def __load__(self,crop_x=(0,-1),crop_y=(0,-1)):
self.__IMG_input__=cv2.imread(self.__filename__)
if len(self.__IMG_input__.shape)==3:
self.__IMG_gray__=cv2.imread(self.__filename__,0)[crop_y[0]:crop_y[1],crop_x[0]:crop_x[1]]
else:
self.__IMG_gray__=cv2.imread(self.__filename__,0)[crop_y[0]:crop_y[1],crop_x[0]:crop_x[1]]
self.__IMG_input__=None
def make_sets():
training_data = []
training_labels = []
prediction_data = []
prediction_labels = []
for emotion in emotions:
training, prediction = get_files(emotion)
# Append data to training and prediction list, and generate labels 0-7
for item in training:
image = cv2.imread(item) # open image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert to grayscale
clahe_image = clahe.apply(gray)
landmarks_vectorised = get_landmarks(clahe_image)
if landmarks_vectorised == "error":
pass
else:
training_data.append(landmarks_vectorised) # append image array to training data list
training_labels.append(emotions.index(emotion))
for item in prediction:
image = cv2.imread(item)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
clahe_image = clahe.apply(gray)
landmarks_vectorised = get_landmarks(clahe_image)
if landmarks_vectorised == "error":
pass
else:
prediction_data.append(landmarks_vectorised)
prediction_labels.append(emotions.index(emotion))
return training_data, training_labels, prediction_data, prediction_labels
def template_matching():
img = cv2.imread('messi.jpg',0)
img2 = img.copy()
template = cv2.imread('face.png',0)
w, h = template.shape[::-1]
# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
for meth in methods:
img = img2.copy()
method = eval(meth)
# Apply template Matching
res = cv2.matchTemplate(img,template,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(img,top_left, bottom_right, 255, 2)
plt.subplot(121),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.suptitle(meth)
plt.show()
def main():
im1 = cv2.imread('/Users/grub/Desktop/Yale/BabyBlendr/python/image.jpg')
im2 = cv2.imread('/Users/grub/Desktop/Yale/BabyBlendr/python/image3.jpg')
avgR1, avgG1, avgB1 = getRGB(im1)
avgR2, avgG2, avgB2 = getRGB(im2)
avgR = (avgR1+avgR2)/2
avgG = (avgG1+avgG2)/2
avgB = (avgB1+avgB2)/2
result = np.array([avgR, avgG, avgB])
light = np.array([255, 218, 190])
medium = np.array([180, 138, 120])
dark = np.array([60, 46, 40])
light_diff = np.linalg.norm(result-light)
medium_diff = np.linalg.norm(result-medium)
dark_diff = np.linalg.norm(result-dark)
result_diff = [light_diff, medium_diff, dark_diff]
if (min(result_diff) == light_diff):
print "Light skin tone"
elif (min(result_diff) == medium_diff):
print "Medium skin tone"
elif (min(result_diff) == dark_diff):
print "Dark skin tone"
def coolBlack():
IMAGE_WEIGHT = 0.5
image = cv2.imread("G:/Filters/wasim.jpg",0)
black = cv2.imread("G:/Filters/black5.jpg",0)
black = cv2.resize(black, image.shape[::-1])
res1 = cv2.addWeighted(image, IMAGE_WEIGHT, black, 1 - IMAGE_WEIGHT, 1)
#NORMALIZE IMAGES
image = np.float32(image)
black = np.float32(black)
image /= 255
black /= 200
res = image*black
cv2.imshow("RES", res)
cv2.waitKey(0)
fname = "G:/Filtes/temp.jpg"
cv2.imwrite(fname, res)
res = cv2.imread(fname, 0)
cv2.imshow("BLACK", res)
cv2.waitKey(0)
def test_n_largest_area_contours_images__with_invert(self):
# given
image = cv2.imread("./images/SnipNLargestAreaContours/"
"test_n_largest_area_contours_image__with_invert__input_image.png",
cv2.IMREAD_GRAYSCALE)
n = 2
invert_flag = True
snip_n_largest_area_contours = SnipNLargestAreaContours(image, n, invert_flag)
expected_largest_contour_image_1 = cv2.imread(
"./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_1.png",
flags=cv2.IMREAD_GRAYSCALE)
expected_largest_contour_image_2 = cv2.imread(
"./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_2.png",
flags=cv2.IMREAD_GRAYSCALE)
expected_n_largest_area_contours_images = [expected_largest_contour_image_1, expected_largest_contour_image_2]
# when
actual_n_largest_area_contours_images = snip_n_largest_area_contours.n_largest_area_contours_images
# that
self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[0],
expected_n_largest_area_contours_images[0]), True)
self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[1],
expected_n_largest_area_contours_images[1]), True)
def Color_Features_Extract(img_folder):
print "Color_Features_Extract Start"
starttime = datetime.datetime.now()
back = np.array([255,128,128])
image_num = len(os.listdir(seg_img_folder))
Color_Features = []
for index, image_name in enumerate(os.listdir(img_folder)):
image_path = img_folder + str("/") +image_name
image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2LAB)
rows, columns, lab = image.shape
# Make densely-sampling color features
pixel_index = 0
for x in range(rows):
for y in range(columns):
if pixel_index % 9 == 0 and np.array_equal(image[x][y],back) == False:
Color_Features.append(image[x][y].tolist())
pixel_index += 1
# Get CodeBook of Color_Features
Color_Features = np.float32(Color_Features)
# Define criteria = ( type, max_iter = 10 , epsilon = 1.0 )
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
# Set flags (Just to avoid line break in the code)
flags = cv2.KMEANS_RANDOM_CENTERS
# Apply KMeans
compactness,labels,centers = cv2.kmeans(Color_Features,800,None,criteria,10,flags)
Image_Color_Features = [[0 for x in range(800)] for y in range(image_num)]
color_index = 0
for image_index, image_name in enumerate(os.listdir(img_folder)):
image_path = img_folder + str("/") +image_name
image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2LAB)
rows, columns, lab = image.shape
pixel_index = 0
for x in range(rows):
for y in range(columns):
if pixel_index % 9 == 0 and np.array_equal(image[x][y],back) == False:
Image_Color_Features[image_index][labels[color_index]] += 1
color_index += 1
pixel_index += 1
print image_name
endtime = datetime.datetime.now()
print "Time: " + str((endtime - starttime).seconds) + "s"
print "Color_Features_Extract End"
return Image_Color_Features
def __init__(self, filename):
super(CustomJpeg, self).__init__()
self.filename = filename
# 0 is to read as grayscale
self.figure = cv2.imread(self.filename, 0)
# generate image RGB -> YCbCr
self.figure_ycbcr = cv2.cvtColor(
cv2.imread(self.filename), cv2.COLOR_BGR2YCR_CB)
# got each element from YCbCr
self.y, self.cb, self.cr = self._split_channel_(self.figure_ycbcr)
if not _options.output:
self.output_filename = self.filename.replace(
self.filename.split('.')[-1], 'cjpeg')
else:
self.output_filename = _options.output
if self.figure is None:
raise FileNotFound(self.filename)
self.shape = self.figure.shape
self.pixs = _options.size
self.scrambled = np.array([])
self.bitarray = Bitset()
self.bitarray.name = self.output_filename
self.bitarray.verbose = False
def realisticTexturemap(H_G_M, scale):
map_img = cv2.imread('Images/ITUMap.bmp')
point = getMousePointsForImageWithParameter(map_img, 1)[0]
texture = cv2.imread('Images/ITULogo.jpg')
#texture = cv2.cvtColor(texture,cv2.COLOR_BGR2GRAY)
H_T_M = np.zeros(9).reshape(3,3)
H_T_M[0][0] = scale
H_T_M[1][1] = scale
H_T_M[0][2] = point[0]
H_T_M[1][2] = point[1]
H_T_M[2][2] = 1
H_M_G = np.linalg.inv(H_G_M)
H_T_G = np.dot(H_M_G, H_T_M)
fn = "GroundFloorData/sunclipds.avi"
cap = cv2.VideoCapture(fn)
#load Tracking data
running, frame = cap.read()
while running:
running, frame = cap.read()
h,w,d = frame.shape
warped_texture = cv2.warpPerspective(texture, H_T_G,(w, h))
result = cv2.addWeighted(frame, .6, warped_texture, .4, 50)
cv2.imshow("Result", result)
cv2.waitKey(0)
def camactivate ():
with picamera.PiCamera() as camera:
camera.resolution = (512,512)
time.sleep(2)
camera.capture('im1.jpg')
time.sleep(2)
camera.capture('im2.jpg')
time.sleep(2)
camera.capture('im3.jpg')
time.sleep(2)
camera.capture('im4.jpg')
im1=cv2.imread('im1.jpg',1)
im2=cv2.imread('im2.jpg',1)
im3=cv2.imread('im3.jpg',1)
im4=cv2.imread('im4.jpg',1)
cv2.putText(im1,'Cam1',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im2,'Cam2',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im3,'Cam3',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im4,'Cam4',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.namedWindow('Catenated Images',cv2.WINDOW_NORMAL)
concat=np.zeros((1024,1024,3),np.uint8)
concat[0:512,0:512,:]=im1
concat[0:512,512:1024,:]=im2
concat[512:1024,0:512,:]=im3
concat[512:1024,512:1024,:]=im4
cv2.imshow('Catenated Images',concat)
cv2.imwrite('concat.jpg',concat)
cv2.waitKey(0)
def __init__(self, load_models=True):
self.api_key = self.__read_api_key()
self.__load_models = load_models
if not load_models:
return
print("... start building the models")
t1 = time.clock()
self.__batch_size = 50
self.__img_dim_80 = 80
self.__img_dim_28 = 28
# some images needed for visualization
img_sc_prohib = cv2.imread("D:\\_Dataset\\UK\\sc_prohibitory.png", cv2.IMREAD_UNCHANGED)
img_sc_mandat = cv2.imread("D:\\_Dataset\\UK\\sc_mandatory.png", cv2.IMREAD_UNCHANGED)
img_sc_warning = cv2.imread("D:\\_Dataset\\UK\\sc_warning.png", cv2.IMREAD_UNCHANGED)
self.__sc_imgs = (img_sc_warning, img_sc_prohib, img_sc_mandat)
# load the models once and for all
prohib_recog_model_path = "D:\\_Dataset\\GTSRB\\cnn_model_p_80.pkl"
mandat_recog_model_path = "D:\\_Dataset\\GTSRB\\cnn_model_m_80.pkl"
prohib_detec_model_path = "D:\\_Dataset\\GTSDB\\las_model_p_80_binary.pkl"
mandat_detec_model_path = "D:\\_Dataset\\GTSDB\\las_model_m_80_binary.pkl"
superclass_recognition_model_path = "D:\\_Dataset\\SuperClass\\cnn_model_28_lasagne.pkl"
# build the model once and for all
self.__detect_net_p = self.__build_detector(prohib_recog_model_path, prohib_detec_model_path, self.__batch_size)
self.__detect_net_m = self.__build_detector(mandat_recog_model_path, mandat_detec_model_path, self.__batch_size)
self.__recog_superclass_cnn = self.__build_classifier(superclass_recognition_model_path)
t2 = time.clock()
duration = t2 - t1
print("... finish building the models, time(sec.): %f" % (duration))
def listener():
global fnobj
rospy.init_node('reconocimiento', anonymous=True)
rospy.Subscriber("chatter", coordenadas, callback)
rate = rospy.Rate(50) #hz
cap = cv2.VideoCapture(0)
fnobj = 'logo.png'
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift')
while not rospy.is_shutdown():
ret, frame = cap.read()
crop_img = frame[y:h,x:w]
cv2.imwrite("full.png",crop_img)
img1 = cv2.imread('full.png',0)
img2 = cv2.imread(fnobj, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'usando', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
match_and_draw('analisis', matcher,desc1,desc2,kp1,kp2,img1,img2)
cv2.waitKey(1)
def test():
# 3 cards on flat table
cards_3 = cv2.imread('images/set-3-texture.jpg')
# 5 cards at an angle
cards_5 = cv2.imread('images/set-5-random.jpg')
thresh_3 = s.get_binary(cards_3)
contours = s.find_contours(thresh_3, 3)
assert len(s.transform_cards(cards_3, contours, 3)) == 3
res5 = s.detect_cards(cards_5)
assert res5 is not None and len(res5) == 5
res3 = s.detect_cards(cards_3)
assert res3 is not None and len(res3) == 3
for i in range(len(res5)):
c = res5[i]
# util.show(c, 'card')
cv2.imwrite('images/cards/card-5-%d.jpg' % i, c)
for i in range(len(res3)):
c = res3[i]
# util.show(c, 'card')
cv2.imwrite('images/cards/card-3-%d.jpg' % i, c)
# for cards detected, get properties
for link in os.listdir('images/cards'):
img = cv2.imread('images/cards/%s' % link)
test_props(img)
print 'tests pass'
def get_diffs( file_img1, file_img2, min_pts ):
# CHECK: file existence
if not (os.path.isfile(file_img1) and os.path.isfile(file_img2)):
return -1
# read in images
img1 = cv2.imread( file_img1 )
img2 = cv2.imread( file_img2 )
# CHECK: img size equality
if img1.shape != img2.shape:
return -2
# get threshold of the difference between the images
ret, im_diff = cv2.threshold( (img1 - img2), 0,255, cv2.THRESH_BINARY)
# collapse image into single channel
im_diff = cv2.add(cv2.split(im_diff)[0],cv2.split(im_diff)[1],cv2.split(im_diff)[2])
# find groups of contiguous points where images are different
cont, _ = cv2.findContours( im_diff, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
# put all contours with more than min_pts points into JSON
d = {'diff':[]}
for c in cont:
if len(c) > min_pts:
momnt = cv2.moments(c)
d['diff'].append([momnt['m10']/momnt['m00'],momnt['m01']/momnt['m00']])
# dump JSON to stdout
json.dump(d,sys.stdout)
def normalize_cv(image1,image2,compare_dir):
#img = Image.open(image1)
#height < width
width_heights = []
width_heights.append(["long_dir/",(100,150)])
#width < height
width_heights.append(["wide_dir/",(150,100)])
#equal
width_heights.append(["equal_dir/",(150,150)])
#= img.size
#resize comparison image
for width_height in width_heights:
if not os.path.exists(width_height[0]):
os.mkdir(width_height[0])
os.chdir(width_height[0])
img = cv2.imread(image1)
resized_image1 = cv2.resize(img, width_height[1])
new_path1 = path_append(image1,width_height[0])
cv2.imwrite(new_path1,resized_image1)
img2 = cv2.imread(image2)
resized_image2 = cv2.resize(img2,width_height[1])
new_path2 = path_append(image2,width_height[0])
cv2.imwrite(new_path2,resized_image2)
os.chdir("../")
#resize test directory
for pic in glob(compare_dir+"*"):
if os.path.isfile(pic):
full_pic = os.path.abspath(pic)
im = cv2.imread(full_pic)
resized_image = cv2.resize(im, width_height[1])
new_path = path_append(pic,width_height[0])
cv2.imwrite(new_path,resized_image)
def getMultiTemplePos(self,srcPicPath,templePicPath):
print("srcpath",srcPicPath,"temppath",templePicPath)
img_src=cv2.imread(srcPicPath)
img_src_gray=cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)
srcw,srch=img_src_gray.shape[::-1]
print("get pic:",srcw,srch)
img_temple=cv2.imread(templePicPath)
img_temple_gray=cv2.cvtColor(img_temple, cv2.COLOR_BGR2GRAY)
templew,templeh=img_temple_gray.shape[::-1]
res = cv2.matchTemplate(img_src_gray,img_temple_gray,cv2.TM_CCOEFF_NORMED)
# print("get temple",res)
# cv2.imshow('src',img_src_gray)
# cv2.imshow('temple',img_temple_gray)
# cv2.waitKey(0)
threshold = 0.7
loc = np.where( res >= threshold)
print(loc)
# zipres=zip(*loc[::-1])
# print("zipres",zipres)
# if len(zipres)==0:
# return False,None,None,None
# else:
# return True,zipres[0],templew,templeh
for pt in zip(*loc[::-1]):
cv2.rectangle(img_src, pt, (pt[0] + templew, pt[1] + templeh),(7,249,151), 2)
cv2.imshow('Detected',img_src)
cv2.waitKey(0)
cv2.destroyAllWindows()
def pro_progess(filepath="../data"):
height = 299
train_files = os.listdir(filepath + '/train')
train = np.zeros((len(train_files), height, height, 3), dtype=np.uint8)
labels = list(filter(lambda x: x[:3] == 'dog', train_files))
test_files = os.listdir(filepath + '/test')
test = np.zeros((len(test_files), height, height, 3), dtype=np.uint8)
for i in tqdm(range(len(train_files))):
filename = filepath + train_files[i]
img = cv2.imread(filename)
img = cv2.resize(img, (height, height))
train[i] = img[:, :, ::-1]
for i in tqdm(range(len(test_files))):
filename = filepath + test_files[i]
img = cv2.imread(filename)
img = cv2.resize(img, (height, height))
test[i] = img[:, :, ::-1]
print ('Training Data Size = %.2 GB' % (sys.getsizeof(train)/1024**3))
print ('Testing Data Size = %.2 GB' % (sys.getsizeof(test)/1024**3))
X_train, X_val, y_train, y_val = train_test_split(
train, labels, shuffle=True, test_size=0.2, random_state=42)
return X_train, X_val, y_train, y_val
def register_image(file_path, reference_form_path, output_path, result_writer, config_file):
reference = cv2.imread(reference_form_path, 0)
logging.info("read reference %s", reference_form_path)
orb = cv2.SIFT()
kp2, des2 = orb.detectAndCompute(reference, None)
image = cv2.imread(file_path, 0)
logging.info("read uploaded image %s", file_path)
kp1, des1 = orb.detectAndCompute(image, None)
logging.info("detected orb")
bf = cv2.BFMatcher(cv2.NORM_L2)
raw_matches = bf.knnMatch(des1, trainDescriptors=des2, k=2)
logging.info("knn matched")
matches = filter_matches(kp1, kp2, raw_matches)
mkp1, mkp2 = zip(*matches)
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
logging.info("starting RANSAC")
homography_transform, mask = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
logging.info("RANSAC finished")
homography, transform = check_homography(homography_transform)
good_enough_match = check_match(homography, transform)
h, w = reference.shape
image_transformed = cv2.warpPerspective(image, homography_transform, (w, h))
logging.info("transformed image")
head, file_name = os.path.split(file_path)
transformed_image = write_transformed_image(image_transformed, homography, transform, good_enough_match, file_name,
output_path, result_writer)
logging.info("transformed %s", transformed_image)
return create_response(transformed_image, good_enough_match, config_file)
def getOneTemplePos(self,srcPicPath,templePicPath):
# print(srcPicPath,templePicPath)
img_src=cv2.imread(srcPicPath)
img_src_gray=cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)
srcw,srch=img_src_gray.shape[::-1]
print("img_src gray",srcw,srch)
img_temple=cv2.imread(templePicPath)
img_temple_gray=cv2.cvtColor(img_temple, cv2.COLOR_BGR2GRAY)
templew,templeh=img_temple_gray.shape[::-1]
print("temple gray",templew,templeh)
# cv2.imshow('rgb',img_src)
# cv2.imshow('gray',img_src_gray)
# cv2.imshow('template',img_temple_gray)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
res = cv2.matchTemplate(img_src_gray,img_temple_gray,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print(min_val, max_val, min_loc, max_loc)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
bottom_right = (top_left[0] + templew, top_left[1] + templeh)
cv2.rectangle(img_src,top_left, bottom_right, 255, 2)
print(top_left, bottom_right)
def find_hostiles(screen):
img_rgb = cv2.imread(screen)
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
neut_img = cv2.imread('neut.png', 0)
red_img = cv2.imread('red.png', 0)
neut2_img = cv2.imread('neut2.png', 0)
w, h = red_img.shape[::-1]
neut_match = cv2.matchTemplate(img_gray, neut_img, cv2.TM_CCOEFF_NORMED)
neut2_match = cv2.matchTemplate(img_gray, neut2_img, cv2.TM_CCOEFF_NORMED)
neut2_match = cv2.matchTemplate(img_gray, neut2_img, cv2.TM_CCOEFF_NORMED)
red_match = cv2.matchTemplate(img_gray, red_img, cv2.TM_CCOEFF_NORMED)
threshold = 0.99
loc_neut = numpy.where(neut_match >= threshold)
loc_neut2 = numpy.where(red_match >= threshold)
loc_red = numpy.where(red_match >= threshold)
total_match = len(loc_neut[0]) + len(loc_red[0]) + len(loc_neut2[0])
if total_match > 0:
for pt in zip(*loc_red[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
for pt in zip(*loc_neut[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
for pt in zip(*loc_neut2[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
cv2.imwrite('res.png', img_rgb)
return total_match
def read_image(img_path, image_dims=None, mean=None):
"""
Reads an image from file path or URL, optionally resizing to given image dimensions and
subtracting mean.
:param img_path: path to file, or url to download
:param image_dims: image dimensions to resize to, or None
:param mean: mean file to subtract, or None
:return: loaded image, in RGB format
"""
import urllib
filename = img_path.split("/")[-1]
if img_path.startswith('http'):
urllib.urlretrieve(img_path, filename)
img = cv2.imread(filename)
else:
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if image_dims is not None:
img = cv2.resize(img, image_dims) # resize to image_dims to fit model
img = np.rollaxis(img, 2) # change to (c, h, w) order
img = img[np.newaxis, :] # extend to (n, c, h, w)
if mean is not None:
mean = np.array(mean)
if mean.shape == (3,):
mean = mean[np.newaxis, :, np.newaxis, np.newaxis] # extend to (n, c, 1, 1)
img = img.astype(np.float32) - mean # subtract mean
return img
def procesamiento_imagen():
## Convertir a grayscale
img = Image.open(rostro).convert('LA')
img.save('greyscale.png')
## Resize
foo = Image.open("greyscale.png")
foo = foo.resize((256,256),Image.ANTIALIAS)
foo.save("greyscale.png",optimize=True,quality=95)
## Eliminar ruido
img = cv2.imread('greyscale.png')
dst = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)
## Canny detector
img = cv2.imread('greyscale.png',0)
edges = cv2.Canny(img,256,256)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()
def tratamento_img(img):
kernel = np.ones((7,4),np.uint8)
img = cv2.imread(img,0)
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
titles = ['BINARY']
images = [thresh1]
img = plt.imshow(images[0],'gray')
plt.xticks([]),plt.yticks([])
plt.savefig("pretobranco", dpi=100)
#plt.show()
img = cv2.imread("binaria.png",0)
dilate = cv2.dilate(img,kernel,iterations = 1)
cv2.imwrite('dilate.png',dilate)
img = cv2.imread("dilate.png",0)
erosion = cv2.erode(img,kernel,iterations = 1)
cv2.imwrite('erosion.png',erosion)
img = "dilate.png"
os.system("convert erosion.png erosion.pgm")
os.system("convert dilate.png dilate.pgm")
k = cv2.waitKey(0)
return True
def baxter_sleep():
"""
Send the image located at the specified path to the head
display on Baxter.
@param path: path to the image file to load and send
"""
img = cv2.imread("$YOUT_BAXTER_WORKSPACE/src/my_baxter/BaxterFace/sleep1.png")
msg = cv_bridge.CvBridge().cv2_to_imgmsg(img, encoding="bgr8")
pub = rospy.Publisher('/robot/xdisplay', Image, latch=True, queue_size=1)
pub.publish(msg)
# Sleep to allow for image to be published.
rospy.sleep(0.5)
img = cv2.imread("$YOUT_BAXTER_WORKSPACE/src/my_baxter/BaxterFace/sleep2.png")
msg = cv_bridge.CvBridge().cv2_to_imgmsg(img, encoding="bgr8")
pub.publish(msg)
rospy.sleep(0.17)
img = cv2.imread("$YOUT_BAXTER_WORKSPACE/src/my_baxter/BaxterFace/sleep3.png")
msg = cv_bridge.CvBridge().cv2_to_imgmsg(img, encoding="bgr8")
pub.publish(msg)
rospy.sleep(0.17)
img = cv2.imread("$YOUT_BAXTER_WORKSPACE/src/my_baxter/BaxterFace/sleep4.png")
msg = cv_bridge.CvBridge().cv2_to_imgmsg(img, encoding="bgr8")
pub.publish(msg)
rospy.sleep(0.17)
def preprocess_training(image_dict, size=(40,100), norm_width=40):
processed = defaultdict(list)
widths = defaultdict(list)
avg_widths = defaultdict(list)
avg_width_list = []
for bond_type in image_dict.keys():
imgs = image_dict[bond_type]
for img in imgs:
im = cv2.imread(img,0)
widths[bond_type].append(im.shape[1])
for key in widths:
avg_width_list.append(np.mean(widths[key]))
avg_widths[key] = np.mean(widths[key])
max_width = max(avg_width_list)
for bond_type in image_dict.keys():
imgs = image_dict[bond_type]
for img in imgs:
im = cv2.imread(img,0)
ret, im = cv2.threshold(im, THRESH_VAL, 255, cv2.THRESH_BINARY_INV)
border = max(int((max_width-im.shape[1])/2),0)
im = cv2.copyMakeBorder(im,0,0,border,border,cv2.BORDER_CONSTANT,0)
im = cv2.resize(im,size)
im = cv2.GaussianBlur(im,(5,5),5)
#plt.imshow(im, cmap="Greys_r")
#plt.show()
center = im[20:80,:]
processed[bond_type].append(center)
return processed
def __init__(self,filename,imageL='tmp/inputL.jpg',imageR='tmp/inputR.jpg'):
print 'model created'
self.filename = filename
try: #make sure OpenCV can actually process the images
self.testL = cv2.pyrDown(cv2.imread(imageL))
self.testR = cv2.pyrDown(cv2.imread(imageR))
assert (self.testR.size == self.testL.size)
self.width,self.height = self.testL.shape[:2] #scales down
#convert image to smaller size
loadedImageL = cv.LoadImage(imageL,cv.CV_LOAD_IMAGE_COLOR)
destL = cv.CreateImage((self.height,self.width), 8, 3)
# 8 = bits, 3 = color
cv.Resize(loadedImageL,destL,interpolation=cv.CV_INTER_AREA)
cv.SaveImage(imageL, destL)
#convert image to smaller size
loadedImageR = cv.LoadImage(imageR,cv.CV_LOAD_IMAGE_COLOR)
destR = cv.CreateImage((self.height,self.width), 8, 3)
cv.Resize(loadedImageR,destR,interpolation=cv.CV_INTER_AREA)
cv.SaveImage(imageR, destR)
self.imageL = cv2.pyrDown(cv2.imread(imageL))
self.imageR = cv2.pyrDown(cv2.imread(imageR))
except Exception as e:
print e
quit()
def run(self, args):
if not args:
print 'Invalid option.'
elif args[0] in ['attack', 'spell', 'tokugi']:
# 「こうげき」「じゅもん」「とくぎ」でフィールドモード
self.field_mode(args[0])
elif args[0] == 'slot':
# スロットゲームモード
self.slot_mode()
elif args[0] == 'debug':
# debugサブディレクトリ以下すべて読み込み
print 'DEBUG: Reading all files'
for filename in [f for f in os.listdir('debug') if f.endswith('.png')]:
print 'DEBUG:', filename,
img_orig = cv2.imread(os.path.join('debug', filename))
img_proc = self.transform(img_orig)
self.debug_mode(img_proc)
elif args[0].endswith('.png'):
# 指定ファイル読み込み（＋デバッグファイル出力）
self.debug = True
print 'DEBUG:', sys.argv[1]
img_orig = cv2.imread(sys.argv[1])
img_proc = self.transform(img_orig)
print ''
self.debug_mode(img_proc)
def process(self, batch=16, objectName='unknow',times=4):
gnum=0
lnum=0
maxnum=len(self.imagelist)*times
while gnum < maxnum :
images=[]
for lnum in range(0, batch):
if gnum + lnum < maxnum:
images.append(cv2.imread(os.path.join(self.imageFolder, self.imagelist[gnum+lnum])))
else:
break
images_aug=[]
for i in range(0,times):
images_aug_i = self.seq.augment_images(images)
images_aug.extend(images_aug_i)
for i,image in enumerate(images_aug):
bgnum=random.randint(0,len(self.backgroundlist)-1)
bgfile=os.path.join(self.backgroundFolder,self.backgroundlist[bgnum])
bgimg=cv2.imread(bgfile)
xml='%d.xml'%(gnum+i+1)
jpeg='%d.jpg'%(gnum+i+1)
print('%d/%d: save image to %s, save xml to %s'%(gnum+i+1,maxnum,jpeg,xml))
classify2detect.process(image=image,
background=bgimg,
objectName=objectName,
xml=xml,
jpeg=jpeg)
gnum = gnum + len(images_aug)
def makeMorph(p1_filename, p2_filename, output_filename, points1, points2, tri, alpha):
base_points = [[0, 0], [0, 400], [0, 799], [300, 799], [599, 799], [599, 400], [599, 0], [300, 0]]
# points1 += base_points
# points2 += base_points
img1 = cv2.imread(p1_filename)
img2 = cv2.imread(p2_filename)
# alpha = 0.5
img1 = np.float32(img1)
img2 = np.float32(img2)
points = []
for i in range(0, len(points1)):
x = (1 - alpha) * points1[i][0] + alpha * points2[i][0]
y = (1 - alpha) * points1[i][1] + alpha * points2[i][1]
points.append((x, y))
imgMorph = np.zeros(img1.shape, dtype=img1.dtype)
for t_tri in tri:
x, y, z = t_tri
x = int(x)
y = int(y)
z = int(z)
t1 = [points1[x], points1[y], points1[z]]
t2 = [points2[x], points2[y], points2[z]]
t = [points[x], points[y], points[z]]
# Morph one triangle at a time.
morphTriangle(img1, img2, imgMorph, t1, t2, t, alpha)
cv2.imwrite(output_filename, np.uint8(imgMorph))
def mean_squared_error(image1, image2):
list_of_files = glob.glob('/home/pi/Desktop/camera/*.jpg')
latest_file = max(list_of_files, key=os.path.getctime) # get latest file in our directory
text = "Possible motion detected"
subject = "Pi security camera alert"
send_to = "test"
send_from = "test"
#Load images
original = cv2.imread("control.jpg", 0) # control file of what the house looks like
questionable = cv2.imread(latest_file, 0)
image1 = image1.astype(float)
image2 = image2.astype(float)
error_rank = np.sum((image1 - image2) ** 2)
error_rank /= float(image1.shape[0] * image1.shape[1])
test = calculated(error_ranking)
if test == True:
send_mail(send_from, send_to, subject, text, latest_file)
else:
time1 = time.strftime("%d/%m/%Y %H:%M:%S")
re.sub('[^A-Za-z0-9]+', '', time1)
f = open(error_rank+"-"time1+".txt", 'w+')
f.close
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("original", image)
flipped = cv2.flip(image, 1)
cv2.imshow("flipped horizontally", flipped)
flipped = cv2.flip(image, 0)
cv2.imshow("flipped vertically", flipped)
flipped = cv2.flip(image, -1)
cv2.imshow("flipped horizontally and vertically", flipped)
cv2.waitKey(0)
import os
import cv2
import shutil
def mkdir(path):
folder = os.path.exists(path)
if not folder:
os.makedirs(path)
num=0
right=0
img_width = 512
img_height = 128
img_channel= 3
jvzhen = img_width * img_height * img_channel
for line in open("./1.txt"):
img_pa = line.strip('\n')
if ".png" in img_pa:
num=num+1
print "all_img: "+str(num)
img = cv2.imread(img_pa)
if img.size != jvzhen:
with open('./error.txt', 'a') as f:
f.writelines(line)
else:
right=right+1
print "right : "+str(right)
print "-----------------------------"
# grab the list of images that we'll be describing
print("[INFO] handling images...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the raw pixel intensities matrix, the features matrix,
# and labels list
rawImages = []
features = []
labels = []
# loop over the input images
for (i, imagePath) in enumerate(imagePaths):
# load the image and extract the class label
# our images were named as labels.image_number.format
image = cv2.imread(imagePath)
# get the labels from the name of the images by extract the string before "."
label = imagePath.split(os.path.sep)[-1].split(".")[0]
# extract raw pixel intensity "features"
#followed by a color histogram to characterize the color distribution of the pixels
# in the image
pixels = image_to_feature_vector(image)
hist = extract_color_histogram(image)
# add the messages we got to the raw images, features, and labels matricies
rawImages.append(pixels)
features.append(hist)
labels.append(label)
# show an update every 200 images until the last image
cv2.setMouseCallback('choose corresponding points',draw_circle)
while count < 8: #we only select 9 points, it should be more accurate if we choose more points
if isleft == 1:
cv2.imshow('choose corresponding points', image_l)
elif isleft == 0:
cv2.imshow('choose corresponding points', image_r)
if cv2.waitKey(20) & 0xFF == 27:
break
if len(sys.argv) < 2:
print '''
Instruction: Run this program with arguments: python as5.py left.jpg right.jpg
'''
else:
image_l = cv2.imread(sys.argv[1])
image_r = cv2.imread(sys.argv[2])
cv2.imshow('left image', image_l)
cv2.imshow('right image', image_r)
print 'Press any key to choose corresponding points'
key = cv2.waitKey(0)
if key == 27:
cv2.destroyAllWindows()
print 'Please choose the 8 pairs of corresponding points on the images\n'
image_l = cv2.imread(sys.argv[1])
image_r = cv2.imread(sys.argv[2])
leftpoints = []
import cv2
import numpy as np
from pyzbar.pyzbar import decode
img = cv2.imread('')
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
with open('datas.txt') as f:
datas_list = f.read().splitlines()
print(datas_list)
while True:
success, img = cap.read()
for barcode in decode(img):
mydata = barcode.data.decode('utf-8')
print(mydata)
if mydata in datas_list:
# print("Successful Authenticated")
detect_output = 'Authorized'
color = (0, 255, 0)
else:
# print("Failed to authenticate!!!")
detect_output = 'Un-authorized'
color = (0, 0, 255,)
# build a shape around the detected barcode.
pts = np.array([barcode.polygon], np.int32)
# https://segmentfault.com/a/1190000015663722
import cv2
import numpy as np
img = cv2.imread('D:\code\opencv_learning\opencv_learning\last\DATA1.png')
cv2.imshow('src', img)
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[1]
epsilon = 0.1 * cv2.arcLength(cnt, True)
approx = cv2.approxpolyDP(cnt, epsilon, True)
cv2.polylines(img, [approx], True, (0, 0, 255), 2)
cv2.imshow('show', img)
cv2.waitkey()
import cv2
import numpy as np
import math
import random
import matplotlib.pyplot as plt
if __name__ == "__main__":
img = cv2.imread("C:\\Users\\ani49\\OneDrive\\Documents\\GitHub\\homework-3-ani4991\\Lenna.png",0)
#mean = 4
#variance = 10
prob_noise = 0.01
thres = 1 - prob_noise
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
rdn = random.random()
if rdn < prob_noise:
img[i][j] = 0
elif rdn > thres:
img[i][j] = 255
else:
img[i][j] = img[i][j]
#plt.hist(img, bins='auto')
#plt.show()
#print("got noise matrix and waiting for hist")
cv2.imwrite("img_with_salt&pepper_noise.png",np.uint8(img))
cv2.waitKey(0)
cv2.destroyAllWindows()
import cv2
import pandas as pd
from tqdm import tqdm
train = pd.read_csv('Christof/assets/train.csv')
test = pd.read_csv('Christof/assets/sample_submission.csv')
path_to_train = 'Christof/assets/train_rgb_512/'
path_to_test = 'Christof/assets/test_rgb_512/'
fns = [path_to_test + f + '.png' for f in test['Id']]
import numpy as np
channel_avg = np.zeros(3)
channel_std = np.zeros(3)
#images = np.zeros((len(fns),512,512,3))
for i, fn in tqdm(enumerate(fns)):
image = cv2.imread(fn, cv2.IMREAD_UNCHANGED)
channel_avg += np.mean(np.reshape(image, (-1, 3)), axis=0)
channel_std += np.std(np.reshape(image, (-1, 3)), axis=0)
channel_avg /= len(fns)
channel_std /= len(fns)
print(channel_avg / 255)
print(channel_std / 255)
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Read image
img = cv2.imread("imori.jpg").astype(np.float32)
H, W, C = img.shape
# Otsu binary
## Grayscale
out = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
out = out.astype(np.uint8)
## Determine threshold of Otsu's binarization
max_sigma = 0
max_t = 0
for _t in range(1, 255):
v0 = out[np.where(out < _t)]
m0 = np.mean(v0) if len(v0) > 0 else 0.
w0 = len(v0) / (H * W)
v1 = out[np.where(out >= _t)]
m1 = np.mean(v1) if len(v1) > 0 else 0.
w1 = len(v1) / (H * W)
sigma = w0 * w1 * ((m0 - m1) ** 2)
if sigma > max_sigma:
max_sigma = sigma
max_t = _t
## Binarization
#print("threshold >>", max_t)
def read():
"""
Demos the largest_rotated_rect function
"""
for i in range(1, 1001):
img_name = 'img_' + str(i) + '.jpg'
gt_name = 'gt_img_' + str(i) + '.txt'
print img_name
img_path = os.path.join(file_path, img_name)
with open(os.path.join(gt_path, gt_name), 'r') as f:
gt_lines = [
o.decode('utf-8-sig').encode('utf-8') for o in f.readlines()
]
gt_strs = [g.strip().split(',')[-1] for g in gt_lines]
gt_coors = [g.strip().split(',')[0:8] for g in gt_lines]
#gt_coors = [int(g) for g in gt_coors]
for ii, g in enumerate(gt_coors):
gt_coors[ii] = [int(a) for a in g]
#gt_coors = np.array(gt_coor,dtype=np.int32)
#print 'gt_coors',gt_coors
image = cv2.imread(img_path)
image_height, image_width = image.shape[0:2]
angles = [-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90]
for j in angles:
print 'angle', j
image_orig = np.copy(image)
[image_rotated, boxes_rotated] = rotate_image(image, gt_coors, j)
image_rotated_cropped, boxes_rotated_cropped = crop_around_center(
image_rotated, boxes_rotated,
*rotatedRectWithMaxArea(image_width, image_height,
math.radians(j)))
new_img_name = 'img_' + str(i) + '_' + str(j) + '.jpg'
plt.clf()
fig, ax = plt.subplots(3, 5, figsize=(40, 30))
fig.tight_layout()
plt.subplot(1, 3, 1)
plt.imshow(image)
currentAxis = plt.gca()
for index, gt in enumerate(gt_coors):
gt = np.array(gt).reshape(4, 2)
currentAxis.add_patch(
plt.Polygon(gt, fill=None, edgecolor='r', linewidth=2))
currentAxis.add_patch(plt.Circle(gt[0], 5))
currentAxis.add_patch(plt.Circle(gt[1], 5, color='r'))
plt.subplot(1, 3, 2)
plt.imshow(image_rotated)
currentAxis = plt.gca()
for index in range(boxes_rotated.shape[0]):
#print 'plot boxes_rotated ',boxes_rotated[index]
gt_rotated = boxes_rotated[index].reshape(4, 2)
currentAxis.add_patch(
plt.Polygon(gt_rotated,
fill=None,
edgecolor='b',
linewidth=2))
currentAxis.add_patch(plt.Circle(gt_rotated[0], 5))
currentAxis.add_patch(plt.Circle(gt_rotated[1], 5, color='r'))
plt.subplot(1, 3, 3)
plt.imshow(image_rotated_cropped)
currentAxis = plt.gca()
result_lines = []
for index in range(len(boxes_rotated_cropped)):
#print 'plot boxes_rotated ',boxes_rotated[index]
gt_rotated_cropped = boxes_rotated_cropped[index]
currentAxis.add_patch(
plt.Polygon(gt_rotated_cropped,
fill=None,
edgecolor='g',
linewidth=2))
currentAxis.add_patch(plt.Circle(gt_rotated_cropped[0], 5))
currentAxis.add_patch(
plt.Circle(gt_rotated_cropped[1], 5, color='r'))
currentAxis.add_patch(
plt.Circle(gt_rotated_cropped[2], 6, color='y'))
currentAxis.add_patch(
plt.Circle(gt_rotated_cropped[3], 6, color='black'))
'''
result_line = []
for p in range(4):
for q in range(2):
result_line.append(gt_rotated_cropped[p][q])
print 'result_line',result_line
result_line = [str(a) for a in result_line]
result_lines.append(','.join(result_line))
'''
#with open(os.path.join(new_gt_path,'gt_img_'+str(j)+'.txt'),'w') as f:
#f.write('\r\n'.join(result_lines))
#cv2.imwrite(os.path.join(save_rotate_path,new_img_name),image_rotated)
#cv2.imwrite(os.path.join(save_crop_path,new_img_name), image_rotated_cropped)
plt.savefig(os.path.join(vis_save_path, new_img_name), dpi=200)
plt.close(fig)
print "Done"
def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
import cv2
import numpy as np
# NB! Image sizes should be the same
# You can change the images, yet, be sure that they have the same width and height
img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg']
image_list = [cv2.imread(k) for k in img_samples]
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
pred_height, pred_width, c = image_list[0].shape
net_width, net_height = (network_width(net), network_height(net))
img_list = []
for custom_image_bgr in image_list:
custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
custom_image = cv2.resize(
custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
custom_image = custom_image.transpose(2, 0, 1)
img_list.append(custom_image)
arr = np.concatenate(img_list, axis=0)
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(net_width, net_height, c, data)
batch_dets = network_predict_batch(net, im, batch_size, pred_width,
pred_height, thresh, hier_thresh, None, 0, 0)
batch_boxes = []
batch_scores = []
batch_classes = []
for b in range(batch_size):
num = batch_dets[b].num
dets = batch_dets[b].dets
if nms:
do_nms_obj(dets, num, meta.classes, nms)
boxes = []
scores = []
classes = []
for i in range(num):
det = dets[i]
score = -1
label = None
for c in range(det.classes):
p = det.prob[c]
if p > score:
score = p
label = c
if score > thresh:
box = det.bbox
left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2,
box.x + box.w / 2, box.y + box.h / 2))
boxes.append((top, left, bottom, right))
scores.append(score)
classes.append(label)
boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0)
cv2.rectangle(image_list[b], (left, top),
(right, bottom), boxColor, 2)
cv2.imwrite(os.path.basename(img_samples[b]),image_list[b])
batch_boxes.append(boxes)
batch_scores.append(scores)
batch_classes.append(classes)
free_batch_detections(batch_dets, batch_size)
return batch_boxes, batch_scores, batch_classes
#template matching and render it effectively useless.
# import the necessary packages
import argparse
import cv2
import numpy as np
import imutils
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-s", "--source", required=True, help="Path to the source image")
ap.add_argument("-t", "--template", required=True, help="Path to the template image")
args = vars(ap.parse_args())
# load the source and template image
source = cv2.imread(args["source"])
template = cv2.imread(args["template"])
(tempH, tempW) = template.shape[:2]
# find the template in the source image
# The cv2.matchTemplate method requires three parameters: the source image, the template , and the template matching method.
# We’ll pass in cv2.TM_CCOEFF to indicate we want to use the correlation coefficient method.
result = cv2.matchTemplate(source, template, cv2.TM_CCOEFF)
# Now that we have computed the result matrix, we can apply the cv2.minMaxLoc function to find the (x, y) coordinates of the best match.
# We pass in our result, and in return we receive a 4-tuple consisting of the minimum value in the result,
# the maximum value in result, the (x, y) coordinates of the minimum value, and the (x, y) coordinates of the maximum value, respectively
(minVal, maxVal, minLoc, (x, y)) = cv2.minMaxLoc(result)
print("minVal:{}, maxVal: {}, minLoc: {}, (x: {}, y: {})".format(minVal, maxVal, minLoc, x, y))
image, model, landmarks, bb, resized_crop)
percent_wearing_masks = num_wearing_masks / len(resized_crop) * 100
image = cv2.putText(
image, (str(percent_wearing_masks) + "% wearing masks"),
(30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1,
cv2.LINE_AA)
cv2.imshow('Input', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
print()
func = 0
elif func == 2:
image = cv2.imread(
input("Please enter the complete image file path: ").strip('"'))
image = image[:, :, ::-1]
landmarks, bb, cropped_faces, resized_crop = find_faces(image, model2)
if (type(resized_crop) == int and resized_crop == 0):
image = cv2.putText(image, "No faces detected", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1,
cv2.LINE_AA)
else:
image, num_wearing_masks = di.convert_image(
image, model, landmarks, bb, resized_crop)
percent_wearing_masks = num_wearing_masks / len(resized_crop) * 100
image = cv2.putText(
image, (str(percent_wearing_masks) + "% wearing masks"),
(30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1,
# importing cv2
import cv2
import numpy as np
# path
path = r'C:\Users\admin\Desktop\MDT425\Week5 - CV2_ImageDetection\img\sampleImage.png'
pathHSV = r'C:\Users\admin\Desktop\MDT425\Week5 - CV2_ImageDetection\img\HSV.png'
# Using cv2.imread() method
img = cv2.imread(path)
hsvChart = cv2.imread(pathHSV)
#rows cols
width, height = img.shape[0:2]
width, height = hsvChart.shape[0:2]
hsvImg = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hsvChartImg = cv2.cvtColor(hsvChart, cv2.COLOR_BGR2HSV)
lower = np.array([100, 200, 200])
upper = np.array([135, 255, 255])
marking = cv2.inRange(hsvImg, lower, upper)
markingChart = cv2.inRange(hsvChartImg, lower, upper)
# Displaying the image
cv2.imshow('Image', img)
cv2.imshow('HSV', marking)
cv2.imshow('Mark Image HSV', markingChart)
cv2.waitKey()
def _grab_board(self):
time.sleep(3)
samples = np.float32(np.loadtxt('vectors.data'))
responses = np.float32(np.loadtxt('samples.data'))
model = cv2.KNearest()
model.train(samples, responses)
window = gtk.gdk.get_default_root_window()
x, y, width, height, _ = window.get_geometry()
ss = gtk.gdk.Pixbuf.get_from_drawable(gtk.gdk.Pixbuf(gtk.gdk.COLORSPACE_RGB, True, 8, width, height),
gtk.gdk.get_default_root_window(),
gtk.gdk.colormap_get_system(),
0, 0, x, y, width, height)
ss.save(TMP_FILE, 'png')
raw = cv2.imread(TMP_FILE)
gray = cv2.cvtColor(raw, cv2.COLOR_BGR2GRAY)
threshold = cv2.adaptiveThreshold(gray, 255, 1, 1, 11, 15)
cache = threshold.copy()
contours, _ = cv2.findContours(threshold,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
squares = []
for c in contours:
c = cv2.approxPolyDP(c, 4, True)
if len(c) != 4:
continue
if not cv2.isContourConvex(c):
continue
squares.append(c)
board_size = max(cv2.contourArea(s) for s in squares)
board = [s for s in squares if cv2.contourArea(s) == board_size][0]
min_x = min(s[0][0] for s in board)
max_x = max(s[0][0] for s in board)
min_y = min(s[0][1] for s in board)
max_y = max(s[0][1] for s in board)
step_x = (max_x - min_x) / 9.0
step_y = (max_y - min_y) / 9.0
values = {}
centroids = {}
for y in range(9):
values[y] = {}
for x in range(9):
local_min_y = min_y + (y * step_y) + 5
local_max_y = min_y + ((y+1) * step_y) - 5
local_min_x = min_x + (x * step_x) + 5
local_max_x = min_x + ((x+1) * step_x) - 5
roi = cache[
local_min_y:local_max_y,
local_min_x:local_max_x]
centroids[(y, x)] = (
int((local_min_y + local_max_y) / 2.0),
int((local_min_x + local_max_x) / 2.0))
cache = cache.copy()
roi_cache = roi.copy()
contours, _ = cv2.findContours(roi,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
if not contours:
values[y][x] = (0, 0)
continue
item = max(contours, key=lambda c: cv2.contourArea(c))
_x, _y, _w, _h = cv2.boundingRect(item)
digit = roi_cache[_y:_y+_h, _x:_x+_w]
small_digit = cv2.resize(digit, (10, 10))
vector = small_digit.reshape((1, 100)).astype(np.float32)
_, results, _, err = model.find_nearest(vector, k=1)
value = int(results.ravel()[0])
values[y][x] = (value, err[0][0])
errs = [values[y][x][1] for y in range(9) for x in range(9)]
err_threshold = np.percentile(errs, 90)
# "TODO": relearn 1 :(
for y in range(9):
for x in range(9):
val, err = values[y][x]
if err > err_threshold and val == 7:
values[y][x] = 1
else:
values[y][x] = val
return values, centroids
def XShadow(path):
img = cv2.imread(path)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
height,width = img.shape[:2]
# print(height,width)
#blur = cv2.GaussianBlur(gray,(5,5),0)
blur = cv2.blur(gray,(8,8))
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
if(width > 500):
kernel = np.ones((4, 4),np.uint8) #卷积核
else:
kernel = np.ones((2, 2),np.uint8) #卷积核
dilation = cv2.dilate(thresh,kernel,iterations = 1) #膨胀操作使得单个文字图像被黑像素填充
'''
cv2.imshow('image',thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
perPixelValue = 1 #每个像素的值
projectValArry = np.zeros(width, np.int8) #创建一个用于储存每列黑色像素个数的数组
for i in range(0,width):
for j in range(0,height):
perPixelValue = dilation[j,i]
if (perPixelValue == 255): #如果是黑字
projectValArry[i] += 1
# print(projectValArry[i])
canvas = np.zeros((height,width), dtype="uint8")
for i in range(0,width):
for j in range(0,height):
perPixelValue = 255 #白色背景
canvas[j, i] = perPixelValue
for i in range(0,width):
for j in range(0,projectValArry[i]):
perPixelValue = 0 #黑色直方图投影
canvas[height-j-1, i] = perPixelValue
'''
cv2.imshow('canvas',canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
list = []
startIndex = 0 #记录进入字符区的索引
endIndex = 0 #记录进入空白区域的索引
inBlock = 0 #是否遍历到了字符区内
for i in range(width):
if (inBlock == 0 and projectValArry[i] != 0):
inBlock = 1
startIndex = i
elif (inBlock == 1 and projectValArry[i] == 0):
endIndex = i
inBlock = 0
#subImg = gray[0:height, startIndex:endIndex+1] #endIndex+1
#print(startIndex,endIndex+1)
list.append([startIndex, 0, endIndex-startIndex-1, height])
#print(len(list))
return list
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('noisy_leaf.jpg',0)
#ret1, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, imgf = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#blur = cv2.GaussianBlur(img, (5,5), 0)
#ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
plt.subplot(3,1,1), plt.imshow(img,cmap = 'gray')
plt.title('Original Noisy Image'), plt.xticks([]), plt.yticks([])
plt.subplot(3,1,2), plt.hist(img.ravel(), 256)
plt.axvline(x=ret, color='r', linestyle='dashed', linewidth=2)
plt.title('Histogram'), plt.xticks([]), plt.yticks([])
plt.subplot(3,1,3), plt.imshow(imgf,cmap = 'gray')
plt.title('Otsu thresholding'), plt.xticks([]), plt.yticks([])
plt.show()
def removeBackground(image_file, out_folder):
img = cv2.imread(image_file)
# Crop out white borders from image so the edge detection algorithm doesnt detect the border as an edge
im = Image.fromarray(img)
bg = Image.new(im.mode, im.size, (255, 255, 255))
diff = ImageChops.difference(im, bg)
diff = ImageChops.add(diff, diff, 2.0, -100)
bbox = diff.getbbox()
if bbox:
im = im.crop(bbox)
img = np.array(im)
height, width, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
# Calculate otsu threshold for edge detection
ret, threshed_img = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
high = ret
low = high * 0.5
# Edge detection
edges = cv2.Canny(gray, low, high)
edges = cv2.dilate(edges, None)
edges = cv2.erode(edges, None)
# Find all contours and get the one with the biggest area
_, contours, _ = cv2.findContours(edges, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
max_contour = sorted(contours,
key=lambda c: cv2.contourArea(c),
reverse=True)[0]
x, y, w, h = cv2.boundingRect(max_contour)
# Add a 5px buffer to bounding box
x1 = x if x - 0 > 5 else 5
x2 = x + w if abs(width - (x + w)) > 5 else (x + w) - 5
y1 = y if y - 0 > 5 else 5
y2 = y + h if abs(height - (y + h)) > 5 else (y + h) - 5
# Create bounding box based on the largest contour of the image
rect = (x1, y1, x2, y2)
mask = np.zeros(img.shape[:2], dtype=np.uint8)
# Dummy placeholders
bgdmodel = np.zeros((1, 65), np.float64)
fgdmodel = np.zeros((1, 65), np.float64)
# Iteratively extract foreground object from background
cv2.grabCut(img, mask, rect, bgdmodel, fgdmodel, 10, cv2.GC_INIT_WITH_RECT)
cv2.grabCut(img, mask, rect, bgdmodel, fgdmodel, 10, cv2.GC_INIT_WITH_MASK)
# Remove background from image
mask2 = np.where((mask == 1) + (mask == 3), 255, 0).astype('uint8')
output = cv2.bitwise_and(img, img, mask=mask2)
# Convert black background to white
output[np.where((output == [0, 0, 0]).all(axis=2))] = [255, 255, 255]
cv2.imwrite(os.path.join(out_folder, os.path.basename(image_file)), output)
import cv2
face_cascade=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")#building a casscade classifier
img=cv2.imread("venv/penguin.jpg")
gray_img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #reading the image as grayscale
faces=face_cascade.detectMultiScale(gray_img,scaleFactor=1.05,minNeighbors=5)#finding faces
print(type(faces))
print(faces)
for x,y,w,h in faces:
img=cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)
resized_img=cv2.resize(img,(int(img.shape[1]/2),int(img.shape[0]/2)))
cv2.imshow("Gray",resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return vis
if __name__ == '__main__':
print __doc__
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift')
try: fn1, fn2 = args
except:
fn1 = '../c/box.png'
fn2 = '../c/box_in_scene.png'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'using', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
def match_and_draw(win):
print 'matching...'
def binarize(path):
char_img = cv2.imread(path, 2)
resized = cv2.resize(char_img, (250, 250))
ret, thr_image = cv2.threshold(resized.copy(), 0, 255, cv2.THRESH_BINARY)
# We don't use retVal for the moment, just take the thresholded image, so we ignore
return thr_image
def func(x):
pass
a = 0
a = int(input('Enter 1 for VideoCam else 0 '))
if a == 1:
cap = cv2.VideoCapture(0)
if cap.isOpened():
ret, img = cap.read()
else:
ret = False
else:
img = cv2.imread('a.jpg')
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.createTrackbar('nfeatures', 'image', 500, 10000, func)
cv2.createTrackbar('table_number', 'image', 6, 100, func)
cv2.createTrackbar('key size', 'image', 12, 31, func)
cv2.createTrackbar('multi', 'image', 1, 100, func)
cv2.createTrackbar('flag', 'image', 0, 2, func)
cv2.createTrackbar('check', 'image', 100, 500, func)
cv2.createTrackbar('algo', 'image', 1, 1, func)
cv2.createTrackbar('min_match', 'image', 10, 1000, func)
cv2.createTrackbar('aise', 'image', 1, 10, func)
while True:
if a == 1:
ret, img = cap.read()
img = cv2.flip(img, 1)
else:
br_list = []
object_list = []
def toggle_selector(event):
toggle_selector.RS.set_active(True)
if __name__ == '__main__':
for n, image_file in enumerate(os.scandir(image_folder)):
img = image_file
fig, ax = plt.subplots(1, figsize=(10.5, 8))
mngr = plt.get_current_fig_manager()
mngr.window.setGeometry(250, 40, 800, 600)
image = cv2.imread(image_file.path)
print(image_file.path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ax.imshow(image)
toggle_selector.RS = RectangleSelector(
ax, line_select_callback,
drawtype='box', useblit=True,
button=[1], minspanx=5, minspany=5,
spancoords='pixels', interactive=True,
)
bbox = plt.connect('key_press_event', toggle_selector)
key = plt.connect('key_press_event', onkeypress)
plt.tight_layout()
plt.show()
plt.close(fig)
import cv2
image = cv2.imread('/home/congdanh/Desktop/abc.jpg')
# (x,y,w,h) = cv2.selectROI(image)
x = 0
y = 0
h = 1000
w = 1000
# ROI = image[y:y+h, x:x+w]
#
# cv2.imshow("ROI", ROI)
# cv2.imwrite("ROI.png", ROI)
# cv2.waitKey(0)
crop_img = image[y:y + h, x:x + w]
cv2.imshow("cropped", crop_img)
# cv2.waitKey(0)
cv2.imwrite("crop.png", crop_img)
def main():
#set this to s to save the cells as an image to create an dataset.
user = "******"
path = r"images"
#output director to create the dataset
output_path = r"Dataset"
if not os.path.exists(output_path):
os.makedirs(output_path)
files = os.listdir(path)
print(files)
file_number = 5
img_path = os.path.join(path, files[file_number - 1])
img = cv2.imread(img_path)
print(img_path)
# input_img = resizing(img)
# output = input_img.copy()
input_img, img = resize(img)
sudoku_squares, intersections, bin_img, edges, output = preprocessing(
input_img, ret=True)
cv2.imshow("output", output)
cv2.imshow("bin_image", bin_img)
cv2.imshow("edges", edges)
#
# cv2.waitKey(0)
# print(sudoku_squares)
if user == "s":
i = 0
for cubes in sudoku_squares:
image_name = os.path.join(
output_path, r"sudoku_{}_{}.jpg".format(file_number, i))
# print(image_name)
i += 1
cv2.imwrite(image_name, cubes)
exit()
# sudoku_squares, intersections = preprocessing(input_img)
# cv2.imshow("bin_img", bin_img)
# cv2.imshow("input_img", edges)
cv2.imshow("Sudoku", img)
# cv2.imshow("input_img", input_img)
# intersections = intersections.reshape(9, 9)
sudoku = np.ones(81, dtype=np.int)
blank_indices = predictions(sudoku_squares, sudoku)
sudoku = sudoku.reshape(9, 9)
print(sudoku)
sudoku_obj = Sudoku_Solver(sudoku)
print("Sudoku to Solve :")
sudoku_obj.print()
print("Number of Unknowns : {}".format(sudoku_obj.unknowns))
input("Press enter to solve")
sudoku_obj.solve()
print("Solved Sudoku :")
sudoku_obj.print()
img = print_predictions(img, intersections, blank_indices,
sudoku_obj.solved_array)
cv2.imshow("solved", img)
cv2.waitKey(0)
is_train = tf.placeholder( tf.bool )
bn1, bn2, reconstruction_ori = csgan.build_reconstruction(cs_meas, is_train)
summary = tf.merge_all_summaries()
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, checkpointPath)
psnr = np.array([])
for imName in imList:
# Read image
im = cv2.imread(inputDir + imName,0)
im = cv2.normalize(im.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)
[height, width] = im.shape
# Determine the size of zero pad
rowPad = blockSize - (height % blockSize)
colPad = blockSize - (width % blockSize)
# Do zero padding
imPad = np.concatenate((im, np.zeros([rowPad, width])), axis=0)
imPad = np.concatenate((imPad, np.zeros([height+rowPad, colPad])),axis=1)
print imPad.shape
numBlocksRow = (height + rowPad)/blockSize
numBlocksCol = (width + colPad)/blockSize
""" Traffic light classification """
with self.sess_classification.as_default(
), self.graph_classification.as_default(), Timer('classification'):
sfmax = list(
self.sess_classification.run(
tf.nn.softmax(
self.out_graph.eval(
feed_dict={self.in_graph: [image]}))))
sf_ind = sfmax.index(max(sfmax))
## add a colored bbox and publish traffic light if needed
## rosparam set /tl_detector/publish_traffic_light true
if rospy.get_param('~publish_traffic_light', False):
cv2.rectangle(image, (0, 0), (31, 31),
self.index2color[sf_ind], 1)
self.traffic_light_pub.publish(
self.bridge.cv2_to_imgmsg(image, "rgb8"))
return self.index2msg[sf_ind]
if __name__ == "__main__":
classifier = TLClassifier(model_dir='model')
classifier.publish_traffic_light = False
for im in glob.glob('images/*.jpg'):
img = cv2.imread(im)
if not img is None:
color = classifier.get_classification(img)
print("--------------", color, "-------------------")
print(im, 'detected as', classifier.tl2str(color))
#!/usr/bin/python # -*- coding: utf-8 -*- # @File : template_match.py # @Time : 18-7-18 上午11:43 # @Author : Jee # @Email : [email protected] import cv2 import numpy as np from matplotlib import pyplot as plt img_rgb = cv2.imread('imgs/21.jpg') img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) template = cv2.imread('imgs/tmp.png',0) w, h = template.shape[::-1] res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED) threshold = 0.75 loc = np.where( res >= threshold) for pt in zip(*loc[::-1]): cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2) cv2.imshow("tmp",img_rgb) cv2.waitKey(0) # cv2.imwrite('res.png',img_rgb)
for control in controls:
name = control['name']
transformation = control['attr']
min_v = control.get('min', 0)
max_v = control.get('max', 255)
print(control, name, transformation, min_v, max_v)
cv.createTrackbar(name, WINDOW_NAME, min_v, max_v, partial(control_callback, image, transformation))
update_image(sample_image, g_Transformations)
sample_image = sys.argv[1]
sample_image = cv.imread(sample_image)
cv.namedWindow(WINDOW_NAME, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
controls = [
{'name': 'blur', 'min': 1, 'max': 33, 'attr': 'blur'},
{'name': 'eq hist', 'max': 1, 'attr': 'norm_histogram'},
{'name': 'binarize', 'min': 1, 'attr': 'binarize'},
# Morphology controls
{'name': 'Morphology kernel size', 'min': 1, 'max': 10, 'attr': 'morphology/kernel'},
{'name': 'Number of iterations', 'min': 1, 'max': 20, 'attr': 'morphology/iterations'},
# Hough controls
{'name': 'circles scale', 'min': 1, 'max': 10, 'attr': 'circles/scale'},
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('f.jpg', 0)
edges = cv2.Canny(img, 100, 200)
img_gaussian = cv2.GaussianBlur(img, (3, 3), 0)
kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]])
kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
img_prewittx = cv2.filter2D(img_gaussian, -1, kernelx)
img_prewitty = cv2.filter2D(img_gaussian, -1, kernely)
cv2.imshow("Prewitt X", img_prewittx)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
cv2.imshow("Prewitt Y", img_prewitty)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
cv2.imshow("Prewitt", img_prewittx + img_prewitty)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
