810f412821
- hailo infer node subscribes to an image topic - preprocess image via PIL to the desired image parameters (hard- coded currently for yolov5m) - processes inference on hailo chip - post process and prints bounding boxes into terminal
504 lines
19 KiB
Python
504 lines
19 KiB
Python
import json
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import os
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import io
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import time
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from PIL import Image
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from threading import Thread
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import ipdb
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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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import rclpy
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from rclpy.node import Node
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from std_msgs.msg import String as StringMsg
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from sensor_msgs.msg import Image as ImageMsg
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from cv_bridge import CvBridge
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# Collect images from data files
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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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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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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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def get_labels(self, path):
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filename = os.path.join(os.path.dirname(__file__), path)
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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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def get_labels(path):
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filename = os.path.join(os.path.dirname(__file__), path)
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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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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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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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def preproc(self, image, 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-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))
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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 = self._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(self, 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 process_to_picture(self, endnodes, data):
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logits = self.postprocessing(endnodes)
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self.visualize_image(logits, data)
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def visualize_image(self, logits, data):
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labels = data.get_labels("data/daria_labels.json")
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image = visualize_boxes_and_labels_on_image_array(
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data.dataset[0],
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logits['detection_boxes'].numpy()[0],
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logits['detection_classes'][0],
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logits['detection_scores'].numpy()[0],
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labels,
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use_normalized_coordinates=True,
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max_boxes_to_draw=100,
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min_score_thresh=.5,
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agnostic_mode=False,
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line_thickness=4)
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Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
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print("Successfully saved image")
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def postprocessing(self, 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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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 = self.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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# 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_17_14.get)(nmsed_classes).astype(np.int32)
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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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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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class HailoHandler:
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def __init__(self, hef_path='hef/yolov5m.hef'):
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target = PcieDevice()
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self.hef = HEF(hef_path)
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# Configure network groups
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configure_params = ConfigureParams.create_from_hef(hef=self.hef,
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interface=HailoStreamInterface.PCIe)
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network_groups = target.configure(self.hef, configure_params)
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self.network_group = network_groups[0]
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self.input_vstreams_params = InputVStreamParams.make(self.network_group,
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quantized=False,
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format_type=FormatType.FLOAT32)
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self.output_vstreams_params = OutputVStreamParams.make(self.network_group, quantized=False, format_type=FormatType.FLOAT32)
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self.input_vstream_info = self.hef.get_input_vstream_infos()[0]
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self.output_vstream_infos = self.hef.get_output_vstream_infos()
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self.network_group_params = self.network_group.create_params()
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def run_hailo(self, dataset):
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input_data = {self.input_vstream_info.name: dataset}
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with InferVStreams(self.network_group, self.input_vstreams_params, self.output_vstreams_params) as infer_pipeline:
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with self.network_group.activate(self.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 self.output_vstream_infos]
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return out
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def start_hailo_thread(self):
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self.hailo_async = True
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self.hailo_block = False
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self.input_data = None
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self.hailo_thread = Thread(target=self._hailo_async)
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self.hailo_thread.start()
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def _hailo_async(self):
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with InferVStreams(self.network_group, self.input_vstreams_params, self.output_vstreams_params)\
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as infer_pipeline:
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with self.network_group.activate(self.network_group_params):
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self._hailo_async_loop(infer_pipeline)
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def _hailo_async_loop(self, infer_pipeline):
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while self.hailo_async:
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if(not self.hailo_block and type(self.input_data) != type(None)):
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self.infer_results = None
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self.hailo_block = True
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infer_results = infer_pipeline.infer(self.input_data)
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self.infer_results = [infer_results[i.name] for i in self.output_vstream_infos]
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self.input_data = None
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self.hailo_block = False
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def hailo_input(self, input_data):
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while self.hailo_block:
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time.sleep(0.01)
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self.hailo_block = True
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self.input_data = input_data
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self.input_data = {self.input_vstream_info.name: input_data}
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self.infer_results = None
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self.hailo_block = False
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def hailo_output(self):
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while self.hailo_block:
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time.sleep(0.01)
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return self.infer_results
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def stop_hailo_thread(self):
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self.hailo_async = False
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self.hailo_thread.join()
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class HailoNode(Node):
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def __init__(self):
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super().__init__('hailo_image_subscriber')
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self.sub = self.create_subscription(ImageMsg, '/camera/color/image_raw', self.image_callback, 10)
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self.pub = self.create_publisher(ImageMsg, '/hailo_bounding_boxes', 10)
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self.bridge = CvBridge()
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# metadata init
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classes = 3
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self.image_meta = ImageMeta(640, 640, 3)
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self.processor = YoloProcessing(self.image_meta, classes)
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# hailo init
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self.hailo = HailoHandler('hef/yolov5m_daria.hef')
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self.hailo.start_hailo_thread()
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def image_callback(self, data):
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print("Received an image!")
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img = self.convert(data)
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self.image_infer(img)
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def image_infer(self, image):
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image = self.processor.preproc(image)
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dataset = self.dataset_from_image(image)
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self.hailo.hailo_input(dataset)
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out = None
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while(out == None):
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time.sleep(0.0001)
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out = self.hailo.hailo_output()
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logits = self.processor.postprocessing(out)
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labels = get_labels("data/daria_labels.json")
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for bb in range(len(logits['detection_boxes'].numpy()[0])):
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boxes = logits['detection_boxes'].numpy()[0][bb]
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classes = logits['detection_classes'][0][bb]
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scores = logits['detection_scores'].numpy()[0][bb]
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if(scores > 0.01):
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print(boxes)
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print(labels[classes])
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print(scores)
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print("-----")
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# convert ros image to PIL image
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def convert(self, ros_image):
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try:
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img = self.bridge.imgmsg_to_cv2(ros_image, "rgb8")
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image = Image.fromarray(img)
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except CvBridgeError as e:
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print(e)
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return image
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def dataset_from_image(self, image):
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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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dataset[0, :, :, :] = np.array(image)
|
|
return dataset
|
|
|
|
def test_async_yolo5():
|
|
imageMeta = ImageMeta(640, 640, 3)
|
|
processor = YoloProcessing(imageMeta, classes=3)
|
|
data = DataHandler('./data', imageMeta)
|
|
data.load_data(processor.preproc)
|
|
|
|
hailo = HailoHandler('hef/yolov5m_daria.hef')
|
|
hailo.start_hailo_thread()
|
|
|
|
fps = 0
|
|
now = time.time()
|
|
for i in range(100):
|
|
fps += 1
|
|
if now + 1 < time.time():
|
|
fps = 0
|
|
now = time.time()
|
|
|
|
hailo.hailo_input(data.dataset)
|
|
out = None
|
|
while(out == None):
|
|
time.sleep(0.0001)
|
|
out = hailo.hailo_output()
|
|
|
|
Thread(target=processor.postprocessing, args=[out]).start()
|
|
|
|
hailo.stop_hailo_thread()
|
|
|
|
|
|
def test_process_yolo5():
|
|
|
|
imageMeta = ImageMeta(640, 640, 3)
|
|
processor = YoloProcessing(imageMeta, classes=4)
|
|
data = DataHandler('./data', imageMeta)
|
|
data.load_data(processor.preproc)
|
|
|
|
hailo = HailoHandler('hef/yolov5m_daria.hef')
|
|
|
|
now = time.time()
|
|
fps = 0
|
|
for i in range(100):
|
|
fps += 1
|
|
if now + 1 < time.time():
|
|
print(fps)
|
|
fps = 0
|
|
now = time.time()
|
|
|
|
out = hailo.run_hailo(data.dataset)
|
|
logits = processor.postprocessing(out)
|
|
|
|
|
|
labels = data.get_labels("data/daria_labels.json")
|
|
image = visualize_boxes_and_labels_on_image_array(
|
|
data.dataset[0],
|
|
logits['detection_boxes'].numpy()[0],
|
|
logits['detection_classes'][0],
|
|
logits['detection_scores'].numpy()[0],
|
|
labels,
|
|
use_normalized_coordinates=True,
|
|
max_boxes_to_draw=100,
|
|
min_score_thresh=.5,
|
|
agnostic_mode=False,
|
|
line_thickness=4)
|
|
|
|
print("Successfully saved image")
|
|
|
|
def main(args=None):
|
|
rclpy.init(args=args)
|
|
|
|
hailo_node = HailoNode()
|
|
|
|
rclpy.spin(hailo_node)
|
|
|
|
if __name__ == "__main__":
|
|
main()
|