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README.md

@@ -1,13 +1,21 @@
----
-title: Identification
-emoji: 💻
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.5.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Resume Photo Maker
+
+[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/guocheng66/resume-photo-maker)
+
+Make a resume photo with a simple python script and two lightweight deep neural networks.
+
+<img src="images/elon.jpg" width="200">
+<img src="assets/masked_resume_photo_0.jpg" width="200">
+
+## Set up and run
+```bash
+pip install -r requirements.txt
+
+python resume_photo_maker.py --image images/elon.jpg --background_color 255 255 255
+```
+There is a live demo on Hugging Face.[Try it now](https://huggingface.co/spaces/guocheng66/resume-photo-maker).
+
+## Acknowledgements
+https://github.com/ShiqiYu/libfacedetection
+
+https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.9/contrib/PP-HumanSeg

app.py

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+import gradio as gr
+from PIL import ImageColor
+
+import onnxruntime
+import cv2
+import numpy as np
+
+# The common resume photo size is 35mmx45mm
+RESUME_PHOTO_W = 350
+RESUME_PHOTO_H = 450
+
+
+# modified from https://github.com/opencv/opencv_zoo/blob/main/models/face_detection_yunet/yunet.py
+class YuNet:
+    def __init__(
+        self,
+        modelPath,
+        inputSize=[320, 320],
+        confThreshold=0.6,
+        nmsThreshold=0.3,
+        topK=5000,
+        backendId=0,
+        targetId=0,
+    ):
+        self._modelPath = modelPath
+        self._inputSize = tuple(inputSize)  # [w, h]
+        self._confThreshold = confThreshold
+        self._nmsThreshold = nmsThreshold
+        self._topK = topK
+        self._backendId = backendId
+        self._targetId = targetId
+
+        self._model = cv2.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId,
+        )
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model = cv2.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId,
+        )
+
+    def setInputSize(self, input_size):
+        self._model.setInputSize(tuple(input_size))
+
+    def infer(self, image):
+        # Forward
+        faces = self._model.detect(image)
+        return faces[1]
+
+
+class ONNXModel:
+    def __init__(self, model_path, input_w, input_h):
+        self.model = onnxruntime.InferenceSession(model_path)
+        self.input_w = input_w
+        self.input_h = input_h
+
+    def preprocess(self, rgb, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
+        # convert the input data into the float32 input
+        img_data = (
+            np.array(cv2.resize(rgb, (self.input_w, self.input_h)))
+            .transpose(2, 0, 1)
+            .astype("float32")
+        )
+
+        # normalize
+        norm_img_data = np.zeros(img_data.shape).astype("float32")
+
+        for i in range(img_data.shape[0]):
+            norm_img_data[i, :, :] = img_data[i, :, :] / 255
+            norm_img_data[i, :, :] = (norm_img_data[i, :, :] - mean[i]) / std[i]
+
+        # add batch channel
+        norm_img_data = norm_img_data.reshape(1, 3, self.input_h, self.input_w).astype(
+            "float32"
+        )
+        return norm_img_data
+
+    def forward(self, image):
+        input_data = self.preprocess(image)
+        output_data = self.model.run(["argmax_0.tmp_0"], {"x": input_data})
+
+        return output_data
+
+
+def make_resume_photo(rgb, background_color):
+    h, w, _ = rgb.shape
+    bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
+
+    # Initialize models
+    face_detector = YuNet("models/face_detection_yunet_2023mar.onnx")
+    face_detector.setInputSize([w, h])
+    human_segmentor = ONNXModel(
+        "models/human_pp_humansegv2_lite_192x192_inference_model.onnx", 192, 192
+    )
+
+    # yunet uses opencv bgr image format
+    detections = face_detector.infer(bgr)
+
+    results = []
+    for idx, det in enumerate(detections):
+        # bounding box
+        pt1 = np.array((det[0], det[1]))
+        pt2 = np.array((det[0] + det[2], det[1] + det[3]))
+
+        # face landmarks
+        landmarks = det[4:14].reshape((5, 2))
+        right_eye = landmarks[0]
+        left_eye = landmarks[1]
+
+        angle = np.arctan2(right_eye[1] - left_eye[1], (right_eye[0] - left_eye[0]))
+        rmat = cv2.getRotationMatrix2D((0, 0), -angle, 1)
+
+        # apply rotation
+        rotated_bgr = cv2.warpAffine(bgr, rmat, (bgr.shape[1], bgr.shape[0]))
+        rotated_pt1 = rmat[:, :-1] @ pt1
+        rotated_pt2 = rmat[:, :-1] @ pt2
+
+        face_w, face_h = rotated_pt2 - rotated_pt1
+        up_length = int(face_h / 4)
+        down_length = int(face_h / 3)
+        crop_h = face_h + up_length + down_length
+        crop_w = int(crop_h * (RESUME_PHOTO_W / RESUME_PHOTO_H))
+
+        pt1 = np.array(
+            (rotated_pt1[0] - (crop_w - face_w) / 2, rotated_pt1[1] - up_length)
+        ).astype(np.int32)
+        pt2 = np.array((pt1[0] + crop_w, pt1[1] + crop_h)).astype(np.int32)
+
+        resume_photo = rotated_bgr[pt1[1] : pt2[1], pt1[0] : pt2[0], :]
+
+        rgb = cv2.cvtColor(resume_photo, cv2.COLOR_BGR2RGB)
+        mask = human_segmentor.forward(rgb)
+        mask = mask[0].transpose(1, 2, 0)
+        mask = cv2.resize(
+            mask.astype(np.uint8), (resume_photo.shape[1], resume_photo.shape[0])
+        )
+
+        resume_photo = cv2.cvtColor(resume_photo, cv2.COLOR_BGR2RGB)
+        resume_photo[mask == 0] = ImageColor.getcolor(background_color, "RGB")
+        resume_photo = cv2.resize(resume_photo, (RESUME_PHOTO_W, RESUME_PHOTO_H))
+        results.append(resume_photo)
+
+    return results
+
+
+title = "AI证件照：任意照片生成证件照||公众号：正经人王同学"
+
+demo = gr.Interface(
+    fn=make_resume_photo,
+    inputs=[
+        gr.Image(type="numpy", label="input"),
+        gr.ColorPicker(label="设置背景颜色"),
+    ],
+    outputs=gr.Gallery(label="output"),
+    examples=[
+        ["images/queen.png", "#FFFFFF"],
+        ["images/elon.png", "#FFFFFF"],
+        ["images/openai.jpg", "#FFFFFF"],
+        ["images/sam.png", "#FFFFFF"],
+        
+    ],
+    title=title,
+    allow_flagging="never",
+    article="<p style='text-align: center;'><a href='https://mp.weixin.qq.com/s/SvQT5elBPuPYJ0CJ_LlBYA' target='_blank'>公众号：正经人王同学</a></p>",
+)
+
+if __name__ == "__main__":
+    demo.launch() 

dockerfile

@@ -0,0 +1,51 @@
+# # Use an official Python 3.10 runtime as a parent image
+# FROM python:3.10-slim
+
+# # Set the working directory in the container
+# WORKDIR /usr/src/app
+
+# # Copy the current directory contents into the container at /usr/src/app
+# COPY . .
+
+# # Install any needed packages specified in requirements.txt
+# RUN pip install --no-cache-dir -r requirements.txt
+
+# # Make port 7860 available to the world outside this container
+# EXPOSE 7860
+
+# # Run app.py when the container launches
+# CMD ["python","./app.py"]
+
+
+
+# Use an official Python 3.10 runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+
+
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+
+# RUN pip install -U pip
+# https://pypi.tuna.tsinghua.edu.cn/simple
+# RUN pip config set global.index-url http://mirrors.aliyun.com/pypi/simple
+# RUN pip config set install.trusted-host mirrors.aliyun.com
+RUN pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+RUN pip config set install.trusted-host pypi.tuna.tsinghua.edu.cn
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+RUN apt-get update && apt-get install -y libgl1-mesa-glx
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Run app.py when the container launches
+CMD ["python", "./app.py"]
+

maintest.py

@@ -0,0 +1,179 @@
+import onnxruntime
+import cv2
+import numpy as np
+import argparse
+
+# The common resume photo size is 35mmx45mm
+RESUME_PHOTO_W = 350
+RESUME_PHOTO_H = 450
+
+
+# modified from https://github.com/opencv/opencv_zoo/blob/main/models/face_detection_yunet/yunet.py
+class YuNet:
+    def __init__(
+        self,
+        modelPath,
+        inputSize=[320, 320],
+        confThreshold=0.6,
+        nmsThreshold=0.3,
+        topK=5000,
+        backendId=0,
+        targetId=0,
+    ):
+        self._modelPath = modelPath
+        self._inputSize = tuple(inputSize)  # [w, h]
+        self._confThreshold = confThreshold
+        self._nmsThreshold = nmsThreshold
+        self._topK = topK
+        self._backendId = backendId
+        self._targetId = targetId
+
+        self._model = cv2.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId,
+        )
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model = cv2.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId,
+        )
+
+    def setInputSize(self, input_size):
+        self._model.setInputSize(tuple(input_size))
+
+    def infer(self, image):
+        # Forward
+        faces = self._model.detect(image)
+        return faces[1]
+
+
+class ONNXModel:
+    def __init__(self, model_path, input_w, input_h):
+        self.model = onnxruntime.InferenceSession(model_path)
+        self.input_w = input_w
+        self.input_h = input_h
+
+    def preprocess(self, rgb, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
+        # convert the input data into the float32 input
+        img_data = (
+            np.array(cv2.resize(rgb, (self.input_w, self.input_h)))
+            .transpose(2, 0, 1)
+            .astype("float32")
+        )
+
+        # normalize
+        norm_img_data = np.zeros(img_data.shape).astype("float32")
+
+        for i in range(img_data.shape[0]):
+            norm_img_data[i, :, :] = img_data[i, :, :] / 255
+            norm_img_data[i, :, :] = (norm_img_data[i, :, :] - mean[i]) / std[i]
+
+        # add batch channel
+        norm_img_data = norm_img_data.reshape(1, 3, self.input_h, self.input_w).astype(
+            "float32"
+        )
+        return norm_img_data
+
+    def forward(self, image):
+        input_data = self.preprocess(image)
+        output_data = self.model.run(["argmax_0.tmp_0"], {"x": input_data})
+
+        return output_data
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Resume Photo Maker")
+    parser.add_argument(
+        "--background_color",
+        "-bg",
+        nargs="+",
+        type=int,
+        default=(255, 255, 255),
+        help="Set the background color RGB values.",
+    )
+    parser.add_argument(
+        "--image", "-i", type=str, default="images/elon.jpg", help="Input image path."
+    )
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    bgr = cv2.imread(args.image)
+    h, w, _ = bgr.shape
+
+    # Initialize models
+    face_detector = YuNet("models/face_detection_yunet_2023mar.onnx")
+    face_detector.setInputSize([w, h])
+    human_segmentor = ONNXModel(
+        "models/human_pp_humansegv2_lite_192x192_inference_model.onnx", 192, 192
+    )
+
+    # yunet uses opencv bgr image format
+    detections = face_detector.infer(bgr)
+
+    for idx, det in enumerate(detections):
+        # bounding box
+        pt1 = np.array((det[0], det[1]))
+        pt2 = np.array((det[0] + det[2], det[1] + det[3]))
+
+        # face landmarks
+        landmarks = det[4:14].reshape((5, 2))
+        right_eye = landmarks[0]
+        left_eye = landmarks[1]
+
+        angle = np.arctan2(right_eye[1] - left_eye[1], (right_eye[0] - left_eye[0]))
+        rmat = cv2.getRotationMatrix2D((0, 0), -angle, 1)
+
+        # apply rotation
+        rotated_bgr = cv2.warpAffine(bgr, rmat, (bgr.shape[1], bgr.shape[0]))
+        rotated_pt1 = rmat[:, :-1] @ pt1
+        rotated_pt2 = rmat[:, :-1] @ pt2
+
+        face_w, face_h = rotated_pt2 - rotated_pt1
+        up_length = int(face_h / 4)
+        down_length = int(face_h / 3)
+        crop_h = face_h + up_length + down_length
+        crop_w = int(crop_h * (RESUME_PHOTO_W / RESUME_PHOTO_H))
+
+        pt1 = np.array(
+            (rotated_pt1[0] - (crop_w - face_w) / 2, rotated_pt1[1] - up_length)
+        ).astype(np.int32)
+        pt2 = np.array((pt1[0] + crop_w, pt1[1] + crop_h)).astype(np.int32)
+
+        resume_photo = rotated_bgr[pt1[1] : pt2[1], pt1[0] : pt2[0], :]
+
+        rgb = cv2.cvtColor(resume_photo, cv2.COLOR_BGR2RGB)
+        mask = human_segmentor.forward(rgb)
+        mask = mask[0].transpose(1, 2, 0)
+        mask = cv2.resize(
+            mask.astype(np.uint8), (resume_photo.shape[1], resume_photo.shape[0])
+        )
+
+        resume_photo[mask == 0] = args.background_color
+
+        resume_photo = cv2.resize(resume_photo, (RESUME_PHOTO_W, RESUME_PHOTO_H))
+        cv2.imwrite(f"masked_resume_photo_{idx}.jpg", resume_photo)

requirements.txt

@@ -0,0 +1,11 @@
+coloredlogs==15.0.1
+flatbuffers==23.5.26
+humanfriendly==10.0
+mpmath==1.3.0
+numpy==1.26.1
+onnxruntime==1.16.1
+opencv-python==4.8.1.78
+packaging==23.2
+protobuf==4.25.0
+sympy==1.12
+gradio