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camera_error.py
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import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def open_data(datapath, file_type):
file_list = []
path_list = os.listdir(datapath)
for filename in path_list:
if file_type in filename:
file_list.append(os.path.join(datapath, filename))
return file_list
def get_data(file_name):
"""
nose:0 left_ear:1 Right_ear:2 neck:3 left_front_limb:4 right_front_limb:5
left_hind_limb:6 right_hind_limb:7 left_front_claw:8 right_front_claw:9
left_hind_claw:10 right_hind_claw:11 back:12 root_tail:13
mid_tail:14 tip_tail:15
"""
likelihood = []
df = pd.read_csv(file_name)
for i in range(3, 49, 3):
# print(df.iloc[2:, i])
likelihood.append(df.iloc[:, i])
like = np.transpose(likelihood)
# print(likelihood,len(likelihood))
return like
def camera1_max_likelihood(data_list):
likelihood_max = []
for i in range(2, 18002, 1):
likelihood_max.append(np.max(data_list[i, :]))
likelihood_max = [float(i) for i in likelihood_max]
return likelihood_max
def camera2_max_likelihood(data_list1, data_list2):
likelihood_max = []
likelihood_max1 = []
likelihood_max2 = []
for i in range(2, 18002, 1):
likelihood_max1.append(np.max(data_list1[i, :]))
likelihood_max2.append(np.max(data_list2[i, :]))
likelihood_max1 = [float(i) for i in likelihood_max1]
likelihood_max2 = [float(i) for i in likelihood_max2]
likelihood_max3 = likelihood_max1 + likelihood_max2
likelihood_max4 = np.array(likelihood_max3).reshape(18000, 2)
for i in range(0, len(likelihood_max4), 1):
likelihood_max.append(np.max(likelihood_max4[i, :]))
return likelihood_max
def camera3_max_likelihood(data_list1, data_list2, data_list3):
likelihood_max = []
likelihood_max1 = []
likelihood_max2 = []
likelihood_max3 = []
for i in range(2, 18002, 1):
likelihood_max1.append(np.max(data_list1[i, :]))
likelihood_max2.append(np.max(data_list2[i, :]))
likelihood_max3.append(np.max(data_list3[i, :]))
likelihood_max1 = [float(i) for i in likelihood_max1]
likelihood_max2 = [float(i) for i in likelihood_max2]
likelihood_max3 = [float(i) for i in likelihood_max3]
likelihood_max_a = likelihood_max1 + likelihood_max2 + likelihood_max3
likelihood_max_b = np.array(likelihood_max_a).reshape(18000, 3)
for i in range(0, len(likelihood_max_b), 1):
likelihood_max.append(np.max(likelihood_max_b[i, :]))
return likelihood_max
def camera4_max_likelihood(data_list1, data_list2, data_list3, data_list4):
likelihood_max = []
likelihood_max1 = []
likelihood_max2 = []
likelihood_max3 = []
likelihood_max4 = []
for i in range(2, 18002, 1):
likelihood_max1.append(np.max(data_list1[i, :]))
likelihood_max2.append(np.max(data_list2[i, :]))
likelihood_max3.append(np.max(data_list3[i, :]))
likelihood_max4.append(np.max(data_list4[i, :]))
likelihood_max1 = [float(i) for i in likelihood_max1]
likelihood_max2 = [float(i) for i in likelihood_max2]
likelihood_max3 = [float(i) for i in likelihood_max3]
likelihood_max4 = [float(i) for i in likelihood_max4]
likelihood_max_a = likelihood_max1 + likelihood_max2 + likelihood_max3 +likelihood_max4
likelihood_max_b = np.array(likelihood_max_a).reshape(18000, 4)
for i in range(0, len(likelihood_max_b), 1):
likelihood_max.append(np.max(likelihood_max_b[i, :]))
return likelihood_max
if __name__ == '__main__':
# open_data('D:/3D_behavior/Spontaneous_behavior/results/BeAMapping','rec-115-G1-20210919114230_Movement_Labels.csv')
file_list = open_data('D:/3D_behavior/looming_behavior/YJL-camera-test/data',
'DLC_resnet50_black_miceOct24shuffle1_1030000.csv')
camera1_data = get_data(file_list[5])
camera1_likelihood_max = camera1_max_likelihood(camera1_data)
# plt.boxplot(likelihood_max)
camera2_data1 = get_data(file_list[18])
camera2_data2 = get_data(file_list[19])
camera2_likelihood_max = camera2_max_likelihood(camera2_data1, camera2_data2)
camera3_data1 = get_data(file_list[24])
camera3_data2 = get_data(file_list[25])
camera3_data3 = get_data(file_list[19])
camera3_likelihood_max = camera3_max_likelihood(camera3_data1, camera3_data2, camera3_data3)
camera4_data1 = get_data(file_list[1])
camera4_data2 = get_data(file_list[2])
camera4_data3 = get_data(file_list[3])
camera4_data4 = get_data(file_list[4])
camera4_likelihood_max = camera4_max_likelihood(camera4_data1, camera4_data2, camera4_data3, camera4_data4)
data = pd.DataFrame({
"1_camera": camera1_likelihood_max,
"2_camera": camera2_likelihood_max,
"3_camera": camera3_likelihood_max,
"4_camera": camera4_likelihood_max
})
fig = plt.figure(figsize=(15, 10), dpi=300)
ax1 = fig.add_subplot(121)
ax1.set_title('Different Camera of likelihood', fontsize=11)
ax1 = data.boxplot(fontsize=11, grid=False)
# plt.show()
df = np.transpose(data)
camera_1 = df.iloc[0]
camera_2 = df.iloc[1]
camera_3 = df.iloc[2]
camera_4 = df.iloc[3]
# # Create a plot
# ax.violinplot([camera_1, camera_2, camera_3, camera_4], points=40, widths=0.5)
# ax.set_xticklabels(["1_camera", "2_camera", "3_camera", "4_camera"])
# # data.violinplot()
# fig = plt.figure(figsize=(15, 10), dpi=300)
ax2 = fig.add_subplot(122)
ax2.set_title('Different Camera of likelihood', fontsize=11)
fig = ax2.violinplot([camera_1, camera_2, camera_3, camera_4], points=40, widths=0.5)
plt.show(ax1, ax2)