Python cv2.UMat方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert entity.shape[1] == 1, "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def find_address(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('address_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        #showimg(crop_gray)
        w, h = cv2.UMat.get(template).shape[::-1]
        #t1 = round(time.time()*1000)
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        #t2 = round(time.time()*1000)
        #print 'time:%s'%(t2-t1)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(1700*x), top_left[1] + int(550*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def __init__(self, path, queueSize=128):
        # initialize the file video stream along with the boolean
        # used to indicate if the thread should be stopped or not
        self.stream = cv2.VideoCapture(path)
        self.stopped = False
        self.count = 0

        # initialize the queue used to store frames read from
        # the video file
        self.Q = Queue(maxsize=queueSize)

        # We need some info from the file first. See more at:
        # https://docs.opencv.org/4.1.0/d4/d15/group__videoio__flags__base.html#gaeb8dd9c89c10a5c63c139bf7c4f5704d
        self.width = int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH))
        self.height = int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT))

        # since this version uses UMat to store the images to
        # we need to initialize them beforehand
        self.frames = [0] * queueSize
        for ii in range(queueSize):
            self.frames[ii] = cv2.UMat(self.height, self.width, cv2.CV_8UC3) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def get_name(img):
        #    cv2.imshow("method3", img)
        #    cv2.waitKey()
        print('name')
        _, _, red = cv2.split(img) #split 會自動將UMat轉換回Mat
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 151, 50)
        #    red = cv2.medianBlur(red, 3)
        red = img_resize(red, 150)
        img = img_resize(img, 150)
        # showimg(red)
        # cv2.imwrite('name.png', red)
        #    img2 = Image.open('address.png')
        # img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return get_result_vary_length(red, 'chi_sim', img, '-psm 7')
        return get_result_vary_length(red, 'chi_sim', img, '--psm 7')
        # return punc_filter(pytesseract.image_to_string(img, lang='chi_sim', config='-psm 13').replace(" ","")) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def binaryMask(frame, x0, y0, width, height, framecount, plot ):
    global guessGesture, visualize, mod, saveImg
    
    cv2.rectangle(frame, (x0,y0),(x0+width,y0+height),(0,255,0),1)
    #roi = cv2.UMat(frame[y0:y0+height, x0:x0+width])
    roi = frame[y0:y0+height, x0:x0+width]
    
    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray,(5,5),2)
    
    th3 = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
    ret, res = cv2.threshold(th3, minValue, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
    
    if saveImg == True:
        saveROIImg(res)
    elif guessGesture == True and (framecount % 5) == 4:
        #ores = cv2.UMat.get(res)
        t = threading.Thread(target=myNN.guessGesture, args = [mod, res])
        t.start()
    elif visualize == True:
        layer = int(input("Enter which layer to visualize "))
        cv2.waitKey(1)
        myNN.visualizeLayers(mod, res, layer)
        visualize = False

    return res

#%%
# This is the new mask mode. It simply tries to remove the background content by taking a image of the 
# background and subtracts it from the new frame contents of the ROI window.
# So in order to use it correctly, keep the contents of ROI window stable and without your hand in it 
# and then press 'x' key. If you can see the contents of ROI window all blank then it means you are
# good to go for gesture prediction 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def generate_mask(x):
        name_mask_pic = cv2.UMat(cv2.imread('name_mask.jpg'))
        sex_mask_pic = cv2.UMat(cv2.imread('sex_mask.jpg'))
        nation_mask_pic = cv2.UMat(cv2.imread('nation_mask.jpg'))
        birth_mask_pic = cv2.UMat(cv2.imread('birth_mask.jpg'))
        year_mask_pic = cv2.UMat(cv2.imread('year_mask.jpg'))
        month_mask_pic = cv2.UMat(cv2.imread('month_mask.jpg'))
        day_mask_pic = cv2.UMat(cv2.imread('day_mask.jpg'))
        address_mask_pic = cv2.UMat(cv2.imread('address_mask.jpg'))
        idnum_mask_pic = cv2.UMat(cv2.imread('idnum_mask.jpg'))
        name_mask_pic = img_resize_x(name_mask_pic)
        sex_mask_pic = img_resize_x(sex_mask_pic)
        nation_mask_pic = img_resize_x(nation_mask_pic)
        birth_mask_pic = img_resize_x(birth_mask_pic)
        year_mask_pic = img_resize_x(year_mask_pic)
        month_mask_pic = img_resize_x(month_mask_pic)
        day_mask_pic = img_resize_x(day_mask_pic)
        address_mask_pic = img_resize_x(address_mask_pic)
        idnum_mask_pic = img_resize_x(idnum_mask_pic)
        cv2.imwrite('name_mask_%s.jpg'%pixel_x, name_mask_pic)
        cv2.imwrite('sex_mask_%s.jpg' %pixel_x, sex_mask_pic)
        cv2.imwrite('nation_mask_%s.jpg' %pixel_x, nation_mask_pic)
        cv2.imwrite('birth_mask_%s.jpg' %pixel_x, birth_mask_pic)
        cv2.imwrite('year_mask_%s.jpg' % pixel_x, year_mask_pic)
        cv2.imwrite('month_mask_%s.jpg' % pixel_x, month_mask_pic)
        cv2.imwrite('day_mask_%s.jpg' % pixel_x, day_mask_pic)
        cv2.imwrite('address_mask_%s.jpg' %pixel_x, address_mask_pic)
        cv2.imwrite('idnum_mask_%s.jpg' %pixel_x, idnum_mask_pic)

#用於生成模板 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def img_resize_gray(imgorg):
        
        #imgorg = cv2.imread(imgname)
        crop = imgorg
        size = cv2.UMat.get(crop).shape
        # print size
        height = size[0]
        width = size[1]
        # 參數是根據3840調的
        height = int(height * 3840 * x / width)
        # print height
        crop = cv2.resize(src=crop, dsize=(int(3840 * x), height), interpolation=cv2.INTER_CUBIC)
        return hist_equal(cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)), crop 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def find_name(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('name_mask_%s.jpg'%pixel_x, 0))
        # showimg(crop_org)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        # print(max_loc)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(700*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        # showimg(result)
        return cv2.UMat(result) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def find_sex(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('sex_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(300*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def find_idnum(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('idnum_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        #showimg(crop_gray)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(2300*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def get_sex(img):
        _, _, red = cv2.split(img)
        print('sex')
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        #    red = cv2.medianBlur(red, 3)
        #    cv2.imwrite('address.png', img)
        #    img2 = Image.open('address.png')
        red = img_resize(red, 150)
        # cv2.imwrite('sex.png', red)
        # img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return get_result_fix_length(red, 1, 'sex', '-psm 10')
        return get_result_fix_length(red, 1, 'chi_sim', '--psm 10')
        # return pytesseract.image_to_string(img, lang='sex', config='-psm 10').replace(" ","") 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def get_idnum_and_birth(img):
        _, _, red = cv2.split(img)
        print('idnum')
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        red = img_resize(red, 150)
        # cv2.imwrite('idnum_red.png', red)
        #idnum_str = get_result_fix_length(red, 18, 'idnum', '-psm 8')
        # idnum_str = get_result_fix_length(red, 18, 'eng', '--psm 8 ')
        img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        idnum_str = get_result_vary_length(red, 'eng', img, '--psm 8 ')
        return idnum_str, idnum_str[6:14] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def get_spots(self, thermal_image):
        CFlir.drawing = True
        image_copy = thermal_image.copy()
        original_copy = image_copy.copy()
        if len(original_copy.shape) < 3:
            cmap_copy = cv.applyColorMap(original_copy, cv.COLORMAP_JET)

        point = []
        spot_points = []
        flag = [True]
        cv.namedWindow('Image')
        cv.setMouseCallback('Image', CFlir.draw_spots, (point, flag) )
        while(1):         
            image_copy = original_copy.copy()
            for i in range(0,len(spot_points)):
                cv.circle(image_copy, spot_points[i] , 5, 0, -1)
                try:
                    cv.circle(cmap_copy, spot_points[i] , 5, 0, -1)
                except:
                    cv.circle(original_copy, spot_points[i] , 5, 0, -1)

            if len(point) > 0:
                cv.circle(image_copy, tuple(point) , 5, 0, -1)


            if flag[0] == False:
                spot_points.append(tuple(point))
                flag[0] = True


            cv.imshow('Image', image_copy)
            k = cv.waitKey(1) & 0xff
            
            if k == 13 or k == 141 :
                break

        CFlir.drawing = False
        cv.destroyAllWindows()
        # origi_copy = cv.UMat(origi_copy)
        if len(original_copy.shape) == 3:
            gray = cv.cvtColor(original_copy, cv.COLOR_BGR2GRAY)
        else:
            gray = cv.cvtColor(cmap_copy, cv.COLOR_BGR2GRAY)

        ret,thresh = cv.threshold(gray, 10, 255, cv.THRESH_BINARY_INV)
        contours, hierarchy = cv.findContours(thresh,cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE )
        self.spots = contours 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import UMat [as 別名]
def skinMask(frame, x0, y0, width, height, framecount, plot):
    global guessGesture, visualize, mod, saveImg
    # HSV values
    low_range = np.array([0, 50, 80])
    upper_range = np.array([30, 200, 255])
    
    cv2.rectangle(frame, (x0,y0),(x0+width,y0+height),(0,255,0),1)
    #roi = cv2.UMat(frame[y0:y0+height, x0:x0+width])
    roi = frame[y0:y0+height, x0:x0+width]
    
    hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
    
    #Apply skin color range
    mask = cv2.inRange(hsv, low_range, upper_range)
    
    mask = cv2.erode(mask, skinkernel, iterations = 1)
    mask = cv2.dilate(mask, skinkernel, iterations = 1)
    
    #blur
    mask = cv2.GaussianBlur(mask, (15,15), 1)
    
    #bitwise and mask original frame
    res = cv2.bitwise_and(roi, roi, mask = mask)
    # color to grayscale
    res = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
    
    if saveImg == True:
        saveROIImg(res)
    elif guessGesture == True and (framecount % 5) == 4:
        #res = cv2.UMat.get(res)
        t = threading.Thread(target=myNN.guessGesture, args = [mod, res])
        t.start()
    elif visualize == True:
        layer = int(input("Enter which layer to visualize "))
        cv2.waitKey(0)
        myNN.visualizeLayers(mod, res, layer)
        visualize = False
    
    return res


#%%