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free_analysis.py
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free_analysis.py
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import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
video_name = '../data/free_3D/20210104_145248_C.avi'
hours = 1/60
edge = [6, 8]
threshold = 70
# FPS = videoCapture.get(cv2.CAP_PROP_FPS)
to_path = '../data/free_3D/output/'
if_save_pic = 0
background_interval = 1000
if os.path.isdir(to_path):
if len(os.listdir(to_path)) > 0:
print('The file fold is not empty and will stop')
# return None
else:
print('The file fold is not exist and will be created.')
os.makedirs(to_path)
edge_x = edge[0]
edge_y = edge[1]
position = []
start_time = time.time()
videoCapture = cv2.VideoCapture(video_name)
success, frame_temp = videoCapture.read()
if success:
y_max, x_max, n_cha = frame_temp.shape
videoCapture.release()
background = np.ones([y_max, x_max, n_cha])
background = background * 255
background = background.astype('uint8')
videoCapture = cv2.VideoCapture(video_name)
# num_frame = 180000
fps = videoCapture.get(cv2.CAP_PROP_FPS)# 25 #TODO(JZ)
num_frame = int(fps * 60 * 60 * hours/6)
# num_frame = 180000
for K_0 in range(num_frame):
success, frame_temp = videoCapture.read()
# frame_temp = frame_temp[6:230, 144:368, 3]
background = np.minimum(frame_temp, background)
# if K_0 % (fps * 100) == 0:
if K_0 % fps == 0:
end_time = time.time()
# print("Time used: ", end_time - start_time, 's ', 'K_0: ', K_0)
print('bg computing: K_0: ', K_0)
videoCapture.release()
background = background.astype('uint8')
# plt.imshow(background)
num_frame = int(fps * 60 * 60 * hours)
# num_frame = 43300
videoCapture = cv2.VideoCapture(video_name)
# num_frame = 4001
# num_frame = 225000
start_time = time.time()
for K_0 in range(num_frame):
# while True:
# K_0=0
success, frame = videoCapture.read()
if success: # and K_0 % (fps * 10) == 0:
frame_1 = np.copy(frame)
frame_clean = frame_1 - background
# plt.imshow(frame_clean)
frame_blured = cv2.medianBlur(frame_clean, 3)
frame_grey = np.copy(frame_blured)
frame_grey[frame_grey >= 100] = 0
frame_grey[frame_grey <= 20] = 0
frame_grey[frame_grey > 0] = 255
frame_grey = cv2.medianBlur(frame_grey, 5)
# frame_grey = 255 - frame_grey
frame_grey = cv2.cvtColor(frame_grey, cv2.COLOR_RGB2GRAY)
ret, labels, stats, centroid = cv2.connectedComponentsWithStats(frame_grey, connectivity=4)
# print(stats)
K_1 = 0
for i, stat in enumerate(stats):
# stat=stats[1]
if stat[4] > 3000 and stat[4] < 5000:# and stat[2] < 50 and stat[2] > 3 and stat[3] > 3 and stat[3] < 50:
x1, y1, w, h, area = stat
K_1 += 1
position.append([centroid[i][0], centroid[i][1], K_0, K_1, x1, y1, h, w, area])
if K_0 % (fps * 10) == 0:
end_time = time.time()
print("Time used: ", end_time - start_time, 's ', 'K_0: ', K_0)
print('K_0: ', K_0)
videoCapture.release()
position_np = np.array(position)
position_name = video_name[:-4] + '_4_position.npy'
np.save(position_name, position_np)
position_name = video_name[:-4] + '_3_position.npy'
position_all = np.load(position_name)
for K_0 in range(1,4000):#len(position_all)):
if position_all[K_0,3]>2:
print(position_all[K_0])
# if K_0 % 2 == 1:
if position_all[K_0,3]-position_all[K_0-1,3] == 0:
print(position_all[K_0])
if K_0 % 2 == 1:
if position_all[K_0, 3] != 2:
print(position_all[K_0])
else:
if position_all[K_0, 3] != 1:
print(position_all[K_0])
position_1 = []
position_2 = []
for K_0 in range(6000):
if K_0 % 2 == 1:
if position_all[K_0, 3] != 2:
print(position_all[K_0])
else:
if position_all[K_0, 3] != 1:
print(position_all[K_0])
position_part=position_all[0:6000]
position_1 = position_part[position_part[:,3]==1]
position_2 = position_part[position_part[:,3]==2]
assert len(position_1) == len(position_2)
x_y = position_1[:,0:2]
np.savetxt("new_1.csv", x_y, delimiter=',')
position_1 = []
position_2 = []
# position_temp_1 =
'''
先判断是否两个有重合，再来看其他的
，如果有重合的就算了
'''
# def get_position_by_background(video_name, hours=1/60, edge=[6, 8], threshold=70, fps=25,
# to_path='../data/picture/train2020/', if_save_pic=0,
# background_interval=1000):
#
#
# if os.path.isdir(to_path):
# if len(os.listdir(to_path)) > 0 :
# print('The file fold is not empty and will stop')
# return None
# else:
# print('The file fold is not exist and will be created.')
# os.makedirs(to_path)
#
#
# edge_x = edge[0]
# edge_y = edge[1]
#
# position = []
#
# start_time = time.time()
#
# videoCapture = cv2.VideoCapture(video_name)
# success, frame_temp = videoCapture.read()
# if success:
# y_max, x_max, n_cha = frame_temp.shape
# videoCapture.release()
#
#
# background = np.zeros([y_max, x_max, n_cha])
# videoCapture = cv2.VideoCapture(video_name)
# # num_frame = 180000
#
# num_frame = int(fps * 60 * 60 * hours)
# # num_frame = 180000
# for K_0 in range(num_frame):
# success, frame_temp = videoCapture.read()
# # frame_temp = frame_temp[6:230, 144:368, 3]
# background = np.maximum(frame_temp, background)
# if K_0 % (fps * 100) == 0:
# end_time = time.time()
# # print("Time used: ", end_time - start_time, 's ', 'K_0: ', K_0)
# print('bg computing: K_0: ', K_0)
#
# videoCapture.release()
# background = background.astype('uint8')
# # plt.imshow(background)
#
#
# num_frame = int(fps * 60 * 60 * hours)
#
# videoCapture = cv2.VideoCapture(video_name)
# # num_frame = 4001
# # num_frame = 225000
# start_time = time.time()
# for K_0 in range(num_frame):
# # while True:
# # K_0=0
# success, frame = videoCapture.read()
# if success: # and K_0 % (fps * 10) == 0:
# frame_1 = np.copy(frame)
# # y_max, x_max, n_cha = frame_1.shape
# # plt.figure()
# # # plt.imshow(frame_1)
# frame_clean = background - frame_1
# # plt.imshow(frame_clean)
# # frame_new = background - frame_blured
# frame_blured = cv2.medianBlur(frame_clean, 3)
# # frame_bi = cv2.cvtColor(frame_blured, cv2.COLOR_RGB2GRAY)
# # # plt.imshow(frame_bi,'gray')
# #
# # frame_bi[frame_bi < threshold] = 0
# # frame_bi[frame_bi >= threshold] = 255
# # # frame_bi = cv2.adaptiveThreshold(frame_bi, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
# # # cv2.THRESH_BINARY, 25, 5)
# # # plt.imshow(frame_3_body,'gray')
# # frame_3_body = 255 - frame_bi.astype(np.uint8)
#
# # frame_new = background - frame_blured
# # plt.imshow(frame_blured)
#
# frame_grey = cv2.cvtColor(frame_blured, cv2.COLOR_RGB2GRAY)
#
# frame_grey[frame_grey < threshold] = 0
# frame_grey[frame_grey >= threshold] = 255
# # frame_bi = cv2.adaptiveThreshold(frame_grey, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
# # cv2.THRESH_BINARY, 11, 20)
# # plt.imshow(frame_grey)
# # plt.figure()
# # plt.imshow(frame_bi)
# ret, labels, stats, centroid = cv2.connectedComponentsWithStats(frame_grey, connectivity=4)
# # print(stats)
# K_1 = 0
# for i, stat in enumerate(stats):
# # stat=stats[1]
# if stat[4] > 50 and stat[4] < 300 and stat[2] < 50 and stat[2] > 3 and stat[3] > 3 and stat[3] < 50:
# x1, y1, w, h, area = stat
# K_1 += 1
# position.append([centroid[i][0], centroid[i][1], K_0, K_1, x1, y1, h, w, area])
#
# x2, y2 = x1 + w, y1 + h
# # print(y1 - edge,y2 + edge,x1 - edge,x2 + edge)
# y1 = max(0, y1 - edge_y)
# y2 = min(y_max, y2 + edge_y)
# x1 = max(0, x1 - edge_x)
# x2 = min(x_max, x2 + edge_x)
#
#
#
#
# # img_fly = np.copy(frame_grey)
# # # img_fly_ann = np.copy(frame_clean[y1:y2, x1:x2, :])
# # img_fly_ann = np.copy(frame_clean)
# # plt.imshow(img_fly)
# # plt.imshow(img_fly_ann)
# # if if_save_pic == 1:
# # pic_name = str(K_0) + '-' + str(x1) + '_' + str(x2) + '_' \
# # + str(y1) + '_' + str(y2) + '-' + '.jpg'
# # plt.imsave(to_path + pic_name, img_fly)
#
#
# # plt.imsave(to_path_ann + pic_name, img_fly_ann)
# # center = pose[K_0, 3], pose[K_0, 4]#, pose[K_0, 3]#x,y#
# # radius = 5 # int(radius)
# # print(K_0,' ',center,'___',y1,y2,'_',x1,x2,'\n')
# # # center = 0, 120
# # frame_2 = np.copy(frame_1)
# #
# # frame_2 = cv2.circle(frame_2, center, radius, (255, 0, 0), -1)
# # # plt.figure()
# # # plt.imshow(frame)
# # center = x1,y1#pose[K_0, 3], pose[K_0, 4] # , pose[K_0, 3]#x,y#
# # radius = 5 # int(radius)
# # # print(K_0,' ',center,'___',y1,y2,'_',x1,x2,'\n')
# # # center = 0, 120
# # # frame_2 = np.copy(frame_1)
# #
# # cv2.circle(frame_1, center, radius, (255, 0, 0), -1)
# # plt.imshow(frame_1)
# # center = x2, y2
# # radius = 5
# # cv2.circle(frame_1, center, radius, (255, 0, 0), -1)
# #
# # img_fly = np.copy(frame_1)
# # img_fly_ann = np.copy(frame_clean[y1:y2, x1:x2,:])
#
# # img_fly_ann = np.copy(frame_grey[y1:y2, x1:x2,:])
# # # print(img_fly.shape)
# # # plt.figure()
# # # plt.imshow(img_fly)
# #
# # if if_save_pic == 1:
# # pic_name = str(K_0) + '-' + str(x1) + '_' + str(x2) + '_' \
# # + str(y1) + '_' + str(y2) + '-' + '.jpg'
# # # plt.imsave(to_path + pic_name, img_fly)
# # plt.imsave(to_path_ann + pic_name, img_fly_ann)
# #
# #
# #
# # # plt.imsave(to_path+str(K_0)+'.bmp',frame)
# # plt.imsave(to_path+str(K_0)+'_'+str(i)+'.jpg',img_fly)
# # if K_1 == 0:
# # input = []
#
# # input.append(process_single_picture(img_fly))
# # if K_1 == 0:
# # onnx_input_h = 96
# # onnx_input_w = 128
# # print(img_fly.shape)
# # a = cv2.resize(img_fly, (onnx_input_w, onnx_input_h), interpolation=cv2.INTER_CUBIC)
# # # a = img_fly
# # K_1 += 1
# #
# # # if K_1 % batch_size != 0:
# # # input.append(process_single_picture(img_fly))
# #
# # if K_1 % batch_size == 0:
# # img_data_by_batch = np.array(input)
# # result = sess.run(outputs, {"x:0": img_data_by_batch})
# # conf_map, paf = result
# # conf_by_batch = transform_infer_rusult(a, conf_map)
# # pose_result.append(conf_by_batch)
# # input = []
#
# if K_0 % (fps * 100) == 0:
# end_time = time.time()
# print("Time used: ", end_time - start_time, 's ', 'K_0: ', K_0)
# print('K_0: ', K_0)
# videoCapture.release()
# return position