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First National Division VV stats & predictions

Overview of Tomorrow's Football Matches in Belgium's First National Division VV

Tomorrow promises to be an exhilarating day for football fans in Belgium as the First National Division VV hosts a series of compelling matches. With teams battling it out on the field, each game is packed with potential upsets, tactical showdowns, and thrilling performances. This article delves into the detailed schedule of tomorrow's fixtures, expert betting predictions, and what to expect from each encounter.

Belgium

First National Division VV

Schedule of Matches

  • Match 1: Team A vs. Team B - Kick-off at 14:00
  • Match 2: Team C vs. Team D - Kick-off at 16:00
  • Match 3: Team E vs. Team F - Kick-off at 18:30
  • Match 4: Team G vs. Team H - Kick-off at 20:45

Expert Betting Predictions

Betting enthusiasts are eagerly analyzing statistics and player performances to make informed predictions for tomorrow's matches. Here are some expert insights and tips for each game:

Team A vs. Team B

This clash is anticipated to be a closely contested affair, with both teams having a strong home record this season. However, Team A's recent form gives them a slight edge.

  • Prediction: Draw or narrow win for Team A
  • Betting Tip: Over 2.5 goals - Both teams have a penchant for attacking play.

Team C vs. Team D

Team C has been in excellent form, securing consecutive wins in their last five matches. Meanwhile, Team D is struggling with injuries but has a strong defensive record.

  • Prediction: Win for Team C
  • Betting Tip: Under 2.5 goals - Expect a tight defensive battle.

Team E vs. Team F

This fixture is one of the most anticipated matches of the day, with both teams fighting for a spot in the top half of the table. Team E's attacking prowess could be decisive.

  • Prediction: Win for Team E
  • Betting Tip: Both teams to score - High-scoring potential given their offensive capabilities.

Team G vs. Team H

A classic derby match that never fails to deliver drama and excitement. Both teams have key players returning from injury, adding an extra layer of intrigue.

  • Prediction: Draw
  • Betting Tip: Correct score draw (1-1) - Historically close encounters in this fixture.

Detailed Match Analysis

Team A vs. Team B: Tactical Battle

Team A has been dominating possession in recent games, relying on their midfield maestros to control the tempo. Their high pressing game could unsettle Team B's defense.

Key Players to Watch
  • Lionel Starz (Team A): Known for his vision and passing accuracy, Starz could be instrumental in unlocking defenses.
  • Marcus Shield (Team B): A formidable defender who will be crucial in neutralizing Team A's attacking threats.
Possible Line-ups
**Team A**
Goalkeeper: James Keeper
[0]: import numpy as np [1]: import cv2 [2]: import os [3]: def remove_noise(img): [4]: kernel = np.ones((5,5),np.uint8) [5]: erosion = cv2.erode(img,kernel,iterations =1) [6]: dilation = cv2.dilate(erosion,kernel,iterations =1) [7]: return dilation [8]: def remove_shadows(img): [9]: rgb_planes = cv2.split(img) [10]: result_planes = [] [11]: result_norm_planes = [] [12]: for plane in rgb_planes: [13]: dilated_img = cv2.dilate(plane,cv2.getStructuringElement(cv2.MORPH_RECT,(7,7))) [14]: bg_img = cv2.medianBlur(dilated_img,21) [15]: diff_img = 255 - cv2.absdiff(plane,bg_img) [16]: norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_8UC1) [17]: result_planes.append(diff_img) [18]: result_norm_planes.append(norm_img) [19]: result = cv2.merge(result_planes) [20]: result_norm = cv2.merge(result_norm_planes) [21]: return result_norm [22]: def find_contours(img): [23]: #finding contours [24]: contours,hierarchy=cv2.findContours(img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) [25]: return contours [26]: def get_perspective_transform(contours): [27]: #sorting contours according to area [28]: sorted_contours=sorted(contours,key=cv2.contourArea,reverse=True) [29]: #getting index of contour with max area [30]: paper_cnt=sorted_contours.pop(0) def preprocess(path): [1]: img=cv2.imread(path) #code by Raghavendra S def order_points(pts): # initialzie a list of coordinates that will be ordered # such that the first entry in the list is the top-left, # the second entry is the top-right, the third is the # bottom-right, and the fourth is the bottom-left rect = np.zeros((4, 2), dtype="float32") # the top-left point will have the smallest sum, # whereas the bottom-right point will have the largest sum s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # now, compute the difference between the points, the # top-right point will have the smallest difference, # whereas the bottom-left will have the largest difference diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] # return the ordered coordinates return rect def four_point_transform(image, pts): # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br)[0] ** 2) + ((tr[1] - br)[1] ** 2)) heightB = np.sqrt(((tl)[0] - (bl)[0]) ** 2 + ((tl)[1] - (bl)[1]) ** 2) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order dst = np.array([ [0, maxHeight], [maxWidth , maxHeight], [maxWidth , maxHeight], [0 , maxHeight]], dtype="float32") cv2.fillConvexPoly(image,[rect.astype("int")],(255)) cv2.fillConvexPoly(image,[dst.astype("int")],(255)) src_pts=np.array([tl,tr,br ,bl],dtype="float32") dst_pts=np.array([[0,maxHeight],[maxWidth,maxHeight],[maxWidth ,0],[0 ,0]],dtype="float32") M=cv2.getPerspectiveTransform(src_pts,dst_pts) warped=cv2.warpPerspective(image,M,(maxWidth,maxHeight)) warped=warped.astype("uint8") return warped def segment_characters(img): #converting to gray scale image for thresholding. img_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #applying thresholding on gray scale image. ret,img_thresh=cv2.threshold(img_gray ,170 ,255,cv.THRESH_BINARY_INV+cv.THRESH_OTSU) #applying dilation on thresholded image. kernel=np.ones((5 ,5),np.uint8) img_dilation=cv2.dilate(img_thresh,kernel ,iterations=1) #contour detection using RETR_EXTERNAL means that only outer details/corners are detected. im_contours,_=cv.findContours(img_dilation,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE) #sorting detected contours based on area. im_contours=sorted(im_contours,key=cv.contourArea ,reverse=True)[:30] #initializing index for charaters segmented. idx=0 #looping over sorted contours. for c in im_contours: img_thresh_cropped=img_thresh[y:y+h,x:x+w] img_thresh_cropped=img_thresh_cropped.astype(np.uint8) img_thresholded=cv.copyMakeBorder(img_thresh_cropped,top=10,bottom=10,left=10,right=10,borderType=cv.BORDER_CONSTANT,value=[255]) if len(np.unique(img_thresholded))<=3: img_thresholded=np.where(img_thresholded==255,np.ones(shape=img_thresholded.shape)*255,np.zeros(shape=img_thresholded.shape)*255).astype(np.uint8) kernel=np.ones((3 ,3),np.uint8) img_thresholded=cv.morphologyEx(img_thresholded,cv.MORPH_OPEN,kernel) kernel=np.ones((8 ,8),np.uint8) img_thresholded=cv.morphologyEx(img_thresholded,cv.MORPH_CLOSE,kernel) img_thresholded=img_thresholded.astype(np.uint8) img_name="character_"+str(idx)+".jpg" cv.imwrite(os.path.join("characters",img_name),img_thresholded) <|repo_name|>tusharsaini1999/DocumentScanner<|file_sep|>/README.md Document Scanner using OpenCV-Python. This project contains a python script which takes an input image containing document/sheet/plane from scanner/phone camera etc and converts it into scanned document by removing background noise & shadows etc. The process used here is Perspective Transformation also known as Homography. Requirements: Python (version >=3.x.x), OpenCV-Python (version >=3.x.x), NumPy (version >=1.x.x). Instructions: Just run "python doc_scanner.py" from terminal or command prompt while working directory is current directory containing doc_scanner.py. Enter path to input image when asked by script. Wait till process completes and check output folder containing scanned image. Note: You can use any input image containing document/sheet/plane from scanner/phone camera etc. <|repo_name|>tomaszrzeszot/magento<|file_sep|>/src/Magento/Setup/Test/Unit/Module/Di/Code/MetadataBuilderTest.php