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utils.py
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utils.py
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import cv2
import numpy as np
from tensorflow.keras.models import load_model
def setModel():
model = load_model('resources/digit-classifier.h5')
return model
def preProcess(img):
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
imgThreshold = cv2.adaptiveThreshold(imgBlur,255,1,1,11,2)
return imgThreshold
def reorder(points):
points = points.reshape((4,2))
newPoints = np.zeros((4,1,2),dtype=np.int32)
add = points.sum(1)
newPoints[0] = points[np.argmin(add)]
newPoints[3] = points[np.argmax(add)]
diff = np.diff(points,axis=1)
newPoints[1] = points[np.argmin(diff)]
newPoints[2] = points[np.argmax(diff)]
return newPoints
def largestContour(contours):
largest = np.array([])
max_area = 0
for i in contours:
area = cv2.contourArea(i)
if area > 50:
peri = cv2.arcLength(i,True)
approx = cv2.approxPolyDP(i,0.02*peri,True)
if area > max_area and len(approx) == 4:
largest = approx
max_area = area
return largest,max_area
def splitImg(img):
rows = np.vsplit(img,9)
boxes = []
for r in rows:
cols = np.hsplit(r,9)
for box in cols:
boxes.append(box)
return boxes
def getPredictions(boxes, model):
result = []
for box in boxes:
img = np.asarray(box)
img = img[4:img.shape[0]-4, 4:img.shape[1]-4]
img = cv2.resize(img, (28, 28))
img = img / 255
img = img.reshape(1, 28, 28, 1)
predictions = model.predict(img)
class_index = np.argmax(predictions[0])
probability_value = np.amax(predictions)
if probability_value > 0.8:
result.append(class_index)
else:
result.append(0)
return result
# a = setModel()
# a.describe()