# 导入必要的包from scipy.spatial import distance as distfrom imutils import perspectivefrom imutils import contoursimport numpy as npimport argparseimport imutilsimport cv2
def midpoint(ptA, ptB): return ((ptA[0]+ptB[0])*0.5, (ptA[1]+ptB[1])*0.5)
# 构造解析参数ap = argparse.ArugmentParser()ap.add_argument("-i", "--image", required=True,  help="path to the input image")ap.add_argument("-w", "--width", required=True, help="width of the left-most object in the image(in inches)")args = vars(ap.parse_args()

pip3 install imutils

pip3 install --upgrade imutils

# 导入图片转换为灰度图，并进行轻微的模糊image = cv2.imread(args["image"])gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)gray = cv2.GaussianBlur(gray, (7, 7), 0)
# 执行边缘检测# 然后在物体之间的边缘执行膨胀+腐蚀操作使其缝隙闭合edged = cv2.Canny(gray, 50, 100)edged = cv2.dilate(edged, None, iterations=1)edged = cv2.erode(edged, None, iterations=1)
# 在边缘图中查找轮廓cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)cnts = imutils.grab_contours(cnts)
# 从左往右对轮廓进行排序# 初始化'pixels per metric' 校准变量(cnts, _) = contours.sort_contours(cnts)pixelsPerMetric = None

# 分布遍历这些轮廓for c in cnts: # 如果轮廓不够大，直接忽略 if cv2.contourArea(c)<100: continue  # 计算轮廓的选择框 orig = image.copy() box = cv2.minAreaRect(c) box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box) box = np.array(box, dtype="int")  # 对轮廓点进行排序，顺序为左上，右上，右下和左下 # 然后绘制旋转边界框的轮廓 box = perspective.order_points(box) cv2.drawContours(orig, [box.astype("int")], -1, (0,255,0), 2)  # 遍历原始点并绘制出来 for (x, y) in box: cv2.circle(orig, (int(x), int(y)), 5, (0,0,255), -1)

 # 打开有序的边界框，然后计算左上和右上坐标之间的中点， # 再计算左下和右下坐标之间的中点 (tl, tr, br, bl) = box (tltrX, tltrY) = midpoint(tl, tr) (blbrX, blbrY) = midpoint(bl, br)
 # 计算左上点和右上点之间的中点 # 然后是右上角和右下角之间的中点 (tlblX, tlblY) = midpoint(tl, bl) (trbrX, trbrY) = midpoint(tr, br)
 # 在图中画出中点 cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1) cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1) cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1) cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
 # 在中点之间绘制线 cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)), (255, 0, 255), 2) cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)), (255, 0, 255), 2)

 # 计算中点间的欧式距离 dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY)) dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))  # 如果pixels per metric还未初始化， # 则将其计算为像素与提供的度量的比率（本例中为英寸） if pixelsPerMetric is None: pixelsPerMetric = dB / args["width"]

 # 计算物体的大小 dimA = dA / pixelsPerMetric dimB = dB / pixelsPerMetric  # 在图中画出物体的大小 cv2.putText(orig, "{:.1f}in".format(dimA), (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2) cv2.putText(orig, "{:.1f}in".format(dimB), (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)  # 显示输出图片 cv2.imshow("Image", orig) cv2.waitKey(0)

python3 object_size.py --image images/example_01.png --width 0.955