'NoneType' object has no attribute 'shape'
13,524
cv2.imread() and a couple of other operations in cv2 silently return None when it fails. This is not a very nice behavior to have for a function obviouly, as you would expect cv2 to throw some exception.
The problem is that the code is not checking for those cases. So depending on the source of the im variable in your code (probably cv2.imread('..\images/test_final2.jpg') at line 206), the im variable is being assigned None because the reading failed for some reason (file not found, invalid file format, etc).
To solve this problem, just ensure that the returned value is different from None when you call a cv2 function:
if __name__ == '__main__':
logging.basicConfig(filename='coins.log',filemode='w',level=logging.DEBUG)
logging.info("Program started")
if VIDEO:
cap = cv2.VideoCapture(camera)
if (cap.isOpened()== False):
logging.info("Error while opening camera.")
sys.exit(0)
_, image = cap.read()
logging.info("video capture mode")
else:
image = cv2.imread("./image.png")
if image == None:
if not os.path.exists("./image.png"):
logging.info("Still image not found.")
else:
logging.info("Error while opening still image.")
sys.exit(0)
logging.info("still image mode")
final_height = 640
scale = final_height / image.shape[0]
if VIDEO:
while(True):
success, image = cap.read()
if not success:
logging.info('Failed to read video')
sys.exit(0)
image = cv2.resize(image, None, fx=scale, fy=scale)
cv2.imshow("Original", image)
else: # still image
image = cv2.resize(image, None, fx=scale, fy=scale)
cv2.imshow("Original", image)
Author by
Hussein
Updated on June 10, 2022Comments
-
Hussein almost 2 years
I'm trying to run this project on my Mac.There are two main python files in this project I have to run and that's
calibrate.py, to open the camera and get the images, anddetectCoins.py, but I'm getting this error every time when I try to builddetectCoins.py:File "detectCoins.py", line 226, in <module> scale = finalHeight / im.shape[0] AttributeError: 'NoneType' object has no attribute 'shape'Can anyone tell me how to run this project properly? Thanks. the code
import numpy as np import logging, sys import yaml ## ========================================================================= # select video & camera and whether to use calibration VIDEO = 0 camera = 1 CAL = True # Beaware that camera calibration can move some objects out of the frame # and as a result contours will be incomplete and return a very small area # ## ========================================================================== #qIDref = 2.00*25.4 # ID-1 85.60 x 53.98mm IDrefW = 53.98 IDrefL = 85.60 def adjust_gamma(image, gamma=1.0): # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table) def getBoxDim(box1): # return length and width of rotated rect L = box1[1][1] W = box1[1][0] return max(L,W), min(L,W) def checkCoinDia(rc, Wp, Lp, cent): # check if radius is a valid coin ## absolute size using L and W, dia (mm) ## ## dime: 17.91 ## penny: 19.05 ## nickel: 21.21 ## quarter: 24.26 ## fifty-cent: 30.61 ## ID-1: (85.6 x 53.98) ## business crd ( 3.5*25.4 x 2.0*25.4 ) dia = rc * 2.0 * (IDrefW) / Wp # check coin dia in mm and color flag, penny == True result = -1.0 if dia< 16.2: result = -1.0 elif dia < 18.48: if penny == True: result = 1.0 else: result = 10.0 elif dia < 20.13: if penny == True: result = 1.0 else: result = 10.0 elif dia < 22.8: if penny == True: result = 1.0 else: result = 5.0 elif dia < 27.43: result = 25.0 elif dia < 32.0: result = 50.0 return result, dia def checkCenter(img, cir, thresh = [30,15]): # Check if center (x,y) is greater than bkgd threshold cir1 = cir.copy() radius = np.int32(6) # overwrite radius cir1[2] = radius metric, _ = getCircleColor(img, cir1, 'hsv') #yg = (thresh[1]/(thresh[0]+1)) * metric[0] + 10 #val = metric[0] if metric[0] < 0.4*thresh[0]: return True, metric else: return False, metric def getCircleColor(image, circ, flag = 'hsv'): # return mean of colors over circle from RGB input image if flag == 'hsv': color = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) elif flag == 'lab': color = cv2.cvtColor(image, cv2.COLOR_BGR2LAB) else: color = image # https://stackoverflow.com/questions/10469235/opencv-apply-mask-to-a-color-image circ = np.uint16(np.around(circ)) mask = np.full((image.shape[0], image.shape[1]), 0, dtype=np.uint8) cv2.circle(mask, (circ[0], circ[1]), circ[2], (255, 255, 255), -1) metric = cv2.mean(color, mask) return metric, mask def getDimePennyDecision(img, circ): # use hsv s-v space to discern dime from pennies metric, _ = getCircleColor(img, circ, flag = 'hsv') h = metric[0] s = metric[1] penny = True #if (1.10 * h -s + 38 > 0): if (6.4 * h - s - 75 > 0): penny = False return penny def findRangeAllContours(contours): # find x-y range of ALL objects & ID card xmin = 9999 ymin = 9999 xmax = 0 ymax = 0 for c in contours: extLeft = tuple(c[c[:, :, 0].argmin()][0]) extRight = tuple(c[c[:, :, 0].argmax()][0]) extTop = tuple(c[c[:, :, 1].argmin()][0]) extBot = tuple(c[c[:, :, 1].argmax()][0]) if extLeft[0] < xmin: xmin = extLeft[0] if extRight[0] > xmax: xmax = extRight[0] if extTop[1] < ymin: ymin = extTop[1] if extBot[1] > ymax: ymax = extBot[1] rect = [(xmin,ymin), (xmax,ymax)] return rect def getBkgdMetric(contour, flag = 'hsv'): # find color of background # rectRange = findRangeAllContours(contour) x1 = int(rectRange[0][0]/2) y1 = int(rectRange[0][1]/2) r = int(0.5 * np.sqrt(x1**2 + y1**2)) circ = [x1, y1, min(r, 15)] metric_bkgd, mask_bkgd = getCircleColor(blurred, circ, 'hsv') return metric_bkgd, rectRange """ ============ main ====================================== """ if __name__ == '__main__': logging.basicConfig(filename='coins.log',filemode='w',level=logging.DEBUG) logging.info("Program started") if VIDEO: cap = cv2.VideoCapture(camera) logging.info("video capture") if (cap.isOpened()== False): logging.info("Error opening video stream or file") else: im = cv2.imread('..\images/ID_card1.jpg') im = cv2.imread('..\images/ID_card2.jpg') im = cv2.imread('..\images/Lucky_ID.jpg') im = cv2.imread('..\images/ID_card1.jpg') im = cv2.imread('..\images/new_blk.jpg') #im = cv2.imread('..\images/compare_blk.jpg') im = cv2.imread('..\images/test_final2.jpg') #im = cv2.imread('..\images/non-touch.jpg') #im = cv2.imread('..\images\ID-1.jpg') #im = cv2.imread('..\images/velvBkgd.jpg') logging.info("still image") while(True): # read image # ======================================================== if VIDEO: success, im = cap.read() if not success: logging.info('Failed to read video') sys.exit(1) # We will run Object detection at an fixed height image finalHeight = 640 # resize image to height finalHeight scale = finalHeight / im.shape[0] image = cv2.resize(im, None, fx=scale, fy=scale) cv2.imshow("Original",image) """ Apply camera calibration here, using stored matrices # ====================================================== # file used is calibrate.py # matrices stored are: """ if CAL: with open('calibration.yaml') as f: loadeddict = yaml.load(f) K = loadeddict.get('camera_matrix') K = np.array(K) d = loadeddict.get('dist_coeff') d = np.array(d) # Read an example image and acquire its size h, w = image.shape[:2] # Generate new camera matrix from parameters newcameramatrix, roi = cv2.getOptimalNewCameraMatrix(K, d, (w,h), 0) # Generate look-up tables for remapping the camera image mapx, mapy = cv2.initUndistortRectifyMap(K, d, None, newcameramatrix, (w, h), 5) # Remap the original image to a new image newimg = cv2.remap(image, mapx, mapy, cv2.INTER_LINEAR) # Display old and new image if(0): cv2.imshow("Before map", image) cv2.imshow("After map", newimg) imageCorr = newimg else: imageCorr = image """ alter gamma """ gamma = adjust_gamma(imageCorr, 2.2) """ processing pipeline: # ========================================================== # calibrate camera & distortion # gamma # blur before gray # gray blurred image # edge gray image # """ output = gamma.copy() # process image blurred = cv2.GaussianBlur(gamma, (3,3), 0) cv2.imshow("Blurred", blurred) gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY) cv2.imshow("gray", gray) edged = cv2.Canny(gray, 50, 200, 10) # 50,200 cv2.imshow("Canny", edged) """ find bounding boxes for coins and ID-1 # =========================================================== # find all contours: # Pick out largest area as the ID-1 reference # use cv2.minAreaRect(cnt) on largest bounding box # standard ID-1 as reference (bank card or ID card) # # Note: contours NOT good for detecting touching coins # need segmentation for that. """ (_,contours,_) = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) ## debug --- draw contours #item1 = 0 #for circ in contours: #cv2.drawContours(output, circ, -1, (0,0,255), 3) #item1 = item1+1 #cv2.imshow('contours',output) #cv2.waitKey(2000) """ get background color """ metric_bkgd, error = getBkgdMetric(contours, 'hsv') #print((metric_bkgd)) if error[0][0]==0 or error[1][0]==image.shape[1]: print("ERROR: clipping image in x") if error[0][1]==0 or error[1][1]==image.shape[1]: print("ERROR clipping image in y") cmax = max(contours, key = cv2.contourArea) rectID = cv2.minAreaRect(cmax) # find rotated rectangle pnts = cv2.boxPoints(rectID) box = np.int0(pnts) cv2.drawContours(output,[box],0,(255,0,0),2) logging.info("box ID1") logging.info(box) logging.info("") """ find coins and draw circle & bounding rectange using HughCircles # ================================================================== """ HIGH = 175 # param1 LOW = 45 # param2 circles1 = 25 # set max upper limit for coins circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 20, circles1, \ param1=HIGH, param2=LOW, minRadius=15,maxRadius=40) #12,45 # ensure circles exist if circles is not None: # convert the (x, y) coordinates and radius of the circles to integers circlesInt = np.round(circles[0, :]).astype("int") """ loop over the (x, y) coordinates and radius of the Hough circles and use float for precision """ logging.info("ref") L , W = getBoxDim(rectID) logging.info([L, W]) logging.info("circles:") coins = 0 item = 0 amount = 0.0 for circ in circlesInt: #logging.info([item, circles[item]]) x = circles[0][item][0] y = circles[0][item][1] r = circles[0][item][2] # check range of x and y within imageCorr # --- do it here penny = getDimePennyDecision(blurred, circ) center, val2 = checkCenter(blurred, circ, thresh = metric_bkgd) #print([center, np.around(val2), np.around(metric_bkgd)]) print(val2) value, dia = checkCoinDia(r, W, L, penny) metric, mask_c = getCircleColor(blurred, circ, 'hsv') logging.info([item, int(10*dia)/10, value, np.around(metric), penny, center, val2]) #print([item, int(10*dia)/10, value, np.around(metric), penny, center, np.around(val2)]) if value < 0 or not center: color = (0,0,255) #cv2.putText(output, "{}".format('X'), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1) else: coins = coins + 1 amount = amount + value color = (0,255,0) # draw the circle in the output image, then print the circle # # corresponding to the center of the circle cv2.circle(output, (int(x), int(y)), int(r), color, 2) cv2.putText(output, "{}".format(int(10*dia)/10), (int(x) - 10, int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1) # cv2.putText(output, "{}".format(int(metric[ch])), (int(x) - 10, int(y)+15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1) cv2.putText(output, "{}".format(value), (int(x) - 10, int(y)+15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1) item = item + 1 logging.info("---") cv2.putText(output, "coin count: {}".format(coins), (30, 610), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) cv2.putText(output, "$: {}".format(amount/100.0), (30, 630), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) cv2.imshow("output", output) # analyze each coin for color ? if needed ... # ========================================================== # clean up and exit on waitKey # ========================================================== Wait = True if VIDEO==1 & (cv2.waitKey(10) & 0xFF == ord('q')): logging.info("exit waitKey") Wait = False if VIDEO == 0: while True: if cv2.waitKey(0) & 0xFF == ord('q'): logging.info("exit 'q' key") cv2.destroyAllWindows() Wait = False break else: cv2.waitKey(200) # frame rate if not Wait: break # save image to disc (use for project report) file_path = ".\Results/" cv2.imwrite(file_path + "original" + ".png", image) cv2.imwrite(file_path + "calibrated" + ".png", imageCorr) cv2.imwrite(file_path + "gamma" + ".png", gamma) cv2.imwrite(file_path + "gray" + ".png", gray) cv2.imwrite(file_path + "blur" + ".png", blurred) cv2.imwrite(file_path + "edged" + ".png", edged) cv2.imwrite(file_path + "results" + ".png", output) if VIDEO: cap.release() cv2.destroyAllWindows()