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main.py
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import argparse
import cv2
import numpy as np
class yolox():
def __init__(self, model, p6=False, confThreshold=0.5, nmsThreshold=0.5, objThreshold=0.5):
with open('coco.names', 'rt') as f:
self.class_names = f.read().rstrip('\n').split('\n')
self.net = cv2.dnn.readNet(model)
self.input_size = (640, 640)
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
if not p6:
self.strides = [8, 16, 32]
else:
self.strides = [8, 16, 32, 64]
self.confThreshold = confThreshold
self.nmsThreshold = nmsThreshold
self.objThreshold = objThreshold
def preprocess(self, image):
if len(image.shape) == 3:
padded_img = np.ones((self.input_size[0], self.input_size[1], 3)) * 114.0
else:
padded_img = np.ones(self.input_size) * 114.0
img = np.array(image)
r = min(self.input_size[0] / img.shape[0], self.input_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.float32)
padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
image = padded_img
image = image.astype(np.float32)
image = image[:, :, ::-1]
image /= 255.0
image -= self.mean
image /= self.std
return image, r
def demo_postprocess(self, outputs):
grids = []
expanded_strides = []
hsizes = [self.input_size[0] // stride for stride in self.strides]
wsizes = [self.input_size[1] // stride for stride in self.strides]
for hsize, wsize, stride in zip(hsizes, wsizes, self.strides):
xv, yv = np.meshgrid(np.arange(hsize), np.arange(wsize))
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
def nms(self, boxes, scores):
"""Single class NMS implemented in Numpy."""
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 <= self.nmsThreshold)[0]
order = order[inds + 1]
return keep
def multiclass_nms(self, boxes, scores):
"""Multiclass NMS implemented in Numpy"""
final_dets = []
num_classes = scores.shape[1]
for cls_ind in range(num_classes):
cls_scores = scores[:, cls_ind]
valid_score_mask = cls_scores > self.confThreshold
if valid_score_mask.sum() == 0:
continue
else:
valid_scores = cls_scores[valid_score_mask]
valid_boxes = boxes[valid_score_mask]
keep = self.nms(valid_boxes, valid_scores)
if len(keep) > 0:
cls_inds = np.ones((len(keep), 1)) * cls_ind
dets = np.concatenate([valid_boxes[keep], valid_scores[keep, None], cls_inds], 1)
final_dets.append(dets)
if len(final_dets) == 0:
return None
return np.concatenate(final_dets, 0)
def vis(self, img, boxes, scores, cls_ids):
for i in range(len(boxes)):
box = boxes[i]
cls_id = int(cls_ids[i])
score = scores[i]
if score < self.confThreshold:
continue
x0 = int(box[0])
y0 = int(box[1])
x1 = int(box[2])
y1 = int(box[3])
text = '{}:{:.1f}%'.format(self.class_names[cls_id], score * 100)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
cv2.rectangle(img, (x0, y0), (x1, y1), (0, 0, 255), 2)
cv2.rectangle(img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1)
cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 255, 0), thickness=1)
return img
def detect(self, srcimg):
img, ratio = self.preprocess(srcimg)
blob = cv2.dnn.blobFromImage(img)
self.net.setInput(blob)
outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
predictions = self.demo_postprocess(outs[0])[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
boxes_xyxy /= ratio
dets = self.multiclass_nms(boxes_xyxy, scores)
if dets is not None:
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
srcimg = self.vis(srcimg, final_boxes, final_scores, final_cls_inds)
return srcimg
if __name__ == '__main__':
parser = argparse.ArgumentParser("opencv inference sample")
parser.add_argument("--model", type=str, default="yolox_s.onnx", help="Input your onnx model.")
parser.add_argument("--image_path", type=str, default='test_image.png', help="Path to your input image.")
parser.add_argument("--score_thr", type=float, default=0.3, help="Score threshould to filter the result.")
parser.add_argument("--with_p6", action="store_true", help="Whether your model uses p6 in FPN/PAN.")
args = parser.parse_args()
net = yolox(args.model, p6=args.with_p6, confThreshold=args.score_thr)
srcimg = cv2.imread(args.image_path)
srcimg = net.detect(srcimg)
winName = 'Deep learning object detection in OpenCV'
cv2.namedWindow(winName, cv2.WINDOW_NORMAL)
cv2.imshow(winName, srcimg)
cv2.waitKey(0)
cv2.destroyAllWindows()