
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# dim [N, HxW, 3, 4]. scheme xmin, ymin, xmax, ymax
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bbox = np.concatenate((box_centers - wh, box_centers + wh), axis=-1)
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detection_boxes = np.reshape(bbox, (BS, -1, 1, 4)) # dim [N, num_detections, 1, 4]
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detection_scores = np.reshape(class_score, (BS, -1, num_classes)) # dim [N, num_detections, 80]
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# switching scheme from xmin, ymin, xmanx, ymax to ymin, xmin, ymax, xmax:
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detection_boxes_tmp = np.zeros(detection_boxes.shape)
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detection_boxes_tmp[:, :, :, 0] = detection_boxes[:, :, :, 1]
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detection_boxes_tmp[:, :, :, 1] = detection_boxes[:, :, :, 0]
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detection_boxes_tmp[:, :, :, 2] = detection_boxes[:, :, :, 3]
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detection_boxes_tmp[:, :, :, 3] = detection_boxes[:, :, :, 2]
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detection_boxes = detection_boxes_tmp # now scheme is: ymin, xmin, ymax, xmax
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return detection_boxes.astype(np.float32), detection_scores.astype(np.float32)
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def _yolo5_decode(raw_box_centers, raw_box_scales, objness, class_pred, anchors_for_stride, offsets, stride):
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box_centers = (raw_box_centers * 2. - 0.5 + offsets) * stride
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box_scales = (raw_box_scales * 2) ** 2 * anchors_for_stride # dim [N, HxW, 3, 2]
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return box_centers, box_scales, objness, class_pred
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def postprocessing(endnodes):
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"""
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endnodes is a list of 3 output tensors:
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endnodes[0] - stride 32 of input
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endnodes[1] - stride 16 of input
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endnodes[2] - stride 8 of input
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Returns:
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a tensor with dims: [BS, Total_num_of_detections_in_image, 6]
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where:
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total_num_of_detections_in_image = H*W*((1/32^2) + (1/16^2) + (1/8^2))*num_anchors*num_classes,
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with H, W as input dims.
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If H=W=608, num_anchors=3, num_classes=80 (coco 2017), we get:
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total_num_of_detections = 1819440 ~ 1.8M detections per image for the NMS
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"""
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H_input = 640
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W_input = 640
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anchors_list = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
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# TODO make prettier
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strides = [8, 16, 32]
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num_classes = 80
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for output_ind, output_branch in enumerate(endnodes): # iterating over the output layers:
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stride = strides[::-1][output_ind]
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anchors_for_stride = np.array(anchors_list[::-1][output_ind])
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anchors_for_stride = np.reshape(anchors_for_stride, (1, 1, -1, 2)) # dim [1, 1, 3, 2]
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detection_boxes, detection_scores = yolo_postprocess_numpy(output_branch,
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anchors_for_stride,
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stride)
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# detection_boxes is a [BS, num_detections, 1, 4] tensor, detection_scores is a
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# [BS, num_detections, num_classes] tensor
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detection_boxes = detection_boxes / H_input # normalization of box coordinates to 1
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BS = endnodes[0].shape[0]
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H = H_input // stride
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W = W_input // stride
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num_anchors = anchors_for_stride.size // 2
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num_detections = H * W * num_anchors
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# detection_boxes.set_shape((BS, num_detections, 1, 4))
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# detection_scores.set_shape((BS, num_detections, num_classes))
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# concatenating the detections from the different output layers:
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if output_ind == 0:
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detection_boxes_full = detection_boxes
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detection_scores_full = detection_scores
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else:
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detection_boxes_full = tf.concat([detection_boxes_full, detection_boxes], axis=1)
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detection_scores_full = tf.concat([detection_scores_full, detection_scores], axis=1)
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score_threshold = 0.5
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nms_iou_threshold = 0.5
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labels_offset = 1
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(nmsed_boxes, nmsed_scores, nmsed_classes, num_detections) = \
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combined_non_max_suppression(boxes=detection_boxes_full,
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scores=detection_scores_full,
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score_threshold=score_threshold,
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iou_threshold=nms_iou_threshold,
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max_output_size_per_class=100,
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max_total_size=100)
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COCO_17_14 = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9,
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10: 10, 11: 11, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18,
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18: 19, 19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 27,
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26: 28, 27: 31, 28: 32, 29: 33, 30: 34, 31: 35, 32: 36, 33: 37,
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34: 38, 35: 39, 36: 40, 37: 41, 38: 42, 39: 43, 40: 44, 41: 46,
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42: 47, 43: 48, 44: 49, 45: 50, 46: 51, 47: 52, 48: 53, 49: 54,
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50: 55, 51: 56, 52: 57, 53: 58, 54: 59, 55: 60, 56: 61, 57: 62,
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58: 63, 59: 64, 60: 65, 61: 67, 62: 70, 63: 72, 64: 73, 65: 74,
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66: 75, 67: 76, 68: 77, 69: 78, 70: 79, 71: 80, 72: 81, 73: 82,
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74: 84, 75: 85, 76: 86, 77: 87, 78: 88, 79: 89, 80: 90}
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# adding offset to the class prediction and cast to integer
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def translate_coco_2017_to_2014(nmsed_classes):
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return np.vectorize(COCO_2017_TO_2014_TRANSLATION.get)(nmsed_classes).astype(np.int32)
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class YoloProcessing:
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def __init__(self, imageMeta, classes):
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self.output_height = imageMeta.image_height
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self.output_width = imageMeta.image_width
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self.classes = classes
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nmsed_classes = tf.cast(tf.add(nmsed_classes, labels_offset), tf.int16)
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nmsed_classes = translate_coco_2017_to_2014(nmsed_classes)
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def preproc(self, image, resize_side=256):
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return {'detection_boxes': nmsed_boxes,
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'detection_scores': nmsed_scores,
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'detection_classes': nmsed_classes,
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'num_detections': num_detections}
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'''
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imagenet-standard: aspect-preserving resize to 256px smaller-side,
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then central-crop to 224px
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'''
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new_width = int(image.width/image.height*resize_side)
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new_height = resize_side
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x, y = (new_width-self.output_width)/2, 0
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# Select area to crop
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area = (x, y, x+self.output_width, y+self.output_height)
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# Crop, show, and save image
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cropped_img = image.resize((new_width, new_height)).crop(area)
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return cropped_img
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# 20 x 20 -> 32
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# stride = 32
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def yolo_postprocess_numpy(self, net_out, anchors_for_stride, stride):
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"""
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net_out is shape: [N, 19, 19, 255] or [N, 38, 38, 255] or [N, 76, 76, 255]
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first we reshape it to be as in gluon and then follow gluon's shapes.
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output_ind = 0 for stride 32, 1 for stride 16, 2 for stride 8.
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"""
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# net_out = net_out.astype(np.float32) / 256
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num_classes = 4
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BS = net_out.shape[0] # batch size
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H = net_out.shape[1]
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W = net_out.shape[2]
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num_anchors = anchors_for_stride.size // 2 # 2 params for each anchor.
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num_pred = 1 + 4 + num_classes # 2 box centers, 2 box scales, 1 objness, num_classes class scores
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alloc_size = (128, 128)
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grid_x = np.arange(alloc_size[1])
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grid_y = np.arange(alloc_size[0])
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grid_x, grid_y = np.meshgrid(grid_x, grid_y) # dims [128,128], [128,128]
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offsets = np.concatenate((grid_x[:, :, np.newaxis], grid_y[:, :, np.newaxis]), axis=-1) # dim [128,128,2]
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offsets = np.expand_dims(np.expand_dims(offsets, 0), 0) # dim [1,1,128,128,2]
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pred = net_out.transpose((0, 3, 1, 2)) # now dims are: [N,C,H,W] as in Gluon.
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pred = np.reshape(pred, (BS, num_anchors * num_pred, -1)) # dim [N, 255, HxW]
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# dim [N, 361, 255], we did it so that the 255 be the last dim and can be reshaped.
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pred = pred.transpose((0, 2, 1))
|
||||
pred = np.reshape(pred, (BS, -1, num_anchors, num_pred)) # dim [N, HxW, 3, 85]]
|
||||
|
||||
raw_box_centers = pred[:, :, :, 0:2] # dim [N, HxW, 3, 2]
|
||||
raw_box_scales = pred[:, :, :, 2:4] # dim [N,HxW, 3, 2]
|
||||
|
||||
objness = pred[:, :, :, 4:5] # dim [N, HxW, 3, 1]
|
||||
class_pred = pred[:, :, :, 5:] # dim [N, HxW, 3, 80]
|
||||
offsets = offsets[:, :, :H, :W, :] # dim [1, 1, H, W, 2]
|
||||
offsets = np.reshape(offsets, (1, -1, 1, 2)) # dim [1, HxW, 1, 2]
|
||||
box_centers, box_scales, confidence, class_pred = self._yolo5_decode(
|
||||
raw_box_centers=raw_box_centers,
|
||||
raw_box_scales=raw_box_scales,
|
||||
objness=objness,
|
||||
class_pred=class_pred,
|
||||
anchors_for_stride=anchors_for_stride,
|
||||
offsets=offsets,
|
||||
stride=stride)
|
||||
|
||||
class_score = class_pred * confidence # dim [N, HxW, 3, 80]
|
||||
wh = box_scales / 2.0
|
||||
# dim [N, HxW, 3, 4]. scheme xmin, ymin, xmax, ymax
|
||||
bbox = np.concatenate((box_centers - wh, box_centers + wh), axis=-1)
|
||||
|
||||
detection_boxes = np.reshape(bbox, (BS, -1, 1, 4)) # dim [N, num_detections, 1, 4]
|
||||
detection_scores = np.reshape(class_score, (BS, -1, num_classes)) # dim [N, num_detections, 80]
|
||||
|
||||
# switching scheme from xmin, ymin, xmanx, ymax to ymin, xmin, ymax, xmax:
|
||||
detection_boxes_tmp = np.zeros(detection_boxes.shape)
|
||||
detection_boxes_tmp[:, :, :, 0] = detection_boxes[:, :, :, 1]
|
||||
detection_boxes_tmp[:, :, :, 1] = detection_boxes[:, :, :, 0]
|
||||
detection_boxes_tmp[:, :, :, 2] = detection_boxes[:, :, :, 3]
|
||||
detection_boxes_tmp[:, :, :, 3] = detection_boxes[:, :, :, 2]
|
||||
|
||||
detection_boxes = detection_boxes_tmp # now scheme is: ymin, xmin, ymax, xmax
|
||||
return detection_boxes.astype(np.float32), detection_scores.astype(np.float32)
|
||||
|
||||
def _yolo5_decode(self, raw_box_centers, raw_box_scales, objness, class_pred, anchors_for_stride, offsets, stride):
|
||||
box_centers = (raw_box_centers * 2. - 0.5 + offsets) * stride
|
||||
box_scales = (raw_box_scales * 2) ** 2 * anchors_for_stride # dim [N, HxW, 3, 2]
|
||||
return box_centers, box_scales, objness, class_pred
|
||||
|
||||
|
||||
def _get_face_detection_visualization_data(logits):
|
||||
boxes = logits['detection_boxes'][0]
|
||||
def postprocessing(self, endnodes):
|
||||
"""
|
||||
endnodes is a list of 3 output tensors:
|
||||
endnodes[0] - stride 32 of input
|
||||
endnodes[1] - stride 16 of input
|
||||
endnodes[2] - stride 8 of input
|
||||
Returns:
|
||||
a tensor with dims: [BS, Total_num_of_detections_in_image, 6]
|
||||
where:
|
||||
total_num_of_detections_in_image = H*W*((1/32^2) + (1/16^2) + (1/8^2))*num_anchors*num_classes,
|
||||
with H, W as input dims.
|
||||
If H=W=608, num_anchors=3, num_classes=80 (coco 2017), we get:
|
||||
total_num_of_detections = 1819440 ~ 1.8M detections per image for the NMS
|
||||
"""
|
||||
H_input = 640
|
||||
W_input = 640
|
||||
anchors_list = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
|
||||
# TODO make prettier
|
||||
strides = [8, 16, 32]
|
||||
|
||||
face_landmarks = logits.get('face_landmarks')
|
||||
if face_landmarks is not None:
|
||||
face_landmarks = face_landmarks[0].reshape((-1, 5, 2))[:, :, (1, 0)]
|
||||
boxes = boxes[:, (1, 0, 3, 2)]
|
||||
# No name to prevent clobbering the visualization
|
||||
labels = {1: {'id': 1, 'name': ''}}
|
||||
return boxes, labels, face_landmarks
|
||||
for output_ind, output_branch in enumerate(endnodes): # iterating over the output layers:
|
||||
stride = strides[::-1][output_ind]
|
||||
anchors_for_stride = np.array(anchors_list[::-1][output_ind])
|
||||
anchors_for_stride = np.reshape(anchors_for_stride, (1, 1, -1, 2)) # dim [1, 1, 3, 2]
|
||||
|
||||
detection_boxes, detection_scores = self.yolo_postprocess_numpy(output_branch,
|
||||
anchors_for_stride,
|
||||
stride)
|
||||
|
||||
# detection_boxes is a [BS, num_detections, 1, 4] tensor, detection_scores is a
|
||||
# [BS, num_detections, num_classes] tensor
|
||||
detection_boxes = detection_boxes / H_input # normalization of box coordinates to 1
|
||||
BS = endnodes[0].shape[0]
|
||||
H = H_input // stride
|
||||
W = W_input // stride
|
||||
num_anchors = anchors_for_stride.size // 2
|
||||
num_detections = H * W * num_anchors
|
||||
# detection_boxes.set_shape((BS, num_detections, 1, 4))
|
||||
# detection_scores.set_shape((BS, num_detections, num_classes))
|
||||
# concatenating the detections from the different output layers:
|
||||
if output_ind == 0:
|
||||
detection_boxes_full = detection_boxes
|
||||
detection_scores_full = detection_scores
|
||||
else:
|
||||
detection_boxes_full = tf.concat([detection_boxes_full, detection_boxes], axis=1)
|
||||
detection_scores_full = tf.concat([detection_scores_full, detection_scores], axis=1)
|
||||
|
||||
score_threshold = 0.5
|
||||
nms_iou_threshold = 0.5
|
||||
labels_offset = 1
|
||||
|
||||
(nmsed_boxes, nmsed_scores, nmsed_classes, num_detections) = \
|
||||
combined_non_max_suppression(boxes=detection_boxes_full,
|
||||
scores=detection_scores_full,
|
||||
score_threshold=score_threshold,
|
||||
iou_threshold=nms_iou_threshold,
|
||||
max_output_size_per_class=100,
|
||||
max_total_size=100)
|
||||
|
||||
|
||||
def _get_coco_labels():
|
||||
coco_names = json.load(open(os.path.join(os.path.dirname(__file__), 'coco_names.json')))
|
||||
coco_names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in coco_names.items()}
|
||||
return coco_names
|
||||
# adding offset to the class prediction and cast to integer
|
||||
def translate_coco_2017_to_2014(nmsed_classes):
|
||||
return np.vectorize(COCO_17_14.get)(nmsed_classes).astype(np.int32)
|
||||
|
||||
nmsed_classes = tf.cast(tf.add(nmsed_classes, labels_offset), tf.int16)
|
||||
nmsed_classes = translate_coco_2017_to_2014(nmsed_classes)
|
||||
|
||||
return {'detection_boxes': nmsed_boxes,
|
||||
'detection_scores': nmsed_scores,
|
||||
'detection_classes': nmsed_classes,
|
||||
'num_detections': num_detections}
|
||||
|
||||
|
||||
|
||||
class HailoHandler:
|
||||
def __init__(self, hef_path='hef/yolov5m.hef'):
|
||||
target = PcieDevice()
|
||||
|
||||
self.hef = HEF(hef_path)
|
||||
|
||||
# Configure network groups
|
||||
configure_params = ConfigureParams.create_from_hef(hef=self.hef,
|
||||
interface=HailoStreamInterface.PCIe)
|
||||
network_groups = target.configure(self.hef, configure_params)
|
||||
self.network_group = network_groups[0]
|
||||
|
||||
self.input_vstreams_params = InputVStreamParams.make(self.network_group,
|
||||
quantized=False,
|
||||
format_type=FormatType.FLOAT32)
|
||||
|
||||
self.output_vstreams_params = OutputVStreamParams.make(self.network_group, quantized=False, format_type=FormatType.FLOAT32)
|
||||
|
||||
self.input_vstream_info = self.hef.get_input_vstream_infos()[0]
|
||||
self.output_vstream_infos = self.hef.get_output_vstream_infos()
|
||||
self.network_group_params = self.network_group.create_params()
|
||||
|
||||
def run_hailo(self, dataset):
|
||||
|
||||
input_data = {self.input_vstream_info.name: dataset}
|
||||
|
||||
with InferVStreams(self.network_group, self.input_vstreams_params, self.output_vstreams_params) as infer_pipeline:
|
||||
with self.network_group.activate(self.network_group_params):
|
||||
infer_results = infer_pipeline.infer(input_data)
|
||||
|
||||
out = [infer_results[i.name] for i in self.output_vstream_infos]
|
||||
return out
|
||||
|
||||
def _get_labels(label_name):
|
||||
filename = os.path.join(os.path.dirname(__file__), label_name + '.json')
|
||||
names = json.load(open(filename))
|
||||
names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in names.items()}
|
||||
return names
|
||||
|
||||
|
||||
def process_yolo5():
|
||||
|
||||
hef, network_group = init_hailo("yolov5m_22_2")
|
||||
imageMeta = ImageMeta(640, 640, 3)
|
||||
processor = YoloProcessing(imageMeta, classes=3)
|
||||
data = DataHandler('./minimal_data', imageMeta)
|
||||
data.load_data(processor.preproc)
|
||||
|
||||
dataset, names = dataset_read(hef)
|
||||
hailo = HailoHandler('hef/yolov5m_22_2.hef')
|
||||
out = hailo.run_hailo(data.dataset)
|
||||
|
||||
samples = 1000
|
||||
start_time = time.time()
|
||||
fps = 0
|
||||
while samples > 0:
|
||||
if start_time + 1 < time.time():
|
||||
print("fps: " + str(fps))
|
||||
start_time = time.time()
|
||||
fps = 0
|
||||
|
||||
out, names, dataset, names = run_hailo(dataset, names, hef, network_group)
|
||||
|
||||
logits = postprocessing(out)
|
||||
|
||||
fps += 1
|
||||
samples -= 1
|
||||
logits = processor.postprocessing(out)
|
||||
|
||||
|
||||
labels = _get_labels("daria_names")
|
||||
labels = data._get_labels("daria_names")
|
||||
image = visualize_boxes_and_labels_on_image_array(
|
||||
dataset[0],
|
||||
data.dataset[0],
|
||||
logits['detection_boxes'].numpy()[0],
|
||||
logits['detection_classes'][0],
|
||||
logits['detection_scores'].numpy()[0],
|
||||
@@ -324,16 +309,5 @@ def process_yolo5():
|
||||
|
||||
Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
|
||||
|
||||
COCO_2017_TO_2014_TRANSLATION = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10,
|
||||
11: 11, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19,
|
||||
19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 27, 26: 28,
|
||||
27: 31, 28: 32, 29: 33, 30: 34, 31: 35, 32: 36, 33: 37, 34: 38,
|
||||
35: 39, 36: 40, 37: 41, 38: 42, 39: 43, 40: 44, 41: 46, 42: 47,
|
||||
43: 48, 44: 49, 45: 50, 46: 51, 47: 52, 48: 53, 49: 54, 50: 55,
|
||||
51: 56, 52: 57, 53: 58, 54: 59, 55: 60, 56: 61, 57: 62, 58: 63,
|
||||
59: 64, 60: 65, 61: 67, 62: 70, 63: 72, 64: 73, 65: 74, 66: 75,
|
||||
67: 76, 68: 77, 69: 78, 70: 79, 71: 80, 72: 81, 73: 82, 74: 84,
|
||||
75: 85, 76: 86, 77: 87, 78: 88, 79: 89, 80: 90}
|
||||
|
||||
if __name__ == "__main__":
|
||||
process_yolo5()
|
||||
|
||||
Reference in New Issue
Block a user