def test_one_picture(file_path='./datas/img_person/0.png',
save_path='./test/test_100/0.png',
show_org=False,
model_path=None):
"""
只测试一张图片
:param file_path: 输入单张图片完成的绝对路径[str]
:param save_path: 保存单张图片的绝对路径[str]
:param show_org: 是否展示原始人工评语[boolean]
:param model_path: 测试模型路径[str]
:return:
"""
st = time.time()
img_data = generator.load_one_img(
file_path=file_path,
image_size=Params.image_size,
preprocess_method=Params.preprocess_method)
predict_model = rebuild_decoder(model_path=model_path)
predict_text = predict(image_input=img_data, predict_model=predict_model)
sh.show_image(file_path=file_path,
predict_text=predict_text,
save_path=save_path,
show_org=show_org)
en = time.time()
print(predict_text)
print(en - st)
def test_model(test_info=None,
show_org=True,
save_root_path='./test/test_50/',
model_path=None):
"""
:param test_info: 测试数据, e.g (test_lst, test_texts, test_img) [tuple]
:param show_org: 是否展示人工评语[boolean]
:param save_root_path: 保存测试结果的根路径[str]
:param model_path: 测试所需要的模型[str]
:return:
"""
names, texts, images = test_info
predict_model = rebuild_decoder(model_path=model_path)
for name, text, image in zip(names, texts, images):
file_path = os.path.join(Params.img_data_path, str(name) + '.png')
save_path = os.path.join(save_root_path, str(name) + '.png')
predict_text = predict(image_input=image, predict_model=predict_model)
print("图片标号：" + str(name))
original_text = "人工描述：" + text
print(original_text)
sh.show_image(file_path=file_path,
original_text=original_text,
predict_text=predict_text,
save_path=save_path,
show_org=show_org)
print(predict_text)
def hack(img_name):
'''
HACK Recognize a CAPTCHA image
Inputs:
img_name: filename of image
Outputs:
digits: 1x5 matrix, 5 digits in the input CAPTCHA image.
'''
data = np.load('hack_data.npz')
# YOUR CODE HERE (you can delete the following code as you wish)
x_train = data['x_train']
y_train = data['y_train']
# begin answer
image = extract_image.extract_image(img_name)
digits = knn.knn(image, x_train, y_train, 5)
show_image.show_image(image)
# end answer
return digits
# face detecting
facerect = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
if len(facerect) > 0:
print('face detected')
color = (255, 255, 255)
for rect in facerect:
x, y = rect[0:2]
width, height = rect[2:4]
image = frame[y - 10:y + height, x:x + width]
result = model.predict(image)
if result == 0: # owner
print('Owner detected!')
show_image()
else: # not owner
print('You are not the owner.')
# waiting key
k = cv2.waitKey(100)
# Press Esc to exit...
if k == 27:
break
# Release
cap.release()
cv2.destroyAllWindows()
import argparse
import datetime
import zipfile
import time
from show_image import show_image
parser = argparse.ArgumentParser(description="Webcam zipped image viewer.")
parser.add_argument("input_file",
help="log .zip file containing the webcam images")
parser.add_argument("--start",
default="2016-01-01 00:00:00",
help="(YYYY-MM-DD HH:MM:SS) start time")
args = parser.parse_args()
filename = args.input_file
time_start = datetime.datetime.strptime(args.start, "%Y-%m-%d %H:%M:%S")
zf = zipfile.ZipFile(filename)
time_current = time_start
real_time_current = datetime.datetime.now()
while True:
try:
show_image(zf, time_current)
except:
print "Can't show image at time {}.".format(time_current)
while datetime.datetime.now().second == real_time_current.second:
time.sleep(0.01)
real_time_current = datetime.datetime.now()
time_current += datetime.timedelta(seconds=1.0)
from show_image import show_image from video_demo import video_demo from split import split from logic import logic from equal_hist import equal_hist from image_enhancing import image_enhancement # Testing OpenCV ''' show_image() video_demo() split() logic() equal_hist() ''' # Experiment: Image Enhancement image_enhancement()
from extract_image import extract_image
from show_image import show_image
import numpy as np
x_train = np.zeros((5 * 100, 140))
y_train = np.zeros((5 * 100, ))
for i in range(20):
filename = 'captcha_train_data/' + str(i) + '.jpg'
x_train[5 * i:5 * i + 5] = extract_image(filename)
show_image(x_train[5 * i:5 * i + 5])
label = input('label ' + str(i))
for j in range(5):
y_train[5 * i + j] = eval(label[j])
print(y_train[5 * i:5 * i + 5])
np.savez('hack_data.npz', x_train=x_train, y_train=y_train)
def onclick(event):
if event.xdata:
time = pltdates.num2date(event.xdata)
print "Retrieve image from {}...".format(time)
show_image(zf, time)
clasf_name = clasf[u'name']
if u'Triage' in clasf_name:
print('The classifier is Remote Triage')
for item in clasf[u'classes']:
if u'Prone' in item[u'class']:
situation.append('Prone person probability (%0.0f%%)' %
(item['score'] * 100.0))
else:
print(item[u'class'])
print(item[u'score'])
# Find the right face information
for face_im in faces[u'images']:
if filename in face_im[u'image']:
for face in face_im[u'faces']:
face_type.append(
'%s (age %d-%d)' %
(face[u'gender'][u'gender'], face[u'age'][u'min'],
face[u'age'][u'max']))
pos = (face[u'face_location'][u'left'],
face[u'face_location'][u'top'],
face[u'face_location'][u'width'],
face[u'face_location'][u'height'])
face_pos.append(pos)
show_image(filename, 'LieOrStand2/' + filename, situation, face_type,
face_pos)
# Debug variable content so Watson does not get a query for every development iteration
# results = {u'images': [{u'image': u'LieOrStand2.zip/A.jpg', u'classifiers': [{u'classes': [{u'score': 0.889, u'class': u'Erect'}], u'classifier_id': u'RemoteTriage_482484271', u'name': u'Remote Triage'}, {u'classes': [{u'score': 0.798, u'class': u'person'}, {u'score': 0.903, u'class': u'Parkour', u'type_hierarchy': u'/sport/Parkour'}, {u'score': 0.922, u'class': u'sport'}, {u'score': 0.5, u'class': u'skateboarding', u'type_hierarchy': u'/sport/skating/skateboarding'}, {u'score': 0.502, u'class': u'skating'}, {u'score': 0.794, u'class': u'clothing'}, {u'score': 0.964, u'class': u'gray color'}, {u'score': 0.793, u'class': u'charcoal color'}], u'classifier_id': u'default', u'name': u'default'}]}, {u'image': u'LieOrStand2.zip/D.jpg', u'classifiers': [{u'classes': [{u'score': 0.907, u'class': u'Prone'}], u'classifier_id': u'RemoteTriage_482484271', u'name': u'Remote Triage'}, {u'classes': [{u'score': 0.64, u'class': u'stocking', u'type_hierarchy': u'/hosiery/stocking'}, {u'score': 0.725, u'class': u'hosiery'}, {u'score': 0.561, u'class': u'boothose', u'type_hierarchy': u'/hosiery/boothose'}, {u'score': 0.535, u'class': u'pogo stick'}, {u'score': 0.5, u'class': u'belt', u'type_hierarchy': u'/accessory/belt'}, {u'score': 0.501, u'class': u'accessory'}, {u'score': 0.599, u'class': u'sport'}, {u'score': 0.599, u'class': u'knot'}, {u'score': 0.601, u'class': u'clothing'}, {u'score': 0.633, u'class': u'maroon color'}, {u'score': 0.512, u'class': u'purple color'}], u'classifier_id': u'default', u'name': u'default'}]}, {u'image': u'LieOrStand2.zip/C.jpg', u'classifiers': [{u'classes': [{u'score': 0.907, u'class': u'Prone'}], u'classifier_id': u'RemoteTriage_482484271', u'name': u'Remote Triage'}, {u'classes': [{u'score': 0.851, u'class': u'Parkour', u'type_hierarchy': u'/sport/Parkour'}, {u'score': 0.851, u'class': u'sport'}, {u'score': 0.5, u'class': u'nylons', u'type_hierarchy': u'/hosiery/nylons'}, {u'score': 0.508, u'class': u'hosiery'}, {u'score': 0.793, u'class': u'footwear'}, {u'score': 0.8, u'class': u'clothing'}, {u'score': 0.923, u'class': u'charcoal color'}, {u'score': 0.909, u'class': u'gray color'}], u'classifier_id': u'default', u'name': u'default'}]}, {u'image': u'LieOrStand2.zip/B.jpg', u'classifiers': [{u'classes': [{u'score': 0.88, u'class': u'Prone'}], u'classifier_id': u'RemoteTriage_482484271', u'name': u'Remote Triage'}, {u'classes': [{u'score': 0.597, u'class': u'ramp', u'type_hierarchy': u'/mechanical device/machine/inclined plane/ramp'}, {u'score': 0.597, u'class': u'inclined plane'}, {u'score': 0.598, u'class': u'machine'}, {u'score': 0.602, u'class': u'mechanical device'}, {u'score': 0.544, u'class': u'khukuri (knife)', u'type_hierarchy': u'/weapon/knife/khukuri (knife)'}, {u'score': 0.545, u'class': u'knife'}, {u'score': 0.556, u'class': u'weapon'}, {u'score': 0.511, u'class': u'Repairing'}, {u'score': 0.51, u'class': u'Earthquake'}, {u'score': 0.5, u'class': u'stoop', u'type_hierarchy': u'/porch/stoop'}, {u'score': 0.5, u'class': u'porch'}, {u'score': 0.597, u'class': u'lever'}, {u'score': 0.98, u'class': u'gray color'}], u'classifier_id': u'default', u'name': u'default'}]}, {u'image': u'LieOrStand2.zip/IMG_4132.jpg', u'classifiers': [{u'classes': [{u'score': 0.784, u'class': u'Erect'}], u'classifier_id': u'RemoteTriage_482484271', u'name': u'Remote Triage'}, {u'classes': [{u'score': 0.57, u'class': u'lever', u'type_hierarchy': u'/bar/lever'}, {u'score': 0.659, u'class': u'bar'}, {u'score': 0.538, u'class': u'handlebar', u'type_hierarchy': u'/bar/handlebar'}, {u'score': 0.531, u'class': u'skateboarding', u'type_hierarchy': u'/sport/skating/skateboarding'}, {u'score': 0.533, u'class': u'skating'}, {u'score': 0.534, u'class': u'sport'}, {u'score': 0.5, u'class': u'stabilizer bar'}, {u'score': 0.599, u'class': u'weapon'}, {u'score': 0.599, u'class': u'sports equipment'}, {u'score': 0.893, u'class': u'gray color'}, {u'score': 0.581, u'class': u'ash grey color'}], u'classifier_id': u'default', u'name': u'default'}]}], u'custom_classes': 3, u'images_processed': 5}
# faces = {u'images': [{u'image': u'LieOrStand2.zip/B.jpg', u'faces': [{u'gender': {u'gender': u'MALE', u'score': 0.9846315}, u'age': {u'max': 28, u'score': 0.83206326, u'min': 24}, u'face_location': {u'width': 136, u'top': 757, u'left': 1296, u'height': 146}}]}, {u'image': u'LieOrStand2.zip/A.jpg', u'faces': [{u'gender': {u'gender': u'MALE', u'score': 0.99685293}, u'age': {u'max': 40, u'score': 0.2921676, u'min': 32}, u'face_location': {u'width': 135, u'top': 747, u'left': 1730, u'height': 137}}]}, {u'image': u'LieOrStand2.zip/D.jpg', u'faces': [{u'gender': {u'gender': u'MALE', u'score': 0.9732676}, u'age': {u'max': 28, u'score': 0.99969834, u'min': 25}, u'face_location': {u'width': 175, u'top': 644, u'left': 1170, u'height': 205}}]}, {u'image': u'LieOrStand2.zip/IMG_4132.jpg', u'faces': []}, {u'image': u'LieOrStand2.zip/C.jpg', u'faces': [{u'gender': {u'gender': u'MALE', u'score': 0.8604275}, u'age': {u'max': 41, u'score': 0.23711067, u'min': 32}, u'face_location': {u'width': 179, u'top': 794, u'left': 724, u'height': 190}}]}], u'images_processed': 5}
from hack import hack
from extract_image import extract_image
from show_image import show_image
import scipy.misc
import numpy as np
N_train = 100
y = []
for i in range(N_train):
path = './checkcode/' + str(i) + '.aspx'
m = scipy.misc.imread(path, mode='L')
d1 = m[4:18, 4:14].reshape(140)
d2 = m[4:18, 13:23].reshape(140)
d3 = m[4:18, 22:32].reshape(140)
d4 = m[4:18, 31:41].reshape(140)
d5 = m[4:18, 40:50].reshape(140)
d = np.vstack((d1, d2, d3, d4, d5)) # d.shape = (5, 140)
show_image(d)
if (i == 0):
x = np.copy(d)
else:
x = np.vstack((x, d))
label = list(input("Enter 5 labels here: "))
y.append(label)
y = np.array(y)
y = y.reshape(y.shape[0] * y.shape[1])
print(x.shape, y.shape)
np.savez('hack_data.npz', x_train=x, y_train=y)
print('save done')
def onclick(event):
if event.xdata:
time = pltdates.num2date(event.xdata)
print "Retrieve image from {}...".format(time)
show_image(zf, time)
import argparse
import datetime
import zipfile
import time
from show_image import show_image
parser = argparse.ArgumentParser(description="Webcam zipped image viewer.")
parser.add_argument("input_file", help="log .zip file containing the webcam images")
parser.add_argument("--start", default="2016-01-01 00:00:00", help="(YYYY-MM-DD HH:MM:SS) start time")
args = parser.parse_args()
filename = args.input_file
time_start = datetime.datetime.strptime(args.start, "%Y-%m-%d %H:%M:%S")
zf = zipfile.ZipFile(filename)
time_current = time_start
real_time_current = datetime.datetime.now()
while True:
try:
show_image(zf, time_current)
except:
print "Can't show image at time {}.".format(time_current)
while datetime.datetime.now().second == real_time_current.second:
time.sleep(0.01)
real_time_current = datetime.datetime.now()
time_current += datetime.timedelta(seconds=1.0)
"score": 0.98,
"class": "gray color"
}
],
"classifier_id": "default",
"name": "default"
}
]
}
],
"custom_classes": 3,
"images_processed": 5
}
for im in results['images']:
print(' ')
fileparts = os.path.split(im['image'])
filename = fileparts[1]
print(filename)
overlay = ''
for clasf in im['classifiers']:
clasf_name = clasf['name']
if clasf_name is 'Remote Triage':
for item in clasf['classes']:
if item['class'] is 'Prone':
overlay = 'Prone person probability (%f%%)' % (item['score'] * 100.0)
print('Prone person probability ({}%)'.format(item['score'] * 100.0))
show_image('LieOrStand2/' + filename, overlay)
import os
import numpy as np
from extract_image import extract_image
from show_image import show_image
path = "./download/res"
path_test = "./download/res/0.gif"
test = extract_image(path_test)
num_of_image, num_of_input = test.shape
features = np.zeros((num_of_image * 1000, num_of_input))
label = np.zeros((num_of_image * 1000, 1))
i = 0
for file in os.listdir(path):
full_path = os.path.join(path, file)
s_x = extract_image(full_path)
show_image(s_x)
s_number = input("Please input the label\n")
if s_number[0] == 'q':
break
features[i:i + 5, :] = s_x
for j in range(5):
label[i + j, 0] = int(s_number[j])
i = i + 5
np.savez("hack_data.npz", x_train=features, y_train=label)