247 lines
9.3 KiB
Python
247 lines
9.3 KiB
Python
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import cv2
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import numpy as np
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import math
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import Polygon as plg
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def watershed1(image, viz=False):
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boxes = []
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image
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if viz:
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cv2.imshow("gray", gray)
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cv2.waitKey()
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ret, binary = cv2.threshold(gray, 0.6 * np.max(gray), 255, cv2.THRESH_BINARY)
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if viz:
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cv2.imshow("binary", binary)
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cv2.waitKey()
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# 形态学操作，进一步消除图像中噪点
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kernel = np.ones((3, 3), np.uint8)
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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mb = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2) # iterations连续两次开操作
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sure_bg = cv2.dilate(mb, kernel, iterations=3) # 3次膨胀,可以获取到大部分都是背景的区域
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if viz:
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cv2.imshow("sure_bg", sure_bg)
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cv2.waitKey()
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# 距离变换
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dist = cv2.distanceTransform(mb, cv2.DIST_L2, 5)
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if viz:
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cv2.imshow("dist", dist)
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cv2.waitKey()
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ret, sure_fg = cv2.threshold(dist, 0.2 * np.max(dist), 255, cv2.THRESH_BINARY)
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surface_fg = np.uint8(sure_fg) # 保持色彩空间一致才能进行运算，现在是背景空间为整型空间，前景为浮点型空间，所以进行转换
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if viz:
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cv2.imshow("surface_fg", surface_fg)
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cv2.waitKey()
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unknown = cv2.subtract(sure_bg, surface_fg)
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# 获取maskers,在markers中含有种子区域
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ret, markers = cv2.connectedComponents(surface_fg)
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# 分水岭变换
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markers = markers + 1
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markers[unknown == 255] = 0
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if viz:
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color_markers = np.uint8(markers)
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color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
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cv2.imshow("color_markers", color_markers)
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cv2.waitKey()
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markers = cv2.watershed(image, markers=markers)
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image[markers == -1] = [0, 0, 255]
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if viz:
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cv2.imshow("image", image)
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cv2.waitKey()
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for i in range(2, np.max(markers) + 1):
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np_contours = np.roll(np.array(np.where(markers == i)), 1, axis=0).transpose().reshape(-1, 2)
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# print(np_contours.shape)
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rectangle = cv2.minAreaRect(np_contours)
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box = cv2.boxPoints(rectangle)
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w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
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box_ratio = max(w, h) / (min(w, h) + 1e-5)
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if abs(1 - box_ratio) <= 0.1:
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l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
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t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
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box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
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# make clock-wise order
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startidx = box.sum(axis=1).argmin()
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box = np.roll(box, 4 - startidx, 0)
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box = np.array(box)
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boxes.append(box)
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return np.array(boxes)
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def getDetCharBoxes_core(textmap, text_threshold=0.5, low_text=0.4):
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# prepare data
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textmap = textmap.copy()
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img_h, img_w = textmap.shape
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""" labeling method """
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ret, text_score = cv2.threshold(textmap, low_text, 1, 0)
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nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score.astype(np.uint8),
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connectivity=4)
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det = []
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mapper = []
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for k in range(1, nLabels):
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# size filtering
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size = stats[k, cv2.CC_STAT_AREA]
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if size < 10: continue
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# thresholding
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if np.max(textmap[labels == k]) < text_threshold: continue
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# make segmentation map
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segmap = np.zeros(textmap.shape, dtype=np.uint8)
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segmap[labels == k] = 255
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# segmap[np.logical_and(link_score == 1, text_score == 0)] = 0 # remove link area
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x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
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w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
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niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
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sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
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# boundary check
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if sx < 0: sx = 0
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if sy < 0: sy = 0
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if ex >= img_w: ex = img_w
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if ey >= img_h: ey = img_h
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))
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segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
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# make box
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np_contours = np.roll(np.array(np.where(segmap != 0)), 1, axis=0).transpose().reshape(-1, 2)
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rectangle = cv2.minAreaRect(np_contours)
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box = cv2.boxPoints(rectangle)
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# align diamond-shape
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w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
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box_ratio = max(w, h) / (min(w, h) + 1e-5)
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if abs(1 - box_ratio) <= 0.1:
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l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
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t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
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box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
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# make clock-wise order
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startidx = box.sum(axis=1).argmin()
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box = np.roll(box, 4 - startidx, 0)
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box = np.array(box)
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det.append(box)
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mapper.append(k)
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return det, labels, mapper
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def watershed2(image, viz=False):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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gray = np.float32(gray) / 255.0
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boxes, _, _ = getDetCharBoxes_core(gray)
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return np.array(boxes)
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def watershed(oriimage, image, viz=False):
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# viz = True
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boxes = []
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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else:
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gray = image
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if viz:
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cv2.imshow("gray", gray)
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cv2.waitKey()
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ret, binary = cv2.threshold(gray, 0.2 * np.max(gray), 255, cv2.THRESH_BINARY)
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if viz:
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cv2.imshow("binary", binary)
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cv2.waitKey()
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# 形态学操作，进一步消除图像中噪点
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kernel = np.ones((3, 3), np.uint8)
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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mb = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2) # iterations连续两次开操作
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sure_bg = cv2.dilate(mb, kernel, iterations=3) # 3次膨胀,可以获取到大部分都是背景的区域
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sure_bg = mb
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if viz:
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cv2.imshow("sure_bg", mb)
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cv2.waitKey()
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# 距离变换
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# dist = cv2.distanceTransform(mb, cv2.DIST_L2, 5)
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# if viz:
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# cv2.imshow("dist", dist)
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# cv2.waitKey()
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ret, sure_fg = cv2.threshold(gray, 0.6 * gray.max(), 255, cv2.THRESH_BINARY)
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surface_fg = np.uint8(sure_fg) # 保持色彩空间一致才能进行运算，现在是背景空间为整型空间，前景为浮点型空间，所以进行转换
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if viz:
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cv2.imshow("surface_fg", surface_fg)
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cv2.waitKey()
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unknown = cv2.subtract(sure_bg, surface_fg)
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# 获取maskers,在markers中含有种子区域
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ret, markers = cv2.connectedComponents(surface_fg)
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nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(surface_fg,
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connectivity=4)
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# 分水岭变换
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markers = labels.copy() + 1
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# markers = markers+1
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markers[unknown == 255] = 0
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if viz:
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color_markers = np.uint8(markers)
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color_markers = color_markers / (color_markers.max() / 255)
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color_markers = np.uint8(color_markers)
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color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
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cv2.imshow("color_markers", color_markers)
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cv2.waitKey()
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# a = cv2.applyColorMap(gray, cv2.COLORMAP_JET)
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markers = cv2.watershed(oriimage, markers=markers)
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oriimage[markers == -1] = [0, 0, 255]
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if viz:
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color_markers = np.uint8(markers + 1)
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color_markers = color_markers / (color_markers.max() / 255)
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color_markers = np.uint8(color_markers)
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color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
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cv2.imshow("color_markers1", color_markers)
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cv2.waitKey()
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if viz:
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cv2.imshow("image", oriimage)
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cv2.waitKey()
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for i in range(2, np.max(markers) + 1):
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np_contours = np.roll(np.array(np.where(markers == i)), 1, axis=0).transpose().reshape(-1, 2)
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# segmap = np.zeros(gray.shape, dtype=np.uint8)
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# segmap[markers == i] = 255
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# size = np_contours.shape[0]
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# x, y, w, h = cv2.boundingRect(np_contours)
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# if w == 0 or h == 0:
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# continue
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#
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# niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
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# sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
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# # boundary check
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# if sx < 0: sx = 0
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# if sy < 0: sy = 0
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# if ex >= gray.shape[1]: ex = gray.shape[1]
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# if ey >= gray.shape[0]: ey = gray.shape[0]
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))
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# segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
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# np_contours = np.roll(np.array(np.where(segmap != 0)), 1, axis=0).transpose().reshape(-1, 2)
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rectangle = cv2.minAreaRect(np_contours)
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box = cv2.boxPoints(rectangle)
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startidx = box.sum(axis=1).argmin()
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box = np.roll(box, 4 - startidx, 0)
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poly = plg.Polygon(box)
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area = poly.area()
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if area < 10:
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continue
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box = np.array(box)
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boxes.append(box)
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return np.array(boxes)
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if __name__ == '__main__':
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image = cv2.imread('images/standard.jpg', cv2.IMREAD_COLOR)
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boxes = watershed(image, True)
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print(boxes)
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