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bug修复

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wlkjyy 2023-08-18 02:53:42 +08:00 committed by GitHub
parent c087f85445
commit 5edcc4544d
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1 changed files with 23 additions and 1 deletions
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predict.py
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@ -19,7 +19,7 @@ image_path = os.listdir("./data")
# 随机选取一张图片 # 随机选取一张图片
# inp = input('请输入图片名称：') # inp = input('请输入图片名称：')
image_path = "./data/" + random.choice(image_path) image_path = "./data/" + random.choice(image_path)
# image_path = './sample/' + inp # image_path = './123.png'
weight = "./yolov3-tiny_17000.weights" weight = "./yolov3-tiny_17000.weights"
cfg = "./yolov3-tiny.cfg" cfg = "./yolov3-tiny.cfg"
img = cv2.imread(image_path) img = cv2.imread(image_path)
@ -82,6 +82,7 @@ for i, j in enumerate(down_img):
location_down[i+1] = [img[j[1]:j[1]+j[3], j[0]:j[0]+j[2]].astype('float64') / 255.0, j] location_down[i+1] = [img[j[1]:j[1]+j[3], j[0]:j[0]+j[2]].astype('float64') / 255.0, j]
new_list = [] new_list = []
rate = []
for down_i, down_img_ in location_down.items(): for down_i, down_img_ in location_down.items():
# 先是读取下面的图 # 先是读取下面的图
temp = [] temp = []
@ -92,11 +93,32 @@ for down_i, down_img_ in location_down.items():
temp.append(predict[0][0]) temp.append(predict[0][0])
temp_ = temp.index(max(temp)) temp_ = temp.index(max(temp))
new_list.append([down_img_[1], temp_]) new_list.append([down_img_[1], temp_])
rate.append(max(temp))
# 如果在new_list出现重复的序号，就计算谁的概率大，概率最小那个替换为不同的
for i in range(len(new_list)):
for j in range(len(new_list)):
if i != j:
if new_list[i][1] == new_list[j][1]:
if rate[i] > rate[j]:
new_list[j][1] = new_list[j][1] + 1
else:
new_list[i][1] = new_list[i][1] + 1
select_location = []
for (box, pos) in new_list: for (box, pos) in new_list:
x, y, w, h = box[0], box[1], box[2], box[3] x, y, w, h = box[0], box[1], box[2], box[3]
x1, y1, x2, y2 = x, y, x + w, y + h x1, y1, x2, y2 = x, y, x + w, y + h
select_location.append([x1, y1, x2, y2, pos+1])
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255)) cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255))
cv2.putText(img, str(pos+1), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 1, color, thickness) cv2.putText(img, str(pos+1), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 1, color, thickness)
# print(select_location)
# 按照最后的位置排序从小到大
select_location = sorted(select_location, key=lambda x_: x_[4])
print(select_location)
cv2.imshow('1', img) cv2.imshow('1', img)
cv2.waitKey(0) cv2.waitKey(0)