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import cv2
from time import time
import numpy as np
import onnxruntime
class YOLOX_ONNX:
def __init__(self, model_path):
providers = ['CPUExecutionProvider']
self.model = onnxruntime.InferenceSession(model_path, providers=providers)
self.image_size = self.model.get_inputs()[0].shape[-2:]
# print(self.model.get_outputs()[0].name)
# print(self.image_size)
self.labels_map = ['pedestrian']
def pad_to_square(self, image):
height, width = image.shape[:2]
if (width / height) < 1.2:
# print('Square Image')
self.top, self.bottom = 0, 0
self.left, self.right = 0, 0
return image
size = max(height, width)
delta_w = size - width
delta_h = size - height
self.top, self.bottom = delta_h // 2, delta_h - (delta_h // 2)
self.left, self.right = delta_w // 2, delta_w - (delta_w // 2)
print(self.top, self.bottom, self.left, self.right)
color = [114, 114, 114] # padding
return cv2.copyMakeBorder(image, self.top, self.bottom, self.left, self.right, cv2.BORDER_CONSTANT, value=color)
def __preprocess_image(self, img, swap=(2, 0, 1)):
img = self.pad_to_square(img) # training aspect ratio is 1:1
padded_img = np.ones((self.image_size[0], self.image_size[1], 3), dtype=np.uint8) * 114
r = min(self.image_size[0] / img.shape[0], self.image_size[1] / img.shape[1])
resized_img = cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)),
interpolation=cv2.INTER_LINEAR).astype(np.uint8)
padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
padded_img = padded_img.transpose(swap)
padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
return padded_img, r
@staticmethod
def __new_nms(boxes, scores, iou_thresh):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= iou_thresh)[0]
order = order[inds + 1]
return keep
def __parse_output_data(self, outputs):
grids = []
expanded_strides = []
strides = [8, 16, 32]
hsizes = [self.image_size[0] // stride for stride in strides]
wsizes = [self.image_size[1] // stride for stride in strides]
for hsize, wsize, stride in zip(hsizes, wsizes, strides):
xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
grids.append(grid)
shape = grid.shape[:2]
expanded_strides.append(np.full((*shape, 1), stride))
grids = np.concatenate(grids, 1)
expanded_strides = np.concatenate(expanded_strides, 1)
outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
return outputs[0]
def __decode_prediction(self, prediction, img_size, resize_ratio, score_thresh, iou_thresh):
boxes = prediction[:, :4]
classes = prediction[:, 4:5] * prediction[:, 5:]
scores = np.amax(classes, axis=1)
classes = np.argmax(classes, axis=1)
valid_score_mask = scores > score_thresh
if valid_score_mask.sum() == 0:
return np.array([]), np.array([]), np.array([])
valid_scores = scores[valid_score_mask]
valid_boxes = boxes[valid_score_mask]
valid_classes = classes[valid_score_mask]
valid_boxes_xyxy = np.ones_like(valid_boxes)
valid_boxes_xyxy[:, 0] = valid_boxes[:, 0] - valid_boxes[:, 2] / 2.
valid_boxes_xyxy[:, 1] = valid_boxes[:, 1] - valid_boxes[:, 3] / 2.
valid_boxes_xyxy[:, 2] = valid_boxes[:, 0] + valid_boxes[:, 2] / 2.
valid_boxes_xyxy[:, 3] = valid_boxes[:, 1] + valid_boxes[:, 3] / 2.
valid_boxes_xyxy /= resize_ratio
indices = self.__new_nms(valid_boxes_xyxy, valid_scores, iou_thresh)
valid_boxes_xyxy = valid_boxes_xyxy[indices, :]
valid_scores = valid_scores[indices]
valid_classes = valid_classes[indices].astype('int')
# valid_boxes_xyxy, valid_scores, valid_classes = self.__remove_duplicates(valid_boxes_xyxy, valid_scores, valid_classes)
for i, offset in enumerate([self.left, self.top, self.right, self.bottom]):
valid_boxes_xyxy[:, i] = valid_boxes_xyxy[:,
i] - offset # remove pad offsets from boundingbox(xmin,ymin,xmax,ymax)
return valid_boxes_xyxy, valid_scores, valid_classes
def draw_boxes(self, img, boxes, scores=None, classes=None, labels=None):
for i in range(boxes.shape[0]):
cv2.rectangle(img,
(int(boxes[i, 0]), int(boxes[i, 1])),
(int(boxes[i, 2]), int(boxes[i, 3])),
(0, 128, 0),
int(0.005 * img.shape[1]))
### not drawing classes since num_classes is 1(pedestrian) and text not greatly visible in gradio UI
# text_label = ''
# if labels is not None:
# if classes is not None:
# text_label = labels[classes[i]]
# if scores is not None:
# text_label+= ' ' + str("%.2f" % round(scores[i],2))
# elif scores is not None:
# text_label = str("%.2f" % round(scores[i],2))
# w, h = cv2.getTextSize(text_label, 0, fontScale=0.5, thickness=1)[0]
# cv2.putText(img,
# text_label,
# (int(boxes[i,0]) if int(boxes[i,0])+w<img.shape[1] else img.shape[1]-w, int(boxes[i,1])-2 if (int(boxes[i,1])-h>=3) else int(boxes[i,1])+h+2),
# 0,
# 0.5,
# (0,0,255),
# thickness= int(0.005*img.shape[1]),
# lineType=cv2.LINE_AA)
return img
def predict(self, image, score_thresh=0.4, iou_thresh=0.4):
h, w = image.shape[:2]
origin_img = np.copy(image)
model_input = np.copy(image)
model_input, resize_ratio = self.__preprocess_image(model_input)
# print(model_input.shape)
# print('input mean:', np.mean(model_input))
start_time = time()
prediction = self.model.run(None, {self.model.get_inputs()[0].name: model_input[None, :, :, :]})
# print(self.model.get_inputs()[0].name)
# print('output mean:',np.mean(prediction))
prediction = self.__parse_output_data(prediction[0])
d_boxes, d_scores, d_classes = self.__decode_prediction(prediction, (h, w), resize_ratio, score_thresh,
iou_thresh)
self.output_img = self.draw_boxes(origin_img, d_boxes, None, d_classes, self.labels_map)
print('elapsed time:', time() - start_time)
return d_boxes, d_scores, d_classes
# if __name__ == "__main__":
# from matplotlib import pyplot as plt
#
# path = 'test-images/test1.jpg'
# yolox_nano_onnx = YOLOX_ONNX('models/pedestrian-detection-best95.onnx')
# yolox_nano_onnx.predict(cv2.imread(path))
# plt.title('Predicted')
# plt.imshow(cv2.cvtColor(yolox_nano_onnx.output_img, cv2.COLOR_BGR2RGB))
# plt.show()
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