def result(self, image_path):
image = cv2.imread(image_path) # 图片读取
bboxes_pr, layer_n, best_bboxes = self.predict(image) # 预测结果
print(bboxes_pr)
bboxes_pr, layer_n = correct_bboxes(bboxes_pr, layer_n)
print(bboxes_pr)
print(layer_n)
print(best_bboxes)
def result(self, image_path):
image = cv2.imread(image_path) # 图片读取
org_h, org_w, org_c, bboxes_pr, layer_n, best_bboxes = self.predict(
image) # 预测结果
bboxes_pr, layer_n, best_bboxes = correct_bboxes(
bboxes_pr, layer_n, best_bboxes)
print(bboxes_pr)
print(layer_n)
return org_h, org_w, org_c, bboxes_pr, layer_n
def result(self, image_path):
image = cv2.imread(image_path) # 图片读取
bboxes_pr, layer_n = self.predict(image) # 预测结果
print(bboxes_pr)
bboxes_pr, layer_n = correct_bboxes(bboxes_pr, layer_n)
major_pre = cls_major_result(int(bboxes_pr[0][-1]))
print(major_pre)
print(layer_n)
if self.write_image:
image = utils.draw_bbox(image,
bboxes_pr,
show_label=self.show_label)
drawed_img_save_to_path = str(image_path).split("/")[-1]
cv2.imwrite(drawed_img_save_to_path, image)
end_time0 = time.time()
new_classes = {v: k for k, v in classes_id39.items()}
print("加载时间：", end_time0 - start_time)
cls_list = os.listdir(img_root)
nore = 0
error_nu = 0
rig_0 = 0
for c in tqdm(cls_list):
img_dir = img_root + "/" + c
save_dir = save_root + "/" + c
if not os.path.exists(save_dir): os.mkdir(save_dir)
for img in tqdm(os.listdir(img_dir)):
img_path = img_dir + "/" + img
image = cv2.imread(img_path) # 图片读取
bboxes, layer_n, best_bboxes = Y.predict(image)
bboxes, layer_n, best_bboxes = correct_bboxes(bboxes, layer_n, best_bboxes) # 矫正输出结果
bboxes, layer_n = get_potatoml(bboxes, layer_n) # 根据输出结果对中大红薯，中大土豆做输出
he = he_foods(bboxes)
# print(img, bboxes)
image_detect = utils.draw_bbox(image, bboxes, show_label=True)
if len(bboxes) > 0:
cls = int(bboxes[0][-1])
score = round(bboxes[0][-2], 2)
cls_name = new_classes[int(cls)]
print(cls, classes_id39[c], he)
save_dir_cls = save_dir + "/{}".format(cls_name)
if not os.path.exists(save_dir_cls): os.mkdir(save_dir_cls)
acc_80_jpg = 0
acc_60_jpg = 0
acc_30_jpg = 0
acc_00_jpg = 0
error_jpg = 0
clses = os.listdir(img_root)
for c in tqdm(clses):
if c not in ["hu", "others"]:
img_dir = img_root + "/" + c
for img in os.listdir(img_dir):
if img.endswith("jpg"):
all_jpg += 1
img_path = img_dir + "/" + img
end_time1 = time.time()
bboxes_p, layer_, best_bboxes = Y.result(img_path)
bboxes_pr, layer_n, best_bboxes = correct_bboxes(
bboxes_p, layer_, best_bboxes) # 矫正输出结果
bboxes_pr, layer_n = get_potatoml(
bboxes_pr, layer_n) # 根据输出结果对中大红薯，中大土豆做输出
# 图片保存
image = cv2.imread(img_path)
if len(bboxes_pr) == 0:
acc_00_jpg += 1
if not os.path.exists(save_dir + "/noresult"):
os.mkdir(save_dir + "/noresult")
shutil.copy(img_path,
save_dir + "/noresult" + "/" + img)
else:
image = utils.draw_bbox(image,
bboxes_pr,
show_label=True) # 画图
org_h, org_w, pred_bbox, bboxes, layer_n = Y.result(
img_path, save_dir)
best_bboxes_3 = Y.get_top_cls(pred_bbox, org_h, org_w,
3) # 获取top_n类别和置信度
best_bboxes_5 = Y.get_top_cls(pred_bbox, org_h, org_w,
5) # 获取top_n类别和置信度
best_bboxes_8 = Y.get_top_cls(pred_bbox, org_h, org_w,
9) # 获取top_n类别和置信度
best_bboxes_10 = Y.get_top_cls(pred_bbox, org_h, org_w,
10) # 获取top_n类别和置信度
if len(bboxes) == 0:
noresults += 1
shutil.copy(img_path, img_noresult_dir + "/" + img)
else:
bboxes_pr, layer_n = correct_bboxes(
bboxes, layer_n) # 矫正输出结果
# bboxes_pr, layer_n = get_potatoml(bboxes_pr, layer_n) # 根据输出结果对中大红薯，中大土豆做输出
if len(bboxes_pr) == 0:
noresults += 1
# if not os.path.exists(img_dir + "/noresult"): os.mkdir(img_dir + "/noresult")
# shutil.move(img_path, img_dir + "/noresult" + "/" + img)
else:
pre = int(bboxes_pr[0][-1])
# print(pre)
# print(clses[pre])
# print(classes_id22[clses[pre]])
if classes_id39[c] in dict(
best_bboxes_3).keys():
# food_topn_acc_b+=1
food_top3_acc_nums += 1
return bboxes_pr, layer_n
if __name__ == '__main__':
start_time = time.time()
img_dir = "C:/Users/sunyihuan/Desktop/JPGImages" # 图片文件地址
Y = YoloPredict()
end_time0 = time.time()
print("model loading time:", end_time0 - start_time)
error_c = 0
save_dir = "C:/Users/sunyihuan/Desktop/JPGImages_det"
for img in tqdm(os.listdir(img_dir)):
# print(img)
img_path = img_dir + "/" + img
end_time1 = time.time()
bboxes_pr, layer_n = Y.result(img_path)
end_time2 = time.time()
# print("predict time:", end_time2 - end_time1)
if len(bboxes_pr) != 0:
print(img)
bboxes_pr, layer_n = correct_bboxes(bboxes_pr, layer_n)
if len(bboxes_pr) != 0:
error_c += 1
image = cv2.imread(img_path)
image = utils.draw_bbox(image, bboxes_pr)
drawed_img_save_to_path = str(img_path).split("/")[-1]
drawed_img_save_to_path = save_dir + "/" + drawed_img_save_to_path.split(
".jpg")[0] + "_" + str(layer_n[0]) + ".jpg"
# print(drawed_img_save_to_path)
cv2.imwrite(drawed_img_save_to_path, image)
print(error_c)
