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restore original size and move coordinates accordingly

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Anthr@X 2023-07-09 01:05:17 +10:00
parent 34d1f1250a
commit b52d1e8015
1 changed files with 12 additions and 0 deletions
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12
deepface/detectors/YunetWrapper.py
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@ -30,10 +30,13 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
height, width = image.shape[0], image.shape[1] height, width = image.shape[0], image.shape[1]
# resize image if it is too large (Yunet fails to detect faces on large input sometimes) # resize image if it is too large (Yunet fails to detect faces on large input sometimes)
# I picked 640 as a threshold because it is the default value of max_size in Yunet. # I picked 640 as a threshold because it is the default value of max_size in Yunet.
resized = False
if height > 640 or width > 640: if height > 640 or width > 640:
r = 640.0 / max(height, width) r = 640.0 / max(height, width)
original_image = image.copy()
image = cv2.resize(image, (int(width * r), int(height * r))) image = cv2.resize(image, (int(width * r), int(height * r)))
height, width = image.shape[0], image.shape[1] height, width = image.shape[0], image.shape[1]
resized = True
detector.setInputSize((width, height)) detector.setInputSize((width, height))
detector.setScoreThreshold(score_threshold) detector.setScoreThreshold(score_threshold)
_, faces = detector.detect(image) _, faces = detector.detect(image)
@ -49,6 +52,15 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
{x, y}_{re, le, nt, rcm, lcm} stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively. {x, y}_{re, le, nt, rcm, lcm} stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively.
""" """
(x, y, w, h, x_re, y_re, x_le, y_le) = list(map(int, face[:8])) (x, y, w, h, x_re, y_re, x_le, y_le) = list(map(int, face[:8]))
if resized:
image = original_image
x, y, w, h = int(x / r), int(y / r), int(w / r), int(h / r)
x_re, y_re, x_le, y_le = (
int(x_re / r),
int(y_re / r),
int(x_le / r),
int(y_le / r),
)
confidence = face[-1] confidence = face[-1]
confidence = "{:.2f}".format(confidence) confidence = "{:.2f}".format(confidence)
detected_face = image[int(y) : int(y + h), int(x) : int(x + w)] detected_face = image[int(y) : int(y + h), int(x) : int(x + w)]