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GradePredictor/ImgRec.py
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import cv2 | |
import numpy as np | |
def HoldLister(count, imgLink): | |
holds = [] | |
for i in range(0,54): | |
imgLink, count, img = ImageLinkSorter(count, imgLink) | |
holds.append(cv2.imread(img, cv2.IMREAD_GRAYSCALE)) | |
return holds | |
def ImageLinkSorter(count, imgLink): | |
imgLink = list(imgLink) | |
imgLink = NumRemover(imgLink) | |
count += 1 | |
imgLink.insert(23, str(count)) | |
img = "" | |
for i in imgLink: | |
img += i | |
return imgLink, count, img | |
def NumRemover(text): | |
for i in text: | |
if i.isdigit(): | |
text.remove(i) | |
for i in text: | |
if i.isdigit(): | |
NumChecker(text) | |
return(text) | |
def HoldFinder(climbImg, holds, count, imgLink): | |
validHolds = [] | |
for hold in holds: | |
result = cv2.matchTemplate(climbImg, hold, cv2.TM_CCOEFF_NORMED) | |
threshold = 0.9 | |
locations = np.where(result >= threshold) | |
locations = list(zip(*locations[::-1])) | |
if locations != []: | |
imgLink, count, img = ImageLinkSorter(count, imgLink) | |
validHolds.append(img) | |
else: | |
count += 1 | |
for location in locations: | |
top_left = location | |
bottom_right = (top_left[0] + hold.shape[1], top_left[1] + hold.shape[0]) | |
cv2.rectangle(climbImg, top_left, bottom_right, (0, 255, 0), 2) | |
return validHolds | |
def ImgRecMain(picOfClimb): | |
climbImg = cv2.imread(picOfClimb, cv2.IMREAD_GRAYSCALE) | |
holds = HoldLister(0, r"D:\Disso\HoldPics\Hold_0.jpeg") | |
validHolds = HoldFinder(climbImg, holds, 0, r"D:\Disso\HoldPics\Hold_0.jpeg")#May need to change file path for program to function properly. | |
#Unhash these lines to display the image with the holds identified to the screen | |
#cv2.namedWindow("Climbing wall with detected holds", cv2.WINDOW_NORMAL) | |
#cv2.imshow("Climbing wall with detected holds", climbImg) | |
#cv2.waitKey(0) | |
#cv2.destroyAllWindows() | |
return validHolds | |
#ImgRecMain(r"D:\Disso\ClimbPics\Climb_26.jpeg") | |
#ImgRecMain(r"D:\Disso\ClimbPics\Climb_22.jpeg") |