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import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import copy
import tensorflow.keras.backend as K
import cv2
from face_detection.config import cell_size,idx_to_class,class_to_idx,class_colors
def get_objects(y_pred,p=0.5,decode_preds=True):
global tf_anchors
output_size=y_pred.shape[1]
image_size=cell_size*output_size
y_pred=copy.deepcopy(y_pred)
if decode_preds:
y_pred[...,0]=K.sigmoid(y_pred[...,0])
y_pred[...,3:5]=np.clip((K.exp(y_pred[...,3:5])*tf_anchors).numpy(),0,output_size)
# y_pred[...,3:5]=np.clip(y_pred[...,3:5],0,output_size)
objs_found=[]
idxs=np.where(y_pred[...,0]>=p)
if np.size(idxs):
for i,obj in enumerate(y_pred[idxs[0],idxs[1],idxs[2],:]):
# obj (p,x,y,w,h,c_1,c_2,c_3,c_4,c_5.......c_n)
if decode_preds:
obj[1:3]=K.sigmoid(obj[1:3]) # x,y
prob=obj[0]
obj=obj[1:]
obj[4]=np.argmax(obj[4:])
obj=obj[:5]
obj[0]=idxs[1][i]+obj[0] # center x
obj[1]=idxs[0][i]+obj[1] # center y
obj[0]=np.clip(obj[0]-(obj[2]/2),0,output_size) # xmin
obj[1]=np.clip(obj[1]-(obj[3]/2),0,output_size) # ymin
obj_name=idx_to_class[obj[4]]
# obj_details={'p':prob,'xywh':list(obj[:-1]/output_size),'class_idx':int(obj[4]),'class':obj_name} # xywh are scaled 0 to 1
obj_details=[prob,obj[4],*list(obj[:-1]/output_size)] # xywh are scaled 0 to 1 [P,C_IDX,X,Y,W,H]
objs_found.append(obj_details)
return np.array(objs_found)
def list_get_iou(bboxes1, bboxes2):
# bboxes has xywh => xmin,ymin,width,height
bboxes1 = [bboxes1[0],bboxes1[1],bboxes1[0]+bboxes1[2],bboxes1[1]+bboxes1[3]]
bboxes2 = [bboxes2[0],bboxes2[1],bboxes2[0]+bboxes2[2],bboxes2[1]+bboxes2[3]]
xA = max(bboxes1[0], bboxes2[0])
yA = max(bboxes1[1], bboxes2[1])
xB = min(bboxes1[2], bboxes2[2])
yB = min(bboxes1[3], bboxes2[3])
intersection_area = max(0, xB - xA ) * max(0, yB - yA )
box1_area = (bboxes1[2] - bboxes1[0] ) * (bboxes1[3] - bboxes1[1] )
box2_area = (bboxes2[2] - bboxes2[0] ) * (bboxes2[3] - bboxes2[1] )
iou = intersection_area / float(box1_area + box2_area - intersection_area+1e-6)
return iou
def np_iou(bboxes1,bboxes2):
# bboxes has xywh => xmin,ymin,width,height
boxes1_x1 = bboxes1[:,0]
boxes1_y1 = bboxes1[:,1]
boxes1_x2 = boxes1_x1 + bboxes1[:,2]
boxes1_y2 = boxes1_y1 + bboxes1[:,3]
boxes2_x1 = bboxes2[:,0]
boxes2_y1 = bboxes2[:,1]
boxes2_x2 = boxes2_x1 + bboxes2[:,2]
boxes2_y2 = boxes2_y1 + bboxes2[:,3]
xmins = np.maximum(boxes1_x1,boxes2_x1)
ymins = np.maximum(boxes1_y1,boxes2_y1)
xmaxs = np.minimum(boxes1_x2,boxes2_x2)
ymaxs = np.minimum(boxes1_y2,boxes2_y2)
intersection = np.clip((xmaxs-xmins),0,None)*np.clip((ymaxs-ymins),0,None)
union = (boxes1_x2-boxes1_x1)*(boxes1_y2-boxes1_y1) + (boxes2_x2-boxes2_x1)*(boxes2_y2-boxes2_y1)
ious=intersection/((union-intersection)+1e-6)
return ious
def nms(objs_found,iou_threshold=0.2):
'''objs_found list of list:[
[p,c_idx,x,y,w,h],
[p,c_idx,x,y,w,h]
]
'''
if objs_found.size<2 or iou_threshold==1: return objs_found
objs_found=objs_found[np.argsort(objs_found[:,0])[::-1] ]# This was very important
best_boxes=[]
while len(objs_found)>0:
obj=objs_found[0]
best_boxes.append(list(obj))
objs_found=objs_found[1:].reshape(-1,6)
if len(objs_found)>0:
same_class_idxs=np.where(objs_found[:,1]==obj[1])[0] # same class_idx
same_class_objs=objs_found[same_class_idxs].reshape(-1,6)
ious=np_iou(obj[None,2:],same_class_objs[:,2:])
delete_idxs=same_class_idxs[np.where(ious>= iou_threshold)[0]]
objs_found=np.delete(objs_found,delete_idxs,axis=0)
return best_boxes
def show_objects(img,objs_found,return_img=False):
plt.imshow(img)
for i in range(len(objs_found)):
p=objs_found[i]['p']
obj=objs_found[i]['xywh']
obj_name=objs_found[i]['class']
plt.gca().add_patch(Rectangle((obj[0],obj[1]),(obj[2]),(obj[3]),linewidth=4,edgecolor=class_colors[obj_name],facecolor='none'))
plt.text(obj[0],obj[1],obj_name)
def pred_image(img,objs_found,font_scale=2,thickness=4):
def rescale(obj_found,w,h):
# xywh
obj_found[0]*=w
obj_found[1]*=h
obj_found[2]*=w
obj_found[3]*=h
return obj_found
for i in range(len(objs_found)):
# p,c_idx,x,y,w,h
p=objs_found[i][0]
obj_name=objs_found[i][1]
obj=rescale(objs_found[i][2:],img.shape[1],img.shape[0])
img=cv2.rectangle(img,(int(obj[0]),int(obj[1])),(int(obj[0]+obj[2]),int(obj[1]+obj[3])),(class_colors[obj_name]*255),thickness)
img=cv2.putText(img,obj_name,(int(obj[0]),int(obj[1])),cv2.FONT_HERSHEY_SIMPLEX,font_scale, (0,0,0), thickness, lineType=cv2.LINE_AA)
# draw_text(img, "world", font_scale=4, pos=(10, 20 + h), text_color_bg=(255, 0, 0))
return img |