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predict.py
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predict.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import cv2
import numpy as np
import core.utils as utils
from PIL import Image
import tensorflow
if tensorflow.__version__.startswith('1.'):
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
if __name__ == '__main__':
"""
argv = sys.argv
if len(argv) < 5:
print('usage: python test.py gpu_id pb_file img_path_file out_path')
sys.exit()
"""
gpu_id = '0' #argv[1]
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
pb_file = 'ckpts/MosEggs_yolov3_loss=33.0915.ckpt-8.pb' #argv[2]
if not os.path.exists(pb_file):
print('pb_file=%s not exist' % pb_file)
sys.exit()
img_path_file = 'D:/datasets/MosEggs/test' #argv[3]
if not os.path.exists(img_path_file):
print('img_path_file=%s not exist' % img_path_file)
sys.exit()
out_path = 'D:/datasets/MosEggs/out' #argv[4]
if not os.path.exists(out_path):
os.makedirs(out_path)
print('test gpu_id=%s, pb_file=%s, img_file=%s, out_path=%s' % (gpu_id, pb_file, img_path_file, out_path))
num_classes = 1
input_size = 512
score_thresh = 0.3
iou_type = 'iou' #yolov4:diou, else giou
iou_thresh = 0.3
graph = tf.Graph()
return_elements = ["input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0", "pred_lbbox/concat_2:0"]
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=graph, config=config) as sess:
if os.path.isfile(img_path_file):
img = cv2.imread(img_path_file)
# 开始切图 cut
h_step = img.shape[0] // input_size
w_step = img.shape[1] // input_size
h_rest = -(img.shape[0] - input_size * h_step)
w_rest = -(img.shape[1] - input_size * w_step)
img_list = []
# 循环切图
for h in range(h_step):
for w in range(w_step):
# 划窗采样
im = img[(h * input_size) : (h * input_size + input_size), (w * input_size) : (w * input_size + input_size), :]
img_list.append(im)
img_list.append(img[(h * input_size) : (h * input_size + input_size), -input_size:, :])
for w in range(w_step - 1):
img_list.append(img[-input_size:, (w * input_size) : (w * input_size + input_size), :])
img_list.append(img[-input_size:, -input_size:, :])
predict_img_list = []
for i, ims in enumerate(img_list):
img_size = ims.shape[:2]
image_data = utils.image_preporcess(np.copy(ims), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, img_size, input_size, score_thresh)
bboxes = utils.nms(bboxes, iou_type, iou_thresh, method='nms')
out_img = ims
if len(bboxes) > 0:
image = utils.draw_bbox(ims, bboxes)
# image = Image.fromarray(image)
# image.show()
out_img = np.asarray(image)
score = bboxes[0][4]
file_path, file_name = os.path.split(img_path_file)
file, postfix = os.path.splitext(file_name)
# out_file = os.path.join(out_path, file + '_%.6f' % (score) + postfix)
# cv2.imwrite(out_file, out_img)
# print('idx=', idx, 'in_img_file=', in_img_file, 'out_file=', out_file)
predict_img_list.append(out_img)
# 将预测后的图像块再拼接起来
count_temp = 0
result_img = img.copy()
for h in range(h_step):
for w in range(w_step):
result_img[h * input_size: (h + 1) * input_size, w * input_size: (w + 1) * input_size] = predict_img_list[count_temp]
count_temp += 1
result_img[h * input_size: (h + 1) * input_size, w_rest:] = predict_img_list[count_temp][:, w_rest:]
count_temp += 1
for w in range(w_step - 1):
result_img[h_rest:, (w * input_size) : (w * input_size + input_size)] = predict_img_list[count_temp][h_rest:, :]
count_temp += 1
result_img[h_rest:, w_rest:] = predict_img_list[count_temp][h_rest:, w_rest:]
out_file = os.path.join(out_path, img_path_file.replace('.jpg', '_result.jpg'))
cv2.imwrite(out_file, result_img)
print('in_img_file=', img_path_file, 'out_file=', out_file)
elif os.path.isdir(img_path_file):
def detect_img(fim, start_h, start_w, fpredict_bboxes):
img_size = fim.shape[:2]
image_data = utils.image_preporcess(np.copy(fim), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, img_size, input_size, score_thresh)
bboxes = utils.nms(bboxes, iou_type, iou_thresh, method='nms')
for i, bbox in enumerate(bboxes):
# bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates
coor = np.array(bbox[:4], dtype=np.int32)
bbox[0] = int(start_w + coor[0])
bbox[1] = int(start_h + coor[1])
bbox[2] = int(start_w + coor[2])
bbox[3] = int(start_h + coor[3])
fpredict_bboxes.append(bbox)
img_files = os.listdir(img_path_file)
for idx, img_file in enumerate(img_files):
in_img_file = os.path.join(img_path_file, img_file)
#print('idx=', idx, 'in_img_file=', in_img_file)
if not os.path.exists(in_img_file):
print('idx=', idx, 'in_img_file=', in_img_file, ' not exist')
continue
img = cv2.imread(in_img_file)
if img is None:
print('idx=', idx, 'in_img_file=', in_img_file, ' read error')
continue
# 开始切图 cut
h_step = img.shape[0] // input_size
w_step = img.shape[1] // input_size
h_rest = -(img.shape[0] - input_size * h_step)
w_rest = -(img.shape[1] - input_size * w_step)
predict_bboxes = []
# 循环切图
for h in range(h_step):
for w in range(w_step):
# 划窗采样
im = img[(h * input_size) : (h * input_size + input_size), (w * input_size) : (w * input_size + input_size), :]
detect_img(im, h * input_size, w * input_size, predict_bboxes)
# for w_rest
im = img[(h * input_size) : (h * input_size + input_size), -input_size:, :]
detect_img(im, h * input_size, w_rest, predict_bboxes)
# for h_rest
for w in range(w_step - 1):
im = img[-input_size:, (w * input_size) : (w * input_size + input_size), :]
detect_img(im, h_rest, w * input_size, predict_bboxes)
# for h_rest and w_rest
im = img[-input_size:, -input_size:, :]
detect_img(im, h_rest, w_rest, predict_bboxes)
if len(predict_bboxes) > 0:
image = utils.draw_bbox(img, predict_bboxes)
out_img = np.asarray(image)
score = predict_bboxes[0][4]
file_path, file_name = os.path.split(in_img_file)
file, postfix = os.path.splitext(file_name)
out_file = os.path.join(out_path, file + '_%d_%.6f' % (input_size, score) + postfix)
cv2.imwrite(out_file, out_img)
print('idx=', idx, 'in_img_file=', in_img_file, 'out_file=', out_file, 'predict_bboxes.len=', len(predict_bboxes))
#break
elif os.path.isdir(img_path_file) and False:
img_files = os.listdir(img_path_file)
for idx, img_file in enumerate(img_files):
in_img_file = os.path.join(img_path_file, img_file)
# print('idx=', idx, 'in_img_file=', in_img_file)
if not os.path.exists(in_img_file):
print('idx=', idx, 'in_img_file=', in_img_file, ' not exist')
continue
img = cv2.imread(in_img_file)
if img is None:
print('idx=', idx, 'in_img_file=', in_img_file, ' read error')
continue
# 开始切图 cut
h_step = img.shape[0] // input_size
w_step = img.shape[1] // input_size
h_rest = -(img.shape[0] - input_size * h_step)
w_rest = -(img.shape[1] - input_size * w_step)
img_list = []
# 循环切图
for h in range(h_step):
for w in range(w_step):
# 划窗采样
im = img[(h * input_size) : (h * input_size + input_size), (w * input_size) : (w * input_size + input_size), :]
img_list.append(im)
img_list.append(img[(h * input_size):(h * input_size + input_size), -input_size:, :])
for w in range(w_step - 1):
img_list.append(img[-input_size:, (w * input_size):(w * input_size + input_size), :])
img_list.append(img[-input_size:, -input_size:, :])
predict_img_list = []
predict_bboxes_list = []
for i, ims in enumerate(img_list):
img_size = ims.shape[:2]
image_data = utils.image_preporcess(np.copy(ims), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, img_size, input_size, score_thresh)
bboxes = utils.nms(bboxes, iou_type, iou_thresh, method='nms')
out_img = ims
if len(bboxes) > 0:
image = utils.draw_bbox(ims, bboxes)
out_img = np.asarray(image)
for i, bbox in enumerate(bboxes):
predict_bboxes_list.append(bbox)
# score = bboxes[0][4]
# file_path, file_name = os.path.split(in_img_file)
# file, postfix = os.path.splitext(file_name)
# out_file = os.path.join(out_path, file + '_%.6f' % (score) + postfix)
# cv2.imwrite(out_file, out_img)
# print('idx=', idx, 'in_img_file=', in_img_file, 'out_file=', out_file)
predict_img_list.append(out_img)
# 将预测后的图像块再拼接起来
count_temp = 0
result_img = img.copy()
for h in range(h_step):
for w in range(w_step):
result_img[h * input_size: (h + 1) * input_size, w * input_size: (w + 1) * input_size] = predict_img_list[count_temp]
count_temp += 1
result_img[h * input_size: (h + 1) * input_size, w_rest:] = predict_img_list[count_temp][:, w_rest:]
count_temp += 1
if h_rest != 0:
for w in range(w_step - 1):
result_img[h_rest:, (w * input_size): (w * input_size + input_size)] = predict_img_list[count_temp][h_rest:,:]
count_temp += 1
result_img[h_rest:, w_rest:] = predict_img_list[count_temp][h_rest:, w_rest:]
out_file = os.path.join(out_path, img_file.replace('.jpg', '_%d.jpg' % input_size))
cv2.imwrite(out_file, result_img)
print('idx=', idx, 'in_img_file=', in_img_file, 'out_file=', out_file, 'predict_bboxes_list.len=', len(predict_bboxes_list))
break
else:
print('img_path_file=%s is error' % img_path_file)