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| import onnxruntime
import cv2
import numpy as np
import torch
#---------------------NMS--------------------------------------------------------------------
def py_cpu_nms(dets0, conf_thresh, iou_thresh):
"""Pure Python NMS baseline."""
nc = dets0.shape[1] - 5
dets = dets0[dets0[:, 4] > conf_thresh]
dets = xywh2xyxy(dets)
keep_all = []
for cls in range(nc):
dets_single = dets[np.argmax(dets[:,5:],axis=1)==cls]
#print('dets_single %d'%cls,dets_single)
x1 = dets_single[:, 0]
y1 = dets_single[:, 1]
x2 = dets_single[:, 2]
y2 = dets_single[:, 3]
scores = dets_single[:, 4]
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]
keep_rect = dets_single[keep]
#print('keep',keep)
keep_all.extend(keep_rect)
return keep_all
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = np.zeros_like(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
y[:, 4:] = x[:,4:]
return y
#---------------------img_preprocess-----------------------------------------------------------------
def img_preprocess(frame,imgsz):
# im = letterbox(frame, imgsz)[0]
im = cv2.resize(frame,(640,384))
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
im = np.asarray(im, dtype=np.float32)
im = np.expand_dims(im, 0)
im /= 255.0
return im
#---------------------------letterbox------------------------------------------------------------------
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
#-----------------------------decode----------------------------------------
def np_sigmoid(x):
return 1.0/(1.0+1.0/np.exp(x))
def decode_output(pred_raw_data,anchor_txt):
pred_raw_data = np_sigmoid(pred_raw_data)
print(max(pred_raw_data[:, 4]))
pred_raw_data[:, 0] = (pred_raw_data[:, 0] * 2. - 0.5 + anchor_txt[:, 0]) * anchor_txt[:, 4] #x
pred_raw_data[:, 1] = (pred_raw_data[:, 1] * 2. - 0.5 + anchor_txt[:, 1]) * anchor_txt[:, 4] #y
pred_raw_data[:, 2] = (pred_raw_data[:, 2] * 2) ** 2 * anchor_txt[:, 2] # w
pred_raw_data[:, 3] = (pred_raw_data[:, 3] * 2) ** 2 * anchor_txt[:, 3] # h
return pred_raw_data
#-------------------------scale_ratio------------------------------------------------------
def helmet_scale_ratio(each,frame,imgsz=640):
ratio = (frame.shape[0] /384 , frame.shape[1] / imgsz)
each[[0, 2]] *= ratio[1]
each[[1, 3]] *= ratio[0]
return each
#---------------------helmet_detect----------------------------------------------------------------------------
def helmet_detect(face_model,frame):
anchors = np.fromfile('C:/Users/suso/Desktop/yolov5_fire/yolov5_fire_priorbox_384-640.txt',sep=' ')
anchors = anchors.reshape(-1,5)
imgsz = 640
img = img_preprocess(frame,imgsz)
session = onnxruntime.InferenceSession(face_model)
in_name = [input.name for input in session.get_inputs()][0]
out_name = [output.name for output in session.get_outputs()]
pred = session.run(out_name,{in_name: img})
x1 = np.array(pred[0]).reshape(-1, 6)
x2 = np.array(pred[1]).reshape(-1, 6)
x3 = np.array(pred[2]).reshape(-1, 6)
print(x3.shape,max(x3[:,4]))
try:
# Save x1 to file
np.savetxt("C:/Users\suso\Desktop/yolov5_fire/x1.txt", x1, delimiter=' ', fmt='%f')
# Save x2 to file
np.savetxt("C:/Users\suso\Desktop/yolov5_fire/x2.txt", x2, delimiter=' ', fmt='%f')
# Save x3 to file
np.savetxt("C:/Users\suso\Desktop/yolov5_fire/x3.txt", x3, delimiter=' ', fmt='%f')
except Exception as e:
print(f"An error occurred: {str(e)}")
out_data_raw = np.vstack((x1,x2,x3))
np.savetxt("C:/Users\suso\Desktop/yolov5_fire/x4.txt", out_data_raw, delimiter=' ', fmt='%f')
output_from_txt = decode_output(out_data_raw,anchors)
print(len(pred),pred[0].shape)
print("ffffffffff",max(np.array(pred[0]).reshape(-1, 6)[:,4]))
# pred = py_cpu_nms(np.array(pred[0]).reshape(-1, 6), 0.2, 0.45)
pred = py_cpu_nms(output_from_txt, 0.2, 0.45)
return pred,img
#---------------draw--------------------------------------------
def drawHelmetBox(frame,bbox):
print(bbox)
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 0), 3)
label = f'{float(bbox[4]*bbox[5]):.2f}'
cv2.putText(frame, label, (int(bbox[0]), int(bbox[1])+20), 0, 1, [0, 255, 0], thickness=2, lineType=cv2.LINE_AA)
cv2.imshow('frame',frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
# Rescale boxes (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
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