Upload [gradio]model_display.py

[gradio]model_display.py

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+import numpy as np
+import gradio as gr
+import requests
+import base64
+import pandas as pd
+import cv2
+from typing import Tuple
+from PIL import Image
+from io import BytesIO
+
+import os
+
+from Model.Model6.model6_inference import main as model6_inferencer
+from mmyolo.utils import register_all_modules
+
+register_all_modules()
+
+
+def get_access_token(refatch=False) -> str:
+    """获取百度AI的access_token
+    :param refatch:是否重新获取access_token
+    :return:返回access_token"""
+    if refatch:
+        # client_id 为官网获取的AK， client_secret 为官网获取的SK
+        client_id = '7OtH60uo01ZNYN4yPyahlRSx'
+        client_secret = 'D5AxcUpyQyIA7KgPplp7dnz5tM0UIljy'
+        host = 'https://aip.baidubce.com/oauth/2.0/token?' \
+               'grant_type=client_credentials&client_id=%s&client_secret=%s' % (client_id, client_secret)
+        response = requests.get(host)
+        # print(response)
+        if response:
+            return response.json()['access_token']
+    else:
+        r"""
+        {"refresh_token":"25.24b9368ce91f9bd62c8dad38b3436800.315360000.2007815067.282335-30479502",
+         "expires_in":2592000,
+         "session_key":
+            "9mzdWT\/YmQ7oEi9WCRWbXd0YCcrSYQY6kKZjObKunlcKcZt95j9\/q1aJqbVXihpQOXK84o5WLJ8e7d4cXOi0VUJJcz5YEQ==",
+         "access_token":"24.becefee37aba38ea43c546fc154d3016.2592000.1695047067.282335-30479502",
+         "scope":"public brain_all_scope brain_body_analysis brain_body_attr brain_body_number brain_driver_behavior 
+                  brain_body_seg brain_gesture_detect brain_body_tracking brain_hand_analysis wise_adapt 
+                  lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian 
+                  ApsMisTest_Test\u6743\u9650 vis-classify_flower lpq_\u5f00\u653e cop_helloScope 
+                  ApsMis_fangdi_permission smartapp_snsapi_base smartapp_mapp_dev_manage iop_autocar oauth_tp_app 
+                  smartapp_smart_game_openapi oauth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi 
+                  smartapp_opensource_recapi fake_face_detect_\u5f00\u653eScope 
+                  vis-ocr_\u865a\u62df\u4eba\u7269\u52a9\u7406 idl-video_\u865a\u62df\u4eba\u7269\u52a9\u7406 
+                  smartapp_component smartapp_search_plugin avatar_video_test b2b_tp_openapi b2b_tp_openapi_online 
+                  smartapp_gov_aladin_to_xcx","session_secret":"5c8c3dbb80b04f58bb33aa8077758679"
+        }
+        """
+        access_token = "24.becefee37aba38ea43c546fc154d3016.2592000.1695047067.282335-30479502"
+        return access_token
+
+
+def resize_image(img, max_length=2048, min_length=50) -> Tuple[np.ndarray, bool]:
+    """Ensure that the longest side is shorter than 2048px and the shortest side is longer than 50px.
+    :param img: 前端传入的图片
+    :param max_length: 最长边像素
+    :param min_length: 最短边像素
+    :return: 返回处理后的图片和是否进行了resize的标志
+    """
+    flag = False
+    max_side = max(img.shape[0], img.shape[1])
+    min_side = min(img.shape[0], img.shape[1])
+    if max_side > max_length:
+        scale = max_length / max_side
+        img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
+        flag = True
+    if min_side < min_length:
+        scale = min_length / min_side
+        img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
+        flag = True
+    return img, flag
+
+
+def model1_det(x):
+    """人体检测与属性识别
+    :param x:前端传入的图片
+    :return:返回检测结果
+    """
+
+    def _Baidu_det(img):
+        """调用百度AI接口进行人体检测与属性识别
+        :param img:前端传入的图片，格式为numpy.ndarray
+        :return:返回检测结果
+        """
+        request_url = "https://aip.baidubce.com/rest/2.0/image-classify/v1/body_attr"
+        # 保存图片到本地
+        cv2.imwrite('test.jpg', img)
+        # 二进制方式打开图片文件
+        f = open('test.jpg', 'rb')
+        hex_image = base64.b64encode(f.read())
+        # 选择二进制图片和需要输出的属性（12个）
+        params = {
+            "image": hex_image,
+            "type": "gender,age,upper_wear,lower_wear,upper_color,lower_color,"
+                    "orientation,upper_cut,lower_cut,side_cut,occlusion,is_human"
+        }
+        access_token = get_access_token()
+        request_url = request_url + "?access_token=" + access_token
+        headers = {'content-type': 'application/x-www-form-urlencoded'}
+        response = requests.post(request_url, data=params, headers=headers)
+        if response:
+            return response.json()
+
+    def _get_attributes_list(r) -> dict:
+        """获取人体属性列表
+        :param r:百度AI接口返回的json数据
+        :return:返回人体属性列表
+        """
+        all_humans_attributes_list = {}
+        person_num = r['person_num']
+        print('person_num:', person_num)
+        for human_idx in range(person_num):
+            attributes_dict = r['person_info'][human_idx]['attributes']
+            attributes_list = []
+            for key, value in attributes_dict.items():
+                attribute = [key, value['name'], value['score']]
+                attributes_list.append(attribute)
+            new_value = ['attribute', 'attribute_value', 'accuracy']
+            attributes_list.insert(0, new_value)
+            df = pd.DataFrame(attributes_list[1:], columns=attributes_list[0])
+            all_humans_attributes_list[human_idx] = df
+        return all_humans_attributes_list
+
+    def _show_img(img, bboxes):
+        """显示图片
+        :param img:前端传入的图片
+        :param bboxes:检测框坐标
+        :return:处理完成的图片 """
+        line_width = int(max(img.shape[1], img.shape[0]) / 400)
+        for bbox in bboxes:
+            left, top, width, height = bbox['left'], bbox['top'], bbox['width'], bbox['height']
+            right, bottom = left + width, top + height
+            for i in range(left, right):
+                img[top:top + line_width, i] = [255, 0, 0]
+                img[bottom - line_width:bottom, i] = [255, 0, 0]
+            for i in range(top, bottom):
+                img[i, left:left + line_width] = [255, 0, 0]
+                img[i, right - line_width:right] = [255, 0, 0]
+        return img
+
+    result = _Baidu_det(x)
+    HAs_list = _get_attributes_list(result)
+    locations = []
+    for i in range(len(result['person_info'])):
+        locations.append(result['person_info'][i]['location'])
+
+    return _show_img(x, locations), f"模型检测到的人数为：{result['person_num']}人"
+
+
+def model2_rem(x):
+    """背景消除
+    :param x: 前端传入的图片
+    :return: 返回处理后的图片
+    """
+
+    def _Baidu_rem(img):
+        """调用百度AI接口进行背景消除
+        :param img: 前端传入的图片，格式为numpy.ndarray
+        :return: 返回处理后的图片
+        """
+        request_url = "https://aip.baidubce.com/rest/2.0/image-classify/v1/body_seg"
+        bgr_image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        cv2.imwrite('test.jpg', bgr_image)
+        f = open('test.jpg', 'rb')
+        hex_image = base64.b64encode(f.read())
+        params = {"image": hex_image}
+        access_token = get_access_token()
+        request_url = request_url + "?access_token=" + access_token
+        headers = {'content-type': 'application/x-www-form-urlencoded'}
+        response = requests.post(request_url, data=params, headers=headers)
+        if response:
+            encoded_image = response.json()["foreground"]
+            decoded_image = base64.b64decode(encoded_image)
+            image = Image.open(BytesIO(decoded_image))
+            image_array = np.array(image)
+            return image_array
+
+    resized_x, resized_f = resize_image(x)
+    new_img = _Baidu_rem(resized_x)
+    if resized_f:
+        resized_f = "图片尺寸已被修改至合适大小"
+    else:
+        resized_f = "图片尺寸无需修改"
+
+    return new_img, resized_f
+
+
+def model3_ext(x: np.ndarray, num_clusters=12):
+    """主色调提取
+    :param x: 前端传入的图片
+    :param num_clusters: 聚类的数量
+    :return: 返回主色调条形卡片"""
+
+    # TODO: 编写颜色名称匹配算法[most important]
+    # TODO: 修改颜色条形卡片呈现形式，要求呈现颜色名称和比例[important]
+    def _find_name(color):
+        """根据颜色值查找颜色名称
+        :param color:颜色值
+        :return:返回颜色名称
+        """
+        pass
+
+    def _cluster(img, NUM_CLUSTERS):
+        """K-means 聚类提取主色调
+        :param img: 前端传入的图片
+        :param NUM_CLUSTERS: 聚类的数量
+        :return: 返回聚类结果
+        """
+        h, w, ch = img.shape
+        reshaped_x = np.float32(img.reshape((-1, 4)))
+        new_data_list = []
+        for i in range(len(reshaped_x)):
+            if reshaped_x[i][3] < 100:
+                continue
+            else:
+                new_data_list.append(reshaped_x[i])
+        reshaped_x = np.array(new_data_list)
+        reshaped_x = np.delete(reshaped_x, 3, axis=1)
+        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+        NUM_CLUSTERS = NUM_CLUSTERS
+        ret, label, center = cv2.kmeans(reshaped_x, NUM_CLUSTERS, None, criteria,
+                                        NUM_CLUSTERS, cv2.KMEANS_RANDOM_CENTERS)
+        clusters = np.zeros([NUM_CLUSTERS], dtype=np.int32)
+        for i in range(len(label)):
+            clusters[label[i][0]] += 1
+        clusters = np.float32(clusters) / float(len(reshaped_x))
+        center = np.int32(center)
+        x_offset = 0
+        card = np.zeros((50, w, 3), dtype=np.uint8)
+        for c in np.argsort(clusters)[::-1]:
+            dx = int(clusters[c] * w)
+            b = center[c][0]
+            g = center[c][1]
+            r = center[c][2]
+            cv2.rectangle(card, (x_offset, 0), (x_offset + dx, 50),
+                          (int(b), int(g), int(r)), -1)
+            x_offset += dx
+
+        return card, resized_f
+
+    resized_x, resized_f = resize_image(x)
+    card, resized_f = _cluster(resized_x, num_clusters)
+    if resized_f:
+        resized_f = "图片尺寸已被修改至合适大小"
+    else:
+        resized_f = "图片尺寸无需修改"
+
+    return card, resized_f
+
+
+def model4_clo(x_path: str):
+    def _get_result(input_path: str, cls_results: dict) -> pd.DataFrame:
+        """convert the results of model6_2 to a dataframe
+        :param input_path: the (absolute) path of the image
+        :param cls_results: the results of model6_2
+
+        :return: a dataframe to display on the web
+        """
+        result_pd = []
+        img_name = os.path.basename(input_path)
+        pred_profile = cls_results[img_name][0]['pred_class']
+        pred_score = round(cls_results[img_name][0]['pred_score'], 2)
+        result_pd.append([img_name, pred_profile, pred_score])
+        df = pd.DataFrame(result_pd, columns=None)
+        return df
+
+    output_path_root = 'upload_to_web_tmp'
+    if not os.path.exists(output_path_root):
+        os.mkdir(output_path_root)
+    cls_result = model6_inferencer(x_path, output_path_root)
+
+    if cls_result:
+        # use np to read image·
+        x_name = os.path.basename(x_path)
+        pred_x = np.array(Image.open(os.path.join(output_path_root, 'visualizations', x_name)))
+
+        return pred_x, _get_result(x_path, cls_result), "识别成功!"
+    # TODO: 完善识别失败时的处理（model6_inference.py中）[important]
+    return x_path, pd.DataFrame(), "未检测到服装"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Flip text or image files using this demo.")
+    with gr.Tab("人体检测模型"):
+        with gr.Row():
+            model1_input = gr.Image(height=400)
+            model1_output_img = gr.Image(height=400)
+            # model1_output_df = gr.DataFrame()
+        model1_button = gr.Button("开始检测")
+    with gr.Tab("背景消除模型"):
+        with gr.Row():
+            model2_input = gr.Image(height=400)
+            model2_output_img = gr.Image(height=400)
+        model2_button = gr.Button("开始消除")
+    with gr.Tab('主色调提取'):
+        with gr.Row():
+            with gr.Column():
+                # TODO: 参照“蒙娜丽莎”尝试修改前端界面[not important]
+                # TODO: 修改布局，使其更美观[moderately important]
+                model3_input = gr.Image(height=400, image_mode='RGBA')
+                model3_slider = gr.Slider(minimum=1, maximum=20, step=1, value=12,
+                                          min_width=400, label="聚类数量")
+            model3_output_img = gr.Image(height=400)
+        model3_button = gr.Button("开始提取")
+    with gr.Tab("廓形识别"):
+        with gr.Row():
+            model4_input = gr.Image(height=400, type="filepath")
+            model4_output_img = gr.Image(height=400)
+            model4_output_df = gr.DataFrame(headers=['img_name', 'pred_profile', 'pred_score'],
+                                            datatype=['str', 'str', 'number'])
+        model4_button = gr.Button("开始识别")
+    # 设置折叠内容
+    with gr.Accordion("模型运行信息"):
+        running_info = gr.Markdown("等待输入和运行...")
+
+    model1_button.click(model1_det, inputs=model1_input, outputs=[model1_output_img, running_info])
+    model2_button.click(model2_rem, inputs=model2_input, outputs=[model2_output_img, running_info])
+    model3_button.click(model3_ext, inputs=[model3_input, model3_slider], outputs=[model3_output_img, running_info])
+    model4_button.click(model4_clo, inputs=model4_input, outputs=[model4_output_img, model4_output_df, running_info])
+demo.launch(share=True)