Python cv2.CHAIN_APPROX_TC89_KCOS Examples
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code examples of cv2.CHAIN_APPROX_TC89_KCOS().
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Example #1
Source File: ChickenVision.py From ChickenVision with MIT License | 6 votes |
def findTargets(frame, mask): # Finds contours _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS) # Take each frame # Gets the shape of video screenHeight, screenWidth, _ = frame.shape # Gets center of height and width centerX = (screenWidth / 2) - .5 centerY = (screenHeight / 2) - .5 # Copies frame and stores it in image image = frame.copy() # Processes the contours, takes in (contours, output_image, (centerOfImage) if len(contours) != 0: image = findTape(contours, image, centerX, centerY) else: # pushes that it deosn't see vision target to network tables networkTable.putBoolean("tapeDetected", False) # Shows the contours overlayed on the original video return image # Finds the balls from the masked image and displays them on original stream + network tables
Example #2
Source File: ChickenVision.py From ChickenVision with MIT License | 6 votes |
def findCargo(frame, mask): # Finds contours _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS) # Take each frame # Gets the shape of video screenHeight, screenWidth, _ = frame.shape # Gets center of height and width centerX = (screenWidth / 2) - .5 centerY = (screenHeight / 2) - .5 # Copies frame and stores it in image image = frame.copy() # Processes the contours, takes in (contours, output_image, (centerOfImage) if len(contours) != 0: image = findBall(contours, image, centerX, centerY) else: # pushes that it doesn't see cargo to network tables networkTable.putBoolean("cargoDetected", False) # Shows the contours overlayed on the original video return image # Draws Contours and finds center and yaw of orange ball # centerX is center x coordinate of image # centerY is center y coordinate of image
Example #3
Source File: util.py From SpaceNet_Off_Nadir_Solutions with Apache License 2.0 | 5 votes |
def contours_hierarchy(mask): # first, find contours with cv2: it's much faster than shapely image, contours, hierarchy = cv2.findContours( ((mask == 1) * 255).astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # cv2.CHAIN_APPROX_SIMPLE,#orig cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS return contours, hierarchy
Example #4
Source File: binarized_filter_result.py From DVCNN_Lane_Detection with Apache License 2.0 | 5 votes |
def binarized_whatlike_filtered_image(self, img): """ Do normalization and thresholding on the result of weighted hat-like filter image to extract line candidate :param img: input image :return: list of roi pair (top_roi, fv_roi) class which defined in imdb.py """ if img is None: raise ValueError('Image data is invalid') # intensity normalizing the image and thresholding thre image image = img[:, :, 0] inds = np.where(image[:, :] > 650) norm_thresh_img = np.zeros(image.shape).astype(np.uint8) norm_thresh_img[inds] = 255 # find connected component image, contours, hierarchy = cv2.findContours(image=norm_thresh_img, mode=cv2.RETR_CCOMP, method=cv2.CHAIN_APPROX_TC89_KCOS) response_points = self.__find_response_points_in_contours(contours=contours, image=norm_thresh_img) # find rotate rect of each contour and check if it fits the condition, if fits the condition then save the # bounding rectangle of the contour result = [] valid_contours = 0 for index, contour in enumerate(contours): rotrect = cv2.minAreaRect(contour) if self.__is_rrect_valid(rotrect): # the contours is valid and can be saved roi_contours = contour roi_contours = np.reshape(roi_contours, newshape=(roi_contours.shape[0], roi_contours.shape[2])) roi_index = valid_contours valid_contours += 1 top_roi_db = imdb.Roidb(roi_index=roi_index, roi_contours=roi_contours, roi_response_points=response_points[index]) # type: fv_roi_db, roi_is_valid = self.__map_roi_to_front_view(roidb=top_roi_db) if roi_is_valid: result.append((top_roi_db, fv_roi_db)) return result, norm_thresh_img
Example #5
Source File: extract_candidate.py From DVCNN_Lane_Detection with Apache License 2.0 | 5 votes |
def __extract_line_from_filtered_image(img): """ Do normalization and thresholding on the result of weighted hat-like filter image to extract line candidate :param img:input image :return:rotate rect list [] """ image = img[:, :, 0] inds = np.where(image[:, :] > 300) norm_thresh_image = np.zeros(image.shape).astype(np.uint8) norm_thresh_image[inds] = 255 # find connected component image, contours, hierarchy = cv2.findContours(image=norm_thresh_image, mode=cv2.RETR_CCOMP, method=cv2.CHAIN_APPROX_TC89_KCOS) # find rotate rect of each contour and check if it fits the condition, if fits the condition then save the # bounding rectangle of the contour rotate_rect_list = [] bounding_rect_list = [] for i in range(len(contours)): contour = contours[i] rotrect = cv2.minAreaRect(contour) if RoiExtractor.__is_rrect_valid(rotrect): rotate_rect_list.append(rotrect) bnd_rect = cv2.boundingRect(contour) bounding_rect_list.append(bnd_rect) result = { 'rotate_rect_list': rotate_rect_list, 'bounding_rect_list': bounding_rect_list } return result
Example #6
Source File: TableRecognition.py From OTR with GNU General Public License v3.0 | 5 votes |
def __init__(self, img, **kwargs): contours, hierarchy = _find_contours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS, **kwargs) self.hierarchy = hierarchy self.contours = contours self.imgshape = img.shape
Example #7
Source File: TableRecognition.py From OTR with GNU General Public License v3.0 | 5 votes |
def compute_missing_cell_contours(self, missing_cells_mask): contx, _ = _find_contours(missing_cells_mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_TC89_KCOS) return contx
Example #8
Source File: main.py From DE3-ROB1-CHESS with Creative Commons Attribution 4.0 International | 4 votes |
def extractPiece(tile, margin=0.05): imgs = [tile] w, h, _ = tile.shape im_gray = cv2.cvtColor(tile, cv2.COLOR_BGR2GRAY) imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) # im_gray = im_gray[(h*margin):(h*(1-margin)), # (w*margin):(w*(1-margin))] # imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) # im_gray = cv2.equalizeHist(im_gray) im_gray = cv2.medianBlur(im_gray, 3) imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) bright = np.mean(im_gray) im_bw = im_gray im_bw[np.where(im_gray < bright)] = 0 im_bw[np.where(im_gray >= bright)] = 255 imgs.append(cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR)) if np.mean(im_bw) < 128: im_bw = 255 - im_bw imgs.append(cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR)) #_, im_bw = cv2.threshold(im_gray, 50, 250, cv2.THRESH_BINARY | cv2.THRESH_OTSU) #im_bw = cv2.Canny(im_bw, 0,255, apertureSize=5) contours,hierarchy = cv2.findContours(im_bw.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) hulls = [cv2.convexHull(c) for c in contours] ids = ignoreContours(im_bw, hulls, max_area_percentage=0.75, min_area_percentage=0.2) im_bw = cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR) tmp = im_bw.copy() for i in ids: c = np.squeeze(hulls[i], 1) drawContour(tmp, c, randomColor(), thickness=1) imgs.append(tmp) return imgs
Example #9
Source File: extractBoard.py From DE3-ROB1-CHESS with Creative Commons Attribution 4.0 International | 4 votes |
def extractBoards(img, w, h): """Extracts all boards from an image. This function applies perspective correction. :param img: source image :param w: output width :param h: output height :returns: a list the extracted board images """ im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ## Doc ## #writeDocumentationImage(im_gray, "gray") ## Doc ## (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) ## Doc ## #writeDocumentationImage(im_bw, "bw") ## Doc ## contours,hierarchy = cv2.findContours(im_bw, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) ## Doc ## #doc_im_contour = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) #for c in contours: # c = np.squeeze(c,1) # drawContour(doc_im_contour, c, randomColor()) #writeDocumentationImage(doc_im_contour, "contours") #doc_im_perspective = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) #doc_im_contour = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) ## Doc ## contour_ids = ignoreContours(im_bw, contours, hierarchy) boards = [] for i in contour_ids: c = contours[i] c = np.squeeze(c,1) ## Doc ## #color = randomColor() #drawContour(doc_im_contour, c, color) ## Doc ## perspective = getPerspective(img, c) if perspective is not None: b = extractPerspective(img, perspective, w, h) boards.append(b) ## Doc ## #drawPerspective(doc_im_perspective, perspective) ## Doc ## ## Doc ## #writeDocumentationImage(boards[-1], "extracted") #writeDocumentationImage(doc_im_contour, "contours_filtered") #writeDocumentationImage(doc_im_perspective, "perspective") ## Doc ## return boards
Example #10
Source File: main.py From DE3-ROB1-CHESS with Creative Commons Attribution 4.0 International | 4 votes |
def extractPiece(tile, margin=0.05): imgs = [tile] w, h, _ = tile.shape im_gray = cv2.cvtColor(tile, cv2.COLOR_BGR2GRAY) imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) # im_gray = im_gray[(h*margin):(h*(1-margin)), # (w*margin):(w*(1-margin))] # imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) # im_gray = cv2.equalizeHist(im_gray) im_gray = cv2.medianBlur(im_gray, 3) imgs.append(cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR)) bright = np.mean(im_gray) im_bw = im_gray im_bw[np.where(im_gray < bright)] = 0 im_bw[np.where(im_gray >= bright)] = 255 imgs.append(cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR)) if np.mean(im_bw) < 128: im_bw = 255 - im_bw imgs.append(cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR)) #_, im_bw = cv2.threshold(im_gray, 50, 250, cv2.THRESH_BINARY | cv2.THRESH_OTSU) #im_bw = cv2.Canny(im_bw, 0,255, apertureSize=5) contours,hierarchy = cv2.findContours(im_bw.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) hulls = [cv2.convexHull(c) for c in contours] ids = ignoreContours(im_bw, hulls, max_area_percentage=0.75, min_area_percentage=0.2) im_bw = cv2.cvtColor(im_bw, cv2.COLOR_GRAY2BGR) tmp = im_bw.copy() for i in ids: c = np.squeeze(hulls[i], 1) drawContour(tmp, c, randomColor(), thickness=1) imgs.append(tmp) return imgs
Example #11
Source File: extract.py From DE3-ROB1-CHESS with Creative Commons Attribution 4.0 International | 4 votes |
def extractBoards(img, w, h): """Extracts all boards from an image. This function applies perspective correction. :param img: source image :param w: output width :param h: output height :returns: a list the extracted board images """ im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ## Doc ## #writeDocumentationImage(im_gray, "gray") ## Doc ## (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) ## Doc ## #writeDocumentationImage(im_bw, "bw") ## Doc ## contours,hierarchy = cv2.findContours(im_bw, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) ## Doc ## #doc_im_contour = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) #for c in contours: # c = np.squeeze(c,1) # drawContour(doc_im_contour, c, randomColor()) #writeDocumentationImage(doc_im_contour, "contours") #doc_im_perspective = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) #doc_im_contour = cv2.cvtColor(im_gray, cv2.COLOR_GRAY2BGR) ## Doc ## contour_ids = ignoreContours(im_bw, contours, hierarchy) boards = [] for i in contour_ids: c = contours[i] c = np.squeeze(c,1) ## Doc ## #color = randomColor() #drawContour(doc_im_contour, c, color) ## Doc ## perspective = getPerspective(img, c) if perspective is not None: b = extractPerspective(img, perspective, w, h) boards.append(b) ## Doc ## #drawPerspective(doc_im_perspective, perspective) ## Doc ## ## Doc ## #writeDocumentationImage(boards[-1], "extracted") #writeDocumentationImage(doc_im_contour, "contours_filtered") #writeDocumentationImage(doc_im_perspective, "perspective") ## Doc ## return boards