我们从Python开源项目中，提取了以下25个代码示例，用于说明如何使用cv2.CV_LOAD_IMAGE_COLOR。
def get_image_compressed(self): rospy.loginfo("Getting image...") image_msg = rospy.wait_for_message( "/wide_stereo/left/image_raw/compressed", CompressedImage) rospy.loginfo("Got image!") # Image to numpy array np_arr = np.fromstring(image_msg.data, np.uint8) # Decode to cv2 image and store cv2_img = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR) img_file_path = "/tmp/telegram_last_image.png" cv2.imwrite(img_file_path, cv2_img) rospy.loginfo("Saved to: " + img_file_path) return img_file_path # Define a few command handlers
def dirClassificationSentiment(dirName, modelName, modelType): types = ('*.jpg', '*.png',) filesList = [] for files in types: filesList.extend(glob.glob(os.path.join(dirName, files))) filesList = sorted(filesList) print filesList Features = [] plt.close('all'); ax = plt.gca() plt.hold(True) for fi in filesList: P, classNames = fileClassification(fi, modelName, modelType) im = cv2.imread(fi, cv2.CV_LOAD_IMAGE_COLOR) Width = 0.1; Height = 0.1; startX = P[classNames.index("positive")]; startY = 0; myaximage = ax.imshow(cv2.cvtColor(im, cv2.cv.CV_RGB2BGR), extent=(startX-Width/2.0, startX+Width/2.0, startY-Height/2.0, startY+Height/2.0), alpha=1.0, zorder=-1) plt.axis((0,1,-0.1,0.1)) plt.show(block = False); plt.draw() plt.show(block = True);
def visualizeFeatures(Features, Files, Names): y_eig, coeff = pcaDimRed(Features, 2) plt.close("all") print y_eig plt.subplot(2,1,1); ax = plt.gca() for i in range(len(Files)): im = cv2.imread(Files[i], cv2.CV_LOAD_IMAGE_COLOR) Width = 0.2; Height = 0.2; startX = y_eig[i][0]; startY = y_eig[i][1]; print startX, startY myaximage = ax.imshow(cv2.cvtColor(im, cv2.cv.CV_RGB2BGR), extent=(startX-Width/2.0, startX+Width/2.0, startY-Height/2.0, startY+Height/2.0), alpha=1.0, zorder=-1) plt.axis((-3,3,-3,3)) # Plot feaures plt.subplot(2,1,2) ax = plt.gca() for i in range(len(Files)): plt.plot(numpy.array(Features[i,:].T)); plt.xticks(range(len(Names))) plt.legend(Files) ax.set_xticklabels(Names) plt.setp(plt.xticks()[1], rotation=90) plt.tick_params(axis='both', which='major', labelsize=8) plt.tick_params(axis='both', which='minor', labelsize=8) plt.show()
def scriptDemo(): dirName = "demoData/dofDemo" types = ('*.jpg', ) imageFilesList = [] for files in types: imageFilesList.extend(glob.glob(os.path.join(dirName, files))) imageFilesList = sorted(imageFilesList) print imageFilesList Features = numpy.zeros( (len(imageFilesList), 7) ) for i, f in enumerate(imageFilesList): img = cv2.imread(f, cv2.CV_LOAD_IMAGE_COLOR) # read image [F, names] = getDepthOfFieldFeature2(img) Features[i,:] = F # Features[i,j] contains the j-th feature of the i-th file for i in range(7): plt.subplot(7,1,i+1); plt.bar(range(Features[:,i].shape[0]), Features[:,i]) plt.title(names[i]) plt.show() print Features #scriptDemo()
def decode(self, msg): fn = os.path.join(self.directory_, msg) if os.path.exists(fn): im = cv2.imread(fn, cv2.CV_LOAD_IMAGE_COLOR if self.color_ \ else cv2.CV_LOAD_IMAGE_GRAYSCALE) return im_resize(im, shape=self.shape_) else: raise Exception('File does not exist') # Basic type for image annotations
def __init__(self, directory, max_files=20000): """ SUN RGB-D Dataset reader Note: First run find . | grep seg.mat > annotations.txt (in SUNRGBD folder) @params directory: SUNRGBD directory listing with image/*.png, and seg.mat files """ self.directory_ = os.path.expanduser(directory) with open(os.path.join(self.directory_, 'image.txt')) as f: rgb_files = f.read().splitlines() with open(os.path.join(self.directory_, 'depth.txt')) as f: depth_files = f.read().splitlines() assert(len(rgb_files) == len(depth_files)) self.rgb_files_ = [os.path.join(self.directory_, fn) for fn in fnmatch.filter(rgb_files,'*mit_*')][:max_files] self.depth_files_ = [os.path.join(self.directory_, fn) for fn in fnmatch.filter(depth_files,'*mit_*')][:max_files] self.label_files_ = [ os.path.join( os.path.split( os.path.split(fn)[0])[0], 'seg.mat') for fn in self.rgb_files_ ] if not len(self.rgb_files_): raise RuntimeError('{} :: Failed to load dataset'.format(self.__class__.__name__)) print('{} :: Loading {} image/depth/segmentation pairs'.format(self.__class__.__name__, len(self.rgb_files_))) self.rgb_ = imap(lambda fn: self._pad_image(cv2.imread(fn, cv2.CV_LOAD_IMAGE_COLOR)), self.rgb_files_) self.depth_ = imap(lambda fn: self._pad_image(cv2.imread(fn, -1)), self.depth_files_) self.labels_ = imap(self._process_label, self.label_files_) # self.target_hash_ = {item.encode('utf8'): idx+1 # for idx, item in enumerate(loadmat('data/sun3d/seg37list.mat', squeeze_me=True)['seg37list'])} # self.target_unhash_ = {v:k for k,v in self.target_hash_.iteritems()} # self.target_hash_ = SUNRGBDDataset.target_hash # self.target_unhash_ = SUNRGBDDataset.target_unhash # @property # def target_unhash(self): # return self.objects_.target_unhash # @property # def target_hash(self): # return self.objects_.target_hash
def callback_camera(self, data): # format: rgb8; jpeg compressed bgr8 np_img = np.fromstring(data.data, dtype=np.uint8) img = cv2.imdecode(np_img, cv2.CV_LOAD_IMAGE_COLOR) img = cv2.cvtColor(cv2.resize(img, (self.width, self.height)), cv2.COLOR_BGR2GRAY) self.image = np.reshape(img, newshape=(self.width, self.height, 1)) / 256.0
def callback_camera_y(self, data): # format: rgb8; jpeg compressed bgr8 np_img = np.fromstring(data.data, dtype=np.uint8) img = cv2.imdecode(np_img, cv2.CV_LOAD_IMAGE_COLOR) img = cv2.cvtColor(cv2.resize(img, (self.width, self.height)), cv2.COLOR_BGR2GRAY) self.image_y = np.reshape(img, newshape=(self.width, self.height, 1)) / 256.0
def getFeaturesFromFile(fileName, PLOT = False): img = cv2.imread(fileName, cv2.CV_LOAD_IMAGE_COLOR) # read image #img2 = resizeFrame(img, 128)# resize #img2[:,:,0] = img2[:,:,0] + 3.5 * img2.std() * np.random.random([img2.shape[0], img2.shape[1]]) #img2[:,:,1] = img2[:,:,1] + 3.5 * img2.std() * np.random.random([img2.shape[0], img2.shape[1]]) #img2[:,:,2] = img2[:,:,2] + 3.5 * img2.std() * np.random.random([img2.shape[0], img2.shape[1]]) #plt.imshow(img2) #plt.show() #[F, N] = featureExtraction(img2, PLOT) # feature extraction [F, N] = featureExtraction(img, PLOT) # feature extraction return F, N
def processContoursinGt(path): folder = sort_files(path) length_cont_gt = [] for i in range(20,len(folder)): newPath = path + "/0 (" + str(i) + ")" + ".png" img = cv2.imread(newPath,cv2.CV_LOAD_IMAGE_COLOR) #print "Image in processContour: ",img gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (31, 31), 0) thresh = cv2.threshold(gray, 25, 255, cv2.THRESH_BINARY)[1] thresh = cv2.dilate(thresh, None, iterations=2) (cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) length_cont_gt.append(len(cnts)) return length_cont_gt
def processContoursinFr(path1): folder1 = sort_files_fr(path1) length_cont_fr = [] for i in range(1,len(folder1)): newPath = path1 + "/" + str(i)+ ".jpg" img = cv2.imread(newPath,cv2.CV_LOAD_IMAGE_COLOR) #print "Image in processContourinFr: ",img gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (31, 31), 0) thresh = cv2.threshold(gray, 25, 255, cv2.THRESH_BINARY)[1] thresh = cv2.dilate(thresh, None, iterations=2) (cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) length_cont_fr.append(len(cnts)) return length_cont_fr
def fetch_cvimage_from_url(url, maxsize=10 * 1024 * 1024): req = requests.get(url, timeout=5, stream=True) content = '' for chunk in req.iter_content(2048): content += chunk if len(content) > maxsize: req.close() raise ValueError('Response too large') img_array = np.asarray(bytearray(content), dtype=np.uint8) cv2_img_flag = cv2.CV_LOAD_IMAGE_COLOR image = cv2.imdecode(img_array, cv2_img_flag) return image
def receive_frame(self,c,tkk): print "Waiting to receive frames of video from Server...." data = np.empty((700,500), dtype = np.int8) self.sock.recv_into(data) raw=cv2.imdecode(data,cv2.CV_LOAD_IMAGE_COLOR) cv2.imwrite("frame%d.jpeg" %c,raw) path="frame"+str(c)+".jpeg" print "pATH :"+path print c return path
def read_image(filename): image = cv2.imread(filename,cv2.CV_LOAD_IMAGE_COLOR) image = cv2.resize(image, (resize_height, resize_width)) return image
def get_word_image(self, gray_scale=True): col_type = None if gray_scale: col_type = cv2.CV_LOAD_IMAGE_GRAYSCALE else: col_type = cv2.CV_LOAD_IMAGE_COLOR # load the image ul = self.bounding_box['upperLeft'] wh = self.bounding_box['widthHeight'] img = cv2.imread(self.image_path, col_type) if not np.all(self.bounding_box['widthHeight'] == -1): img = img[ul[1]:ul[1]+wh[1], ul[0]:ul[0]+wh[0]] return img
def __MR_readimg(self,img): if isinstance(img,str): # a image path img = cv2.imread(img,cv2.CV_LOAD_IMAGE_COLOR) # img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB).astype(float)/255 # img = cv2.cvtColor(img,cv2.COLOR_BGR2LAB).astype(float)/255 img = cv2.cvtColor(img,cv2.COLOR_RGB2LAB).astype(float)/255 # h = 100 # w = int(float(h)/float(img.shape[0])*float(img.shape[1])) return img #cv2.resize(img,(w,h))
def match_face(model, pair): global_conf = None nparr_model = np.fromstring(model, np.uint8) path = cv2.imdecode(nparr_model, cv2.CV_LOAD_IMAGE_COLOR) recognizer = cv2.face.createLBPHFaceRecognizer() # path = './train_dir/yu/yu2.jpg' model_faces, model_labels = mtcnn.get_face(path) print model_labels model_faces_gray = [] for face in model_faces: gray_image = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) model_faces_gray.append(gray_image) recognizer.train(model_faces_gray, np.array(model_labels)) nparr_pair = np.fromstring(pair, np.uint8) imgPath = cv2.imdecode(nparr_pair, cv2.CV_LOAD_IMAGE_COLOR) # imgPath = './train_dir/yu/yu.jpg' # img_pair = cv2.imread(path) pair_faces, pair_labels = mtcnn.get_face(imgPath) pair_faces_gray = [] for face in pair_faces: gray_image = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) pair_faces_gray.append(gray_image) for face in pair_faces_gray: global global_conf nbr_predicted, conf = recognizer.predict(face) print "Recognized with confidence {}".format(conf) global_conf = conf return global_conf
def im_callback(self, data): np_arr = np.fromstring(data.data, np.uint8) #im = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR) bgr_im = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR) # Need to convert BGR image to RGB b,g,r = cv2.split(bgr_im) rgb_im = cv2.merge([r,g,b]) self.im_data.append( rgb_im )
def scriptDemo(): dirName = "demoData/lineDemo" types = ('*.jpg', ) imageFilesList = [] for files in types: imageFilesList.extend(glob.glob(os.path.join(dirName, files))) imageFilesList = sorted(imageFilesList) Features = np.zeros( (len(imageFilesList), 7) ) labels = [] for i, f in enumerate(imageFilesList): print f if ntpath.basename(f)[0:5]=="noPer": labels.append(0) else: labels.append(1) img = cv2.imread(f, cv2.CV_LOAD_IMAGE_COLOR) # read image [F, names] = getLineFeatures(img) Features[i,:] = F FeaturesPrespective = Features[:,4:7]; fig = plt.figure() color = ["ro","gx"] labels = np.array(labels) ax = fig.add_subplot(111, projection='3d') ax.plot(FeaturesPrespective[np.nonzero(labels==0)[0],0], FeaturesPrespective[np.nonzero(labels==0)[0],1], FeaturesPrespective[np.nonzero(labels==0)[0],2], color[0], label='Non Perspective') ax.plot(FeaturesPrespective[np.nonzero(labels==1)[0],0], FeaturesPrespective[np.nonzero(labels==1)[0],1], FeaturesPrespective[np.nonzero(labels==1)[0],2], color[1], label='Perspective') ax.set_xlabel('Close Intersections - 5%') ax.set_ylabel('Close Intersections - 20%') ax.set_zlabel('Acute Angles') plt.legend(loc='upper left', numpoints=1) plt.show() for f in range(Features.shape[0]): print "{0:.4f}\t{1:.4f}\t{2:.4f}".format(Features[f, 4], Features[f, 5], Features[f, 6]) # For generating figures for paper #scriptDemo() # test intersection: #A1 = np.array([0.0,0.0]) #A2 = np.array([4.0,2.0]) #B1 = np.array([1.0,1.2]) #B2 = np.array([2.0,1.0]) #plt.plot([A1[0], A2[0]], [A1[1],A2[1]]) #plt.plot([B1[0], B2[0]], [B1[1],B2[1]]) #[X, Y] = seg_intersect(A1, A2, B1, B2) #print X, Y #plt.plot(X, Y, '*'); #plt.show()
