Delete [gradio]model_display.py

[gradio]model_display.py

@@ -1,323 +0,0 @@
-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)