import os from multiprocessing import Process import json import time import numpy as np from PIL import Image import tensorflow as tf from tensorflow.image import combined_non_max_suppression from detection_tools.utils.visualization_utils import visualize_boxes_and_labels_on_image_array from hailo_platform import (HEF, PcieDevice, HailoStreamInterface, InferVStreams, ConfigureParams, \ InputVStreamParams, OutputVStreamParams, InputVStreams, OutputVStreams, FormatType) # preprocess dataset for yolov5 size # yolov5 640x640 # resnet18 320x320 def preproc(image, output_height=640, output_width=640, resize_side=256): ''' imagenet-standard: aspect-preserving resize to 256px smaller-side, then central-crop to 224px ''' new_width = int(image.width/image.height*resize_side) new_height = resize_side x , y = (new_width-output_width)/2, 0 # Select area to crop area = (x, y, x+output_width, y+output_height) # Crop, show, and save image cropped_img = image.resize((new_width, new_height)).crop(area) return cropped_img # Collect images from data files def dataset_read(hef): images_path = './minimal_data' names = [] images_list = [img_name for img_name in os.listdir(images_path) if os.path.splitext(os.path.join(images_path, img_name))[1] == '.jpg'] # Define dataset params input_vstream_info = hef.get_input_vstream_infos()[0] output_vstream_infos = hef.get_output_vstream_infos() image_height, image_width, channels = input_vstream_info.shape # dataset = np.zeros((len(images_list), image_height, image_width, channels), # dtype=np.float32) dataset = np.zeros((1, image_height, image_width, channels), dtype=np.float32) for idx, img_name in enumerate(images_list): img = Image.open(os.path.join(images_path, img_name)) img_preproc = preproc(img) dataset[idx,:,:,:] = np.array(img_preproc) names.append(img_name) break return dataset, names # Generate random dataset def dataset_random(image_height, image_width, channels): num_of_images = 10 low, high = 2, 20 dataset = np.random.randint(low, high, (num_of_images, image_height, image_width, channels)).astype(np.float32) return dataset def init_hailo(model_name='yolov5m'): target = PcieDevice() hef_path = f'hef/{model_name}.hef' hef = HEF(hef_path) # Configure network groups configure_params = ConfigureParams.create_from_hef(hef=hef, interface=HailoStreamInterface.PCIe) network_groups = target.configure(hef, configure_params) network_group = network_groups[0] return hef, network_group ''' The target can be used as a context manager ("with" statement) to ensure it's released on time. Here it's avoided for the sake of simplicity ''' def run_hailo(dataset, names, hef, network_group): # Create input and output virtual streams params # Quantized argument signifies whether or not the incoming data is already quantized. # Data is quantized by HailoRT if and only if quantized == False . input_vstreams_params = InputVStreamParams.make(network_group, quantized=False, format_type=FormatType.FLOAT32) # TODO: change to FLOAT32 output_vstreams_params = OutputVStreamParams.make(network_group, quantized=False, format_type=FormatType.FLOAT32) # output_vstreams_params = OutputVStreamParams.make(network_group, # quantized=True, # format_type=FormatType.INT8) input_vstream_info = hef.get_input_vstream_infos()[0] output_vstream_infos = hef.get_output_vstream_infos() input_data = {input_vstream_info.name: dataset} network_group_params = network_group.create_params() with InferVStreams(network_group, input_vstreams_params, output_vstreams_params) as infer_pipeline: with network_group.activate(network_group_params): infer_results = infer_pipeline.infer(input_data) out = [infer_results[i.name] for i in output_vstream_infos] return out, names, dataset, names # 20 x 20 -> 32 # stride = 32 def yolo_postprocess_numpy(net_out, anchors_for_stride, stride): """ net_out is shape: [N, 19, 19, 255] or [N, 38, 38, 255] or [N, 76, 76, 255] first we reshape it to be as in gluon and then follow gluon's shapes. output_ind = 0 for stride 32, 1 for stride 16, 2 for stride 8. """ # net_out = net_out.astype(np.float32) / 256 num_classes = 4 BS = net_out.shape[0] # batch size H = net_out.shape[1] W = net_out.shape[2] num_anchors = anchors_for_stride.size // 2 # 2 params for each anchor. num_pred = 1 + 4 + num_classes # 2 box centers, 2 box scales, 1 objness, num_classes class scores alloc_size = (128, 128) grid_x = np.arange(alloc_size[1]) grid_y = np.arange(alloc_size[0]) grid_x, grid_y = np.meshgrid(grid_x, grid_y) # dims [128,128], [128,128] offsets = np.concatenate((grid_x[:, :, np.newaxis], grid_y[:, :, np.newaxis]), axis=-1) # dim [128,128,2] offsets = np.expand_dims(np.expand_dims(offsets, 0), 0) # dim [1,1,128,128,2] pred = net_out.transpose((0, 3, 1, 2)) # now dims are: [N,C,H,W] as in Gluon. pred = np.reshape(pred, (BS, num_anchors * num_pred, -1)) # dim [N, 255, HxW] # dim [N, 361, 255], we did it so that the 255 be the last dim and can be reshaped. 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 = _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(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 postprocessing(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] num_classes = 80 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 = 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) # adding offset to the class prediction and cast to integer def translate_coco_2017_to_2014(nmsed_classes): return np.vectorize(COCO_2017_TO_2014_TRANSLATION.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} def _get_face_detection_visualization_data(logits): boxes = logits['detection_boxes'][0] 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 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 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") dataset, names = dataset_read(hef) 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 labels = _get_labels("daria_names") image = visualize_boxes_and_labels_on_image_array( 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) 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()