python类preprocess_input()的实例源码

def extract_image_features(img_path):
    model = models.VGG_16('weights/vgg16_weights.h5')
    img = image.load_img(img_path,target_size=(224,224))
    x = image.img_to_array(img)
    x = np.expand_dims(x,axis=0)
    x = preprocess_input(x)
    last_layer_output = K.function([model.layers[0].input,K.learning_phase()],
        [model.layers[-1].input])
    features = last_layer_output([x,0])[0]
    return features

def preprocess_image(image_path):
    img = load_img(image_path, target_size=(img_nrows, img_ncols))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg16.preprocess_input(img)
    return img

# util function to convert a tensor into a valid image

def testonefile(testmodel, img_path, confid_thresh=0.3, fordebug=False ):
        (s,w,h,c) = testmodel.layers[0].input_shape
    fimg, sx, sy, dx, dy, flip,ssx,ssy = genregiontruth_bnum.crop_image(img_path.strip(), w, h, randomize=False)
    xx = fimg.copy()
    img = fimg.astype(float)
    if fordebug:  # read label
        fn=img_path.replace("/JPEGImages/","/labels/")
        fn=fn.replace(".jpg",".txt")              #VOC
        boxlist = genregiontruth_bnum.readlabel(fn.strip(), sx, sy, dx, dy, flip, ssx, ssy)
        for box in boxlist:
            draw.rectangle([(box.x-box.w/2)*w,(box.y-box.h/2)*h,(box.x+box.w/2)*w,(box.y+box.h/2)*h])
    #
    ttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(preprocess_input(np.asarray([xx])), testmodel, confid_thresh,w,h,c)

    iimg = Image.fromarray(img.astype(np.uint8))
    draw = ImageDraw.Draw(iimg, 'RGBA')

    sortedindexlist = np.argsort(confid_value_list)
    colors=[]
    for i in range(3):
        for j in range(7):
            if i==0:
                rcolor = (j+1)*32
                gcolor = 0
                bcolor = 0
            elif i==1:
                rcolor = 0
                gcolor = (j+1)*32
                bcolor = 0
            else:
                rcolor = 0
                gcolor = 0
                bcolor = (j+1)*32
            colors.append( (rcolor, gcolor, bcolor, 127) )
    #print colors

    for i in range(len(confid_value_list)):
        index = sortedindexlist[len(confid_value_list)-i-1]
        for k in range(5): # thick line
            draw.rectangle([x0_list[index]+k,y0_list[index]+k,x1_list[index]-k,y1_list[index]-k], outline=colors[class_id_list[index]])

        labelim = Image.open(cfgconst.label_names[class_id_list[index]])
        draw.bitmap((x0_list[index],y0_list[index]),labelim)

        x = (x0_list[index]+x1_list[index])/2.
        y = (y0_list[index]+y1_list[index])/2.
        x0 = int(x/w*cfgconst.side)*w/cfgconst.side
        y0 = int(y/h*cfgconst.side)*h/cfgconst.side
        x1 = x0 + float(w)/cfgconst.side
        y1 = y0 + float(h)/cfgconst.side
        draw.rectangle([x0,y0,x1,y1], fill=colors[class_id_list[index]] )
        print cfgconst.label_names[class_id_list[index]].split('/')[1].split('.')[0]+': '+str(confid_value_list[index])
    del draw
    iimg.save('predicted.png')

def testvideo(testmodel, videofile, confid_thresh=0.2):
    print 'testdemo '+videofile
    #testmodel = load_model(model_weights_path, custom_objects={'yololoss': ddd.yololoss})
    (s,w,h,c) = testmodel.layers[0].input_shape

    cap = cv2.VideoCapture(videofile)

    while (cap.isOpened()):
        ret, frame = cap.read()
        if not ret:
            break
        #print frame
        nim = scipy.misc.imresize(frame, (w, h, c))
        img = nim
        xx = image.img_to_array(cv2.cvtColor(nim, cv2.COLOR_RGB2BGR))

        ttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(preprocess_input(np.asarray([xx])), testmodel, confid_thresh,w,h,c)
        # found confid box
                for x0,y0,x1,y1,classprob,class_id,confid_value in zip(x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list):
        #
            # draw bounding box
            cv2.rectangle(img, (x0, y0), (x1, y1), (255,255,255), 2)
            # draw classimg
                        classimg = cv2.imread(cfgconst.label_names[class_id])
                        if y0-classimg.shape[0] <= 0:
                            yst =0
                            yend =classimg.shape[0]
                        elif y0 >= img.shape[0]:
                            yst = img.shape[0]-classimg.shape[0]-1
                            yend = img.shape[0]-1
                        else:
                            yst = y0 - classimg.shape[0]
                            yend = y0

                        if x0+classimg.shape[1] >= img.shape[1]:
                            xst = img.shape[1]-classimg.shape[1]-1
                            xend = img.shape[1]-1
                        elif x0 <=0:
                            xst = 0
                            xend = classimg.shape[1]
                        else:
                            xst = x0
                            xend = x0+classimg.shape[1]

                        #

            img[yst:yend, xst:xend] = classimg
            # draw text
            font = cv2.FONT_HERSHEY_SIMPLEX
            cv2.putText(img, str(classprob), (x0,y0+classimg.shape[0]-1), font, 0.5,(255,255,255),2,cv2.LINE_AA)
            cv2.putText(img, str(confid_value), (x0,y1), font, 0.5,(128,255,255),1,cv2.LINE_AA)
            #
        cv2.imshow('frame',img)
        if cv2.waitKey(100) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()