mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 11:35:21 +00:00
some improvements
- loading image from url was loading as rgb instead of bar - we are returning image name in load image function to throw meaningful message for no face found case - we started to use imread again, and throw exception if file name contains non English character - get rid of the duplicate if statement in extract faces
This commit is contained in:
Sefik Ilkin Serengil
parent
76a6bce824
commit
2cc5f39853
@ -16,6 +16,8 @@ from deepface.commons.logger import Logger
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logger = Logger(module="commons.functions")
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# pylint: disable=no-else-raise
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# --------------------------------------------------
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# configurations of dependencies
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@ -73,49 +75,52 @@ def loadBase64Img(uri):
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"""
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encoded_data = uri.split(",")[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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img_bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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# img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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return img_bgr
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def load_image(img):
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"""Load image from path, url, base64 or numpy array.
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"""
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Load image from path, url, base64 or numpy array.
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Args:
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img: a path, url, base64 or numpy array.
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Raises:
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ValueError: if the image path does not exist.
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Returns:
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numpy array: the loaded image.
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image (numpy array): the loaded image in BGR format
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image name (str): image name itself
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"""
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# The image is already a numpy array
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if type(img).__module__ == np.__name__:
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return img
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return img, None
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# The image is a base64 string
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if img.startswith("data:image/"):
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return loadBase64Img(img)
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return loadBase64Img(img), None
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# The image is a url
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if img.startswith("http"):
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return np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))[
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:, :, ::-1
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]
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return (
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np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("BGR"))[
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:, :, ::-1
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],
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# return url as image name
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img,
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)
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# The image is a path
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if os.path.isfile(img) is not True:
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raise ValueError(f"Confirm that {img} exists")
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# For reading images with unicode names
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with open(img, "rb") as img_f:
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chunk = img_f.read()
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chunk_arr = np.frombuffer(chunk, dtype=np.uint8)
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img = cv2.imdecode(chunk_arr, cv2.IMREAD_COLOR)
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return img
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# image must be a file on the system then
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# This causes troubles when reading files with non english names
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# return cv2.imread(img)
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# image name must have english characters
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if img.isascii() is False:
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raise ValueError(f"Input image must not have non-english characters - {img}")
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img_obj_bgr = cv2.imread(img)
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# img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
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return img_obj_bgr, img
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# --------------------------------------------------
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@ -152,7 +157,7 @@ def extract_faces(
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extracted_faces = []
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img = load_image(img)
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img, img_name = load_image(img)
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img_region = [0, 0, img.shape[1], img.shape[0]]
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if detector_backend == "skip":
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@ -163,10 +168,17 @@ def extract_faces(
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# in case of no face found
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if len(face_objs) == 0 and enforce_detection is True:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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+ "or consider to set enforce_detection param to False."
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)
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if img_name is not None:
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raise ValueError(
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f"Face could not be detected in {img_name}."
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"Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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if len(face_objs) == 0 and enforce_detection is False:
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face_objs = [(img, img_region, 0)]
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@ -177,39 +189,38 @@ def extract_faces(
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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# resize and padding
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if current_img.shape[0] > 0 and current_img.shape[1] > 0:
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factor_0 = target_size[0] / current_img.shape[0]
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factor_1 = target_size[1] / current_img.shape[1]
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factor = min(factor_0, factor_1)
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factor_0 = target_size[0] / current_img.shape[0]
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factor_1 = target_size[1] / current_img.shape[1]
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factor = min(factor_0, factor_1)
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dsize = (
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int(current_img.shape[1] * factor),
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int(current_img.shape[0] * factor),
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dsize = (
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int(current_img.shape[1] * factor),
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int(current_img.shape[0] * factor),
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)
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current_img = cv2.resize(current_img, dsize)
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diff_0 = target_size[0] - current_img.shape[0]
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diff_1 = target_size[1] - current_img.shape[1]
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if grayscale is False:
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# Put the base image in the middle of the padded image
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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(0, 0),
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),
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"constant",
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)
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else:
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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),
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"constant",
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)
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current_img = cv2.resize(current_img, dsize)
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diff_0 = target_size[0] - current_img.shape[0]
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diff_1 = target_size[1] - current_img.shape[1]
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if grayscale is False:
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# Put the base image in the middle of the padded image
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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(0, 0),
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),
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"constant",
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)
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else:
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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),
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"constant",
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)
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# double check: if target image is not still the same size with target.
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if current_img.shape[0:2] != target_size:
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