import os
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import urllib
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import traceback
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import time
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import sys
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import numpy as np
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import cv2
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from rknn.api import RKNN
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ONNX_MODEL = 'yolov5s.onnx'
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RKNN_MODEL = 'yolov5s.rknn'
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IMG_PATH = './bus.jpg'
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DATASET = './dataset.txt'
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QUANTIZE_ON = True
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BOX_THESH = 0.5
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NMS_THRESH = 0.6
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IMG_SIZE = 640
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CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
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"fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
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"bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
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"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
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"spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
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"pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop ", "mouse ", "remote ", "keyboard ", "cell phone", "microwave ",
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"oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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def xywh2xyxy(x):
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# Convert [x, y, w, h] to [x1, y1, x2, y2]
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y = np.copy(x)
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y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
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y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
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y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
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y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
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return y
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def process(input, mask, anchors):
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anchors = [anchors[i] for i in mask]
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grid_h, grid_w = map(int, input.shape[0:2])
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box_confidence = sigmoid(input[..., 4])
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box_confidence = np.expand_dims(box_confidence, axis=-1)
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box_class_probs = sigmoid(input[..., 5:])
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box_xy = sigmoid(input[..., :2])*2 - 0.5
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col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
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row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
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col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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grid = np.concatenate((col, row), axis=-1)
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box_xy += grid
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box_xy *= int(IMG_SIZE/grid_h)
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box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
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box_wh = box_wh * anchors
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box = np.concatenate((box_xy, box_wh), axis=-1)
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return box, box_confidence, box_class_probs
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def filter_boxes(boxes, box_confidences, box_class_probs):
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"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
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# Arguments
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boxes: ndarray, boxes of objects.
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box_confidences: ndarray, confidences of objects.
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box_class_probs: ndarray, class_probs of objects.
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# Returns
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boxes: ndarray, filtered boxes.
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classes: ndarray, classes for boxes.
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scores: ndarray, scores for boxes.
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"""
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box_classes = np.argmax(box_class_probs, axis=-1)
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box_class_scores = np.max(box_class_probs, axis=-1)
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pos = np.where(box_confidences[..., 0] >= BOX_THESH)
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boxes = boxes[pos]
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classes = box_classes[pos]
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scores = box_class_scores[pos]
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return boxes, classes, scores
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def nms_boxes(boxes, scores):
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"""Suppress non-maximal boxes.
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# Arguments
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boxes: ndarray, boxes of objects.
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scores: ndarray, scores of objects.
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# Returns
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keep: ndarray, index of effective boxes.
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"""
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x = boxes[:, 0]
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y = boxes[:, 1]
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w = boxes[:, 2] - boxes[:, 0]
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h = boxes[:, 3] - boxes[:, 1]
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areas = w * h
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x[i], x[order[1:]])
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yy1 = np.maximum(y[i], y[order[1:]])
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xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
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yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
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w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
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h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
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inter = w1 * h1
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= NMS_THRESH)[0]
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order = order[inds + 1]
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keep = np.array(keep)
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return keep
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def yolov5_post_process(input_data):
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masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
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anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
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[59, 119], [116, 90], [156, 198], [373, 326]]
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boxes, classes, scores = [], [], []
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for input, mask in zip(input_data, masks):
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b, c, s = process(input, mask, anchors)
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b, c, s = filter_boxes(b, c, s)
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boxes.append(b)
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classes.append(c)
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scores.append(s)
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boxes = np.concatenate(boxes)
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boxes = xywh2xyxy(boxes)
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classes = np.concatenate(classes)
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scores = np.concatenate(scores)
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nboxes, nclasses, nscores = [], [], []
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for c in set(classes):
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inds = np.where(classes == c)
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b = boxes[inds]
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c = classes[inds]
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s = scores[inds]
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keep = nms_boxes(b, s)
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nboxes.append(b[keep])
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nclasses.append(c[keep])
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nscores.append(s[keep])
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if not nclasses and not nscores:
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return None, None, None
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boxes = np.concatenate(nboxes)
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classes = np.concatenate(nclasses)
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scores = np.concatenate(nscores)
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return boxes, classes, scores
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def draw(image, boxes, scores, classes):
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"""Draw the boxes on the image.
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# Argument:
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image: original image.
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boxes: ndarray, boxes of objects.
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classes: ndarray, classes of objects.
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scores: ndarray, scores of objects.
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all_classes: all classes name.
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"""
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for box, score, cl in zip(boxes, scores, classes):
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top, left, right, bottom = box
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print('class: {}, score: {}'.format(CLASSES[cl], score))
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print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
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top = int(top)
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left = int(left)
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right = int(right)
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bottom = int(bottom)
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cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
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cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
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(top, left - 6),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (0, 0, 255), 2)
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def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, ratio, (dw, dh)
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if __name__ == '__main__':
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# Create RKNN object
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rknn = RKNN(verbose=True)
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# pre-process config
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print('--> Config model')
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rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]])
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print('done')
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# Load ONNX model
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print('--> Loading model')
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ret = rknn.load_onnx(model=ONNX_MODEL, outputs=['378', '439', '500'])
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if ret != 0:
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print('Load model failed!')
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exit(ret)
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print('done')
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# Build model
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print('--> Building model')
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ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)
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if ret != 0:
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print('Build model failed!')
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exit(ret)
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print('done')
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# Export RKNN model
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print('--> Export rknn model')
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ret = rknn.export_rknn(RKNN_MODEL)
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if ret != 0:
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print('Export rknn model failed!')
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exit(ret)
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print('done')
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime()
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# ret = rknn.init_runtime('rk3566')
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if ret != 0:
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print('Init runtime environment failed!')
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exit(ret)
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print('done')
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# Set inputs
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img = cv2.imread(IMG_PATH)
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# img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img])
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np.save('./onnx_yolov5_0.npy', outputs[0])
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np.save('./onnx_yolov5_1.npy', outputs[1])
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np.save('./onnx_yolov5_2.npy', outputs[2])
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print('done')
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# post process
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input0_data = outputs[0]
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input1_data = outputs[1]
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input2_data = outputs[2]
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input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
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input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
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input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
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input_data = list()
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input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
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input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
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input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
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boxes, classes, scores = yolov5_post_process(input_data)
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img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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if boxes is not None:
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draw(img_1, boxes, scores, classes)
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# show output
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# cv2.imshow("post process result", img_1)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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rknn.release()
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