要做以下几件事情：
- 一共有多少辆车。
- 有多少个空余的车位。
- 哪个停车位被占用了，哪个停车位没有被占用。
读取图像：
拿到图像之后，我们需要将其预处理，低于120，或者高于255的都处理为0。
def select_rgb_white_yellow(self,image): #过滤掉背景 lower = np.uint8([120, 120, 120]) upper = np.uint8([255, 255, 255]) # lower_red和高于upper_red的部分分别变成0，lower_red～upper_red之间的值变成255,相当于过滤背景 white_mask = cv2.inRange(image, lower, upper) self.cv_show('white_mask',white_mask) masked = cv2.bitwise_and(image, image, mask = white_mask) self.cv_show('masked',masked) return masked
然后再将其与原始图像做与操作，这样的话，只有原始图像是255的像素点留下来了。
然后再做灰度处理，再做边缘检测：
手动选择有效区域：
def select_region(self,image): """ 手动选择区域 """ # first, define the polygon by vertices rows, cols = image.shape[:2] pt_1 = [cols*0.05, rows*0.90] pt_2 = [cols*0.05, rows*0.70] pt_3 = [cols*0.30, rows*0.55] pt_4 = [cols*0.6, rows*0.15] pt_5 = [cols*0.90, rows*0.15] pt_6 = [cols*0.90, rows*0.90] vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32) point_img = image.copy() point_img = cv2.cvtColor(point_img, cv2.COLOR_GRAY2RGB) for point in vertices[0]: cv2.circle(point_img, (point[0],point[1]), 10, (0,0,255), 4) self.cv_show('point_img',point_img) return self.filter_region(image, vertices)
之后做一个mask填充，然后将其分割出来：
def filter_region(self,image, vertices): """ 剔除掉不需要的地方 """ mask = np.zeros_like(image) if len(mask.shape)==2: cv2.fillPoly(mask, vertices, 255) self.cv_show('mask', mask) return cv2.bitwise_and(image, mask)
再利用霍夫变换检测直线，再过滤一些：
def hough_lines(self,image): #输入的图像需要是边缘检测后的结果 #minLineLengh(线的最短长度，比这个短的都被忽略)和MaxLineCap（两条直线之间的最大间隔，小于此值，认为是一条直线） #rho距离精度,theta角度精度,threshod超过设定阈值才被检测出线段 return cv2.HoughLinesP(image, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4) def draw_lines(self,image, lines, color=[255, 0, 0], thickness=2, make_copy=True): # 过滤霍夫变换检测到直线 if make_copy: image = np.copy(image) cleaned = [] for line in lines: for x1,y1,x2,y2 in line: if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55: cleaned.append((x1,y1,x2,y2)) cv2.line(image, (x1, y1), (x2, y2), color, thickness) print(" No lines detected: ", len(cleaned)) return image
def identify_blocks(self,image, lines, make_copy=True): if make_copy: new_image = np.copy(image) #Step 1: 过滤部分直线 cleaned = [] for line in lines: for x1,y1,x2,y2 in line: if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55: cleaned.append((x1,y1,x2,y2)) #Step 2: 对直线按照x1进行排序 import operator list1 = sorted(cleaned, key=operator.itemgetter(0, 1)) #Step 3: 找到多个列，相当于每列是一排车 clusters = {} dIndex = 0 clus_dist = 10 for i in range(len(list1) - 1): distance = abs(list1[i+1][0] - list1[i][0]) if distance <= clus_dist: if not dIndex in clusters.keys(): clusters[dIndex] = [] clusters[dIndex].append(list1[i]) clusters[dIndex].append(list1[i + 1]) else: dIndex += 1 #Step 4: 得到坐标 rects = {} i = 0 for key in clusters: all_list = clusters[key] cleaned = list(set(all_list)) if len(cleaned) > 5: cleaned = sorted(cleaned, key=lambda tup: tup[1]) avg_y1 = cleaned[0][1] avg_y2 = cleaned[-1][1] avg_x1 = 0 avg_x2 = 0 for tup in cleaned: avg_x1 += tup[0] avg_x2 += tup[2] avg_x1 = avg_x1/len(cleaned) avg_x2 = avg_x2/len(cleaned) rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2) i += 1 print("Num Parking Lanes: ", len(rects)) #Step 5: 把列矩形画出来 buff = 7 for key in rects: tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1])) tup_botRight = (int(rects[key][2] + buff), int(rects[key][3])) cv2.rectangle(new_image, tup_topLeft,tup_botRight,(0,255,0),3) return new_image, rects
按列划分区域：
再划分更细：
之后再构建神经网络，对方框里面的图片进行分类。
完整代码 ：https://github.com/ZhiqiangHo/Opencv-Computer-Vision-Practice-Python-
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