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308 lines
9.6 KiB
308 lines
9.6 KiB
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]], output_tensor_type='int8') |
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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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