def train_lbp(): train_dir = "/home/gast/ImageHashing/face_train/" suffix = ".png" size = 200 train = [] for fn in sorted(os.listdir(train_dir)): if fn.endswith(suffix): rgb = Image.load(train_dir + fn) gray = Image.rgb2gray(rgb) resized = Image.resize(gray,size) img = Image.gray2real(resized) lbps = local_binary_pattern(img,3,24) #print(flatten(lbps)) #print(len(flatten(lbps))) train.append(flatten(lbps)) #print(lbps) pca = PCA(n_components=100) #print(len(train)) #print(len(train[0])) train_np = np.array(train) #print(train_np.shape) pca.fit(train_np) return pca
def train_eigen(): # train_dir = "/home/gast/ImageHashing/face_train/" train_dir = "/home/gast/ImageHashing/FEC/fec_images_autoadjust/" suffix = ".png" size = 200 train = [] for fn in sorted(os.listdir(train_dir)): if fn.endswith(suffix): rgb = Image.load(train_dir + fn) gray = Image.rgb2gray(rgb) resized = Image.resize(gray,size) img = Image.gray2real(resized) #lbps = list(flatten(Lbp.real_hash(img))) img_flat = list(flatten(img)) # print(len(flatten(lbps))) train.append(img_flat) #print(lbps) t = np.array(train) #print("len of train"+str(t.shape)) pca = PCA(n_components=100) pca.fit(train) return pca
def __init__(self, x, y, menu): self.pos = (x, y) self.menu = menu self.rect = Button.OFF_IMAGE_RECT[self.menu] self.image = Image.load("IMAGE/TitleMenu.png") self.mouse_point = None if (Button.SELECT_WAV == None): Button.SELECT_WAV = load_wav("SOUND/stomp.wav") if (Button.DES_IMAGE == None): Button.DES_IMAGE = Image.load("IMAGE/Description.png") if (Button.DES_ON_WAV == None or Button.DES_OFF_WAV == None): Button.DES_ON_WAV = load_wav("SOUND/description_on.wav") Button.DES_OFF_WAV = load_wav("SOUND/description_off.wav")
def __init__(self, left, bottom, width, x, y, menu): self.image = Image.load("IMAGE/TitleMenu.png") self.src_rect = (self.left, self.bottom, self.width, self.height) = (left, bottom, width, 90) self.pos = (x, y) self.menu = menu self.mouse_point = None if (Button.SELECT_WAV == None): Button.SELECT_WAV = load_wav("SOUND/stomp.wav")
def load(): global sprite_image if (sprite_image is None): sprite_image = Image.load("IMAGE/Sprite.png") with open("JSON/ObjectRect.json") as file: data = json.load(file) for name in data: #print(name) sprite_rects[name] = tuple(data[name])
def TurtlePainting(Image,filtervalue): pix=Image.load() #turtle.speed(0) turtle.tracer(0) turtle.penup() width=Image.size[0] height=Image.size[1] turtle.setup(width,height+30,-turtle.window_width(),-turtle.window_height()) for i in range(height): for j in range(width): if pix[j,i][0]<=filtervalue: turtle.setpos(j-turtle.window_width()/2,-i+turtle.window_height()/2-10) turtle.dot(8)
def __init__(self): self.pos = (100, 300) self.delta = (0, 0) self.fidx = 0 self.time = 0 self.prev_state = None self.state = Mario.RIGHT_IDLE self.FPS = 7 self.image = Image.load("IMAGE/Mario.png") self.is_collide = False if (Mario.JUMP_WAV == None): Mario.JUMP_WAV = load_wav("SOUND/jump.wav") if (Mario.LIFE_LOST_WAV == None): Mario.LIFE_LOST_WAV = load_wav("SOUND/life lost.wav") Mario.LIFE_LOST_WAV.set_volume(50)
def train_lbp(): train_dir = "/home/gast/ImageHashing/face_train/" suffix = ".png" size = 200 train = [] for fn in sorted(os.listdir(train_dir)): if fn.endswith(suffix): rgb = Image.load(train_dir + fn) gray = Image.rgb2gray(rgb) resized = Image.resize(gray,size) img = Image.gray2real(resized) lbps = list(flatten(Lbp.real_hash(img))) # print(len(flatten(lbps))) train.append(lbps) #print(lbps) pca = PCA(n_components=500) pca.fit(train) return pca
def get_width_height(img_name): Image.load(img_name) x, y = img.size return (x, y)
cameraDir = cameraUP.cross(gaze.vector).normalized() #compute field of view ratio = float (image.height)/float(image.width) #45 degree field of view fieldRad = math.pi * (float(45) / float(2) / float(180)) #get world dimensions width = (math.tan(fieldRad) * 2.0) height = ratio * math.tan(fieldRad) * 2.0 pixelWidth = width / (image.width - 1) pixelHeight = height / (image.height - 1) #begin ray tracing for y in range(image.height): for x in range(image.width): xOffset = cameraUP*(x * pixelWidth - (width/2)) yOffset = cameraDir*(y * pixelHeight - (height/2)) #if x is 1 and y is 1: # print xOffset.x, xOffset.y, xOffset.z # print yOffset.x, yOffset.y, yOffset.z ray = reverseRay(gaze.point, gaze.vector + xOffset + yOffset) color = world.rayColor(ray) image.load(x,y,*color) print 'All done, look in this directory for ' + image.name image.toFile()
import Image imgfile = '~/Challenge/oxygen.png' oxyim = Image.load(imgfile) pix_val = list(oxyim.getdata()) pix_mat = [] pix_ind = 0 for i in range(95): pix_matr.append([]) for j in range(629): pix_matr[i].append(pix_val[pix_ind]) pix_ind += 1 for i in range(40,55): print "Row "+str(i) for j in range(40): print pix_matr[i][j] # first repeating line is 43 testrow = pix_matr[43] for i in range(0, 608, 7): output.append(testrow[i][0]) decoded = '' for i in range(len(output)): decoded += chr(output[i]) print decoded recode = [105, 110, 116, 101, 103, 114, 105, 116, 121]
def compute_hash(filename): re = 64 if args.r != None: re = args.r[0] rgb = Image.load(filename) gray = Image.rgb2gray(rgb) gray_pp = gray if(args.pp != None and args.pp[0] == 'gb'): gray_pp = Image.gauss_blur(gray,2) if(args.pp != None and args.pp[0] == 'tt'): gray_pp = tt_pipeline(gray) resized = Image.resize(gray_pp,re) img = Image.gray2real(resized) hash_result = [] if(args.cm != None and args.cm[0] == "ham"): if(args.hm != None): if args.hm[0] == "ah": img = Image.resize(img,8) hash_result = Average.bin_hash(img) if args.hm[0] == "dh": img = Image.resize2(img,9,8) hash_result = Difference.bin_hash(img) if args.hm[0] == "dct": hash_result = Dct.bin_hash(img,8,8) if args.hm[0] == "zh": hash_result = Zernike.bin_hash(img,0,8) if args.hm[0] == "pzh": hash_result = PseudoZernike.bin_hash(img,0,11) if args.hm[0] == "rash": print("Radon Hash can only output reals") if args.hm[0] == "wu": input_wu = Image.resize(gray,384) hash_result = Wu.bin_hash(input_wu) else: if(args.hm != None): if args.hm[0] == "ah": img = Image.resize(img,8) hash_result = Average.real_hash(img) if args.hm[0] == "dh": img = Image.resize2(img,9,8) hash_result = Difference.real_hash(img) if args.hm[0] == "dct": #img1 = exposure.equalize_hist(img) hash_result = Dct.real_hash(img,8,8) if args.hm[0] == "zh": hash_result = Zernike.real_hash(img,0,12) if args.hm[0] == "pzh": hash_result = PseudoZernike.real_hash(img,0,11) if args.hm[0] == "lbp": hash_result = Lbp.real_hash(img) if args.hm[0] == "lbp1": # im = Image.resize2(gray,220,220) im = Image.resize(gray,220) im2 = Image.gray2real(im) img_blur = Image.gauss_blur(im2,2) hash_result = Lbp.real_hash(img_blur) #hash_result = Lbp.bin_hash(img_blur) #if args.hm[0] == "lbp_pca": # pcad = pickle.load(open("lbp_pca_train.p", "rb" )) # hash_result = Lbp.pca_hash(img,pcad) #print(len(hash_result)) #if args.hm[0] == "eigen": # model = pickle.load(open("eigen_model.p", "rb" )) # hash_result = eigen.transform_eigen(img,model) if args.hm[0] == "fft": hash_result = Fft.fft_hash(img) if args.hm[0] == "rash": img = Image.resize(img,63) hash_result = Radon.real_hash(img) # default if hash_result == []: print("default") hash_result = Dct.real_hash(img,8,8) return hash_result
def __init__(self, file, player=None): self.image = Image.load(file) self.mario = player self.stage_level = 1
import Image import numpy as np im = Image.load('nr33.jpg') im = im.convert('L') arr = np.fromiter(iter(im.getdata()), np.uint8) arr.resize(im.height, im.width) arr ^= 0xFF # invert inverted_im = Image.fromarray(arr, mode='L') inverted_im.show()
import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.feature import match_template import Image needle_rgb = Image.load("90a_scan.png") needle = Image.rgb2gray(needle_rgb) haystack_rgb = Image.load("90a_camera.jpg") haystack = Image.rgb2gray(haystack_rgb) result = match_template(haystack, needle) ij = np.unravel_index(np.argmax(result), result.shape) x, y = ij[::-1] fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 3)) ax1.imshow(needle) ax1.set_axis_off() ax1.set_title('template') ax2.imshow(haystack) ax2.set_axis_off() ax2.set_title('image') # highlight matched region hcoin, wcoin = needle.shape rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none') ax2.add_patch(rect)