Update APP by ID photo DIY (#5707)

Author: wuyefeilin

modelcenter/PP-Matting/APP1/app.py

@@ -1,182 +1,78 @@
-import codecs
-import os
-import sys
-import time
-import zipfile
-
 import gradio as gr
 import numpy as np
-import cv2
-import requests
-import yaml
-from paddle.inference import Config as PredictConfig
-from paddle.inference import create_predictor
-
-lasttime = time.time()
-FLUSH_INTERVAL = 0.1
-
-
-def progress(str, end=False):
-    global lasttime
-    if end:
-        str += "\n"
-        lasttime = 0
-    if time.time() - lasttime >= FLUSH_INTERVAL:
-        sys.stdout.write("\r%s" % str)
-        lasttime = time.time()
-        sys.stdout.flush()
-
-
-def _download_file(url, savepath, print_progress=True):
-    if print_progress:
-        print("Connecting to {}".format(url))
-    r = requests.get(url, stream=True, timeout=15)
-    total_length = r.headers.get('content-length')
-
-    if total_length is None:
-        with open(savepath, 'wb') as f:
-            shutil.copyfileobj(r.raw, f)
-    else:
-        with open(savepath, 'wb') as f:
-            dl = 0
-            total_length = int(total_length)
-            starttime = time.time()
-            if print_progress:
-                print("Downloading %s" % os.path.basename(savepath))
-            for data in r.iter_content(chunk_size=4096):
-                dl += len(data)
-                f.write(data)
-                if print_progress:
-                    done = int(50 * dl / total_length)
-                    progress("[%-50s] %.2f%%" %
-                             ('=' * done, float(100 * dl) / total_length))
-        if print_progress:
-            progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
-
-
-def uncompress(path):
-    files = zipfile.ZipFile(path, 'r')
-    filelist = files.namelist()
-    rootpath = filelist[0]
-    for file in filelist:
-        files.extract(file, './')
-
-
-class DeployConfig:
-    def __init__(self, path):
-        with codecs.open(path, 'r', 'utf-8') as file:
-            self.dic = yaml.load(file, Loader=yaml.FullLoader)
-        self._dir = os.path.dirname(path)
-
-    @property
-    def model(self):
-        return os.path.join(self._dir, self.dic['Deploy']['model'])
-
-    @property
-    def params(self):
-        return os.path.join(self._dir, self.dic['Deploy']['params'])
-
-
-class Predictor:
-    def __init__(self, cfg):
-        """
-        Prepare for prediction.
-        The usage and docs of paddle inference, please refer to
-        https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
-        """
-        self.cfg = DeployConfig(cfg)
-
-        self._init_base_config()
-
-        self._init_cpu_config()
-
-        self.predictor = create_predictor(self.pred_cfg)
-
-    def _init_base_config(self):
-        self.pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)
-        self.pred_cfg.enable_memory_optim()
-        self.pred_cfg.switch_ir_optim(True)
-
-    def _init_cpu_config(self):
-        """
-        Init the config for x86 cpu.
-        """
-        self.pred_cfg.disable_gpu()
-        self.pred_cfg.set_cpu_math_library_num_threads(10)
-
-    def _preprocess(self, img):
-        # resize short edge to 512.
-        h, w = img.shape[:2]
-        short_edge = min(h, w)
-        scale = 512 / short_edge
-        h_resize = int(round(h * scale)) // 32 * 32
-        w_resize = int(round(w * scale)) // 32 * 32
-        img = cv2.resize(img, (w_resize, h_resize))
-        img = (img / 255 - 0.5) / 0.5
-        img = np.transpose(img, [2, 0, 1])[np.newaxis, :]
-        return img
-
-    def run(self, img):
-        input_names = self.predictor.get_input_names()
-        input_handle = {}
-
-        for i in range(len(input_names)):
-            input_handle[input_names[i]] = self.predictor.get_input_handle(
-                input_names[i])
-        output_names = self.predictor.get_output_names()
-        output_handle = self.predictor.get_output_handle(output_names[0])
-
-        img_inputs = img.astype('float32')
-        ori_h, ori_w = img_inputs.shape[:2]
-        img_inputs = self._preprocess(img=img_inputs)
-        input_handle['img'].copy_from_cpu(img_inputs)
-
-        self.predictor.run()
-
-        results = output_handle.copy_to_cpu()
-        alpha = results.squeeze()
-        alpha = cv2.resize(alpha, (ori_w, ori_h))
-        alpha = (alpha * 255).astype('uint8')
-
-        return alpha
-
-
-def model_inference(image):
-    # Download inference model
-    url = 'https://paddleseg.bj.bcebos.com/matting/models/deploy/ppmatting-hrnet_w18-human_512.zip'
-    savepath = './ppmatting-hrnet_w18-human_512.zip'
-    if not os.path.exists('./ppmatting-hrnet_w18-human_512'):
-        _download_file(url=url, savepath=savepath)
-        uncompress(savepath)
 
-    # Inference
-    predictor = Predictor(cfg='./ppmatting-hrnet_w18-human_512/deploy.yaml')
-    alpha = predictor.run(image)
+import utils
+from predict import build_predictor
 
-    return alpha
+IMAGE_DEMO = "./images/idphoto.jpg"
+predictor = build_predictor()
+sizes_play = utils.size_play()
 
 
-def clear_all():
-    return None, None
+def get_output(img, size, bg, download_size):
+    """
+    Get the special size and background photo.
 
+    Args:
+        img(numpy:ndarray): The image array.
+        size(str): The size user specified.
+        bg(str): The background color user specified.
+        download_size(str): The size for image saving.
 
-with gr.Blocks() as demo:
-    gr.Markdown("Objective Detection")
+    """
+    alpha = predictor.run(img)
+    res = utils.bg_replace(img, alpha, bg_name=bg)
 
-    with gr.Column(scale=1, min_width=100):
+    size_index = sizes_play.index(size)
+    res = utils.adjust_size(res, size_index)
+    res_download = utils.download(res, download_size)
+    return res, res_download
 
-        img_in = gr.Image(
-            value="https://paddleseg.bj.bcebos.com/matting/demo/human.jpg",
-            label="Input")
 
-        with gr.Row():
-            btn1 = gr.Button("Clear")
-            btn2 = gr.Button("Submit")
+def download(img, size):
+    utils.download(img, size)
+    return None
 
-        img_out = gr.Image(label="Output").style(height=200)
 
-    btn2.click(fn=model_inference, inputs=img_in, outputs=[img_out])
-    btn1.click(fn=clear_all, inputs=None, outputs=[img_in, img_out])
+with gr.Blocks() as demo:
+    gr.Markdown("""# ID Photo DIY""")
+
+    img_in = gr.Image(value=IMAGE_DEMO, label="Input image")
+    gr.Markdown(
+        """<font color=Gray>Tips: Please upload photos with good posture, center portrait, crown free, no jewelry, ears and eyebrows exposed.</font>"""
+    )
+    with gr.Row():
+        size = gr.Dropdown(sizes_play, label="Sizes", value=sizes_play[0])
+        bg = gr.Radio(
+            ["White", "Red", "Blue"], label="Background color", value='White')
+        download_size = gr.Radio(
+            ["Small", "Middle", "Large"],
+            label="File size (affects image quality)",
+            value='Large',
+            interactive=True)
+
+    with gr.Row():
+        btn1 = gr.Button("Clear")
+        btn2 = gr.Button("Submit")
+
+    img_out = gr.Image(
+        label="Output image", interactive=False).style(height=300)
+    f1 = gr.File(label='Image download').style(height=50)
+    with gr.Row():
+        gr.Markdown(
+            """<font color=Gray>This application is supported by [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg). 
+            If you have any questions or feature requists, welcome to raise issues on [GitHub](https://github.com/PaddlePaddle/PaddleSeg/issues). BTW, a star is a great encouragement for us, thanks!  ^_^</font>"""
+        )
+
+    btn2.click(
+        fn=get_output,
+        inputs=[img_in, size, bg, download_size],
+        outputs=[img_out, f1])
+    btn1.click(
+        fn=utils.clear_all,
+        inputs=None,
+        outputs=[img_in, img_out, size, bg, download_size, f1])
+
     gr.Button.style(1)
 
-demo.launch(share=True)
+demo.launch()

modelcenter/PP-Matting/APP1/download.py

@@ -0,0 +1,55 @@
+import os
+import sys
+import time
+
+import requests
+import zipfile
+
+FLUSH_INTERVAL = 0.1
+lasttime = time.time()
+
+
+def progress(str, end=False):
+    global lasttime
+    if end:
+        str += "\n"
+        lasttime = 0
+    if time.time() - lasttime >= FLUSH_INTERVAL:
+        sys.stdout.write("\r%s" % str)
+        lasttime = time.time()
+        sys.stdout.flush()
+
+
+def download_file(url, savepath, print_progress=True):
+    if print_progress:
+        print("Connecting to {}".format(url))
+    r = requests.get(url, stream=True, timeout=15)
+    total_length = r.headers.get('content-length')
+
+    if total_length is None:
+        with open(savepath, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
+    else:
+        with open(savepath, 'wb') as f:
+            dl = 0
+            total_length = int(total_length)
+            starttime = time.time()
+            if print_progress:
+                print("Downloading %s" % os.path.basename(savepath))
+            for data in r.iter_content(chunk_size=4096):
+                dl += len(data)
+                f.write(data)
+                if print_progress:
+                    done = int(50 * dl / total_length)
+                    progress("[%-50s] %.2f%%" %
+                             ('=' * done, float(100 * dl) / total_length))
+        if print_progress:
+            progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
+
+
+def uncompress(path):
+    files = zipfile.ZipFile(path, 'r')
+    filelist = files.namelist()
+    rootpath = filelist[0]
+    for file in filelist:
+        files.extract(file, './')