def preprocess(self, img):
"""
Gives the final preprocessed version of image.
:param img: Input Image
:return: img, grayscale, canny and the final binary_img
"""
img, gray, cleaned, thresh = self.convert_and_threshold_line(img)
canny, detected_lines = self.getDetectedLinesCanny(cleaned)
if not self.anyDetectedLines(detected_lines):
thresh_final = self.process_img_without_lines(255 - gray, canny)
else:
thresh_final = self.remove_lines_final(thresh, detected_lines)
if self.debug:
imutils.showImage(thresh_final, "Final Processed Image.")
return img, gray, canny, thresh_final
def convert_and_threshold_line(self, img_):
"""
Takes Image, resizes it, converts to grayscale, cleans with divide tricks and thresholds it with Otsu's Threshold.
:param img_: Unprepared Image
:return: resized image, grayscale, cleaned image with divide trick, thresholded image.
"""
img_ = cv2.resize(img_, (self.width, self.height),
interpolation=cv2.INTER_NEAREST)
gray = cv2.cvtColor(img_, cv2.COLOR_BGR2GRAY)
cleaned = cv2.GaussianBlur(gray, (51, 51), 0)
cleaned = cv2.divide(gray, cleaned, scale=255)
if self.debug:
imutils.showImage(cleaned, "After Divide Trick")
thresh = cv2.threshold(cleaned, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
if self.debug:
imutils.showImage(thresh, "After Otsu's Thresholding on Divided")
return img_, gray, cleaned, thresh
def remove_lines_final(self, thresh, detected_lines):
"""
Remove given lines from the given thresholded image, clean and repair.
:param thresh: The thresholded image containing numbers and lines.
:param detected_lines: Binary image containing only lines.
:return: Thresholded Binary Image after removing lines, cleaning and repairing.
"""
thresh[detected_lines > 0] = 0
if self.debug:
imutils.showImage(thresh, "Remove Lines")
thresh = cv2.medianBlur(thresh, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 5))
thresh = cv2.morphologyEx(thresh,
cv2.MORPH_CLOSE,
kernel,
iterations=3)
if self.debug:
imutils.showImage(thresh, "Final Image after Removing Lines")
return thresh
def getDetectedLinesCanny(self, cleaned):
"""
Takes image (cleaned after divide trick) and detects lines using Canny, contours and then morphing open.
:param cleaned: cleaned input image
:return: image after applying canny and binary detected lines.
"""
v = np.median(cleaned)
sigma = 0.33
lower_thresh = int(max(0, (1.0 - sigma) * v))
upper_thresh = int(min(255, (1.0 + sigma) * v))
canny = cv2.Canny(cleaned, lower_thresh, upper_thresh)
cnts, _ = cv2.findContours(canny, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if self.debug:
imutils.showImage(canny, "Canny")
kern_width = 20
new = np.zeros_like(canny)
cv2.drawContours(new, cnts, -1, 255, 2)
new = cv2.erode(new, (6, 2), iterations=2)
if self.debug:
imutils.showImage(new, "Contours on Canny")
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(kern_width, 1))
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(1, kern_width * 4))
detected_lines = cv2.morphologyEx(
new, cv2.MORPH_OPEN, horizontal_kernel,
iterations=2) + cv2.morphologyEx(
new, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
if self.debug:
imutils.showImage(detected_lines, "Detected on Canny Contours(R)")
return canny, detected_lines
roi = np.expand_dims(roi, axis=0)
notSmiling, smiling = model.predict(roi)[0]
label = "Smiling" if smiling > notSmiling else "Not Smiling"
textpos = (fX, fY - 10)
if fY < 30:
if fY + fH < height - 40:
textpos = (fX, fY + fH + 20)
elif fX > 50:
textpos = (0, fY)
else:
textpos = (fX + fW + 10, fY)
cv2.putText(img_clone, f"{label}: {max(notSmiling, smiling) * 100}%",
textpos, cv2.FONT_HERSHEY_SIMPLEX, 0.45, (20, 255, 15), 1)
cv2.rectangle(img_clone, (fX, fY), (fX + fW, fY + fH), (20, 255, 15),
2)
imutils.showImage(img_clone, "Prediction")
else:
if args["video"]:
cap = cv2.VideoCapture(args["video"])
else:
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if args["video"] and not ret:
break
img = frame
height, width = img.shape[:2]
if width > 750 or width < 150:
img = imutils.resize(img, width=600)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)