Create app.py

app.py

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+# This is a sample Python script.
+
+# Press ⌃R to execute it or replace it with your code.
+# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
+import base64
+import datetime
+import json
+
+import cv2
+import requests
+from PIL import Image, ImageDraw, ImageFont, ImageOps
+import numpy as np
+from io import BytesIO
+import time
+
+main_image_path = "/Users/aaron/Documents/temp/16pic_2415206_s.png"
+API_TOKEN = "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"
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+# API_URL = "https://api-inference.huggingface.co/models/hustvl/yolos-tiny"
+API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50"
+# API_OBJECT_URL = "https://api-inference.huggingface.co/models/microsoft/resnet-50"
+API_SEGMENTATION_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
+API_SEGMENTATION_URL_2 = "https://api-inference.huggingface.co/models/nvidia/segformer-b0-finetuned-ade-512-512"
+
+temp_dir = "/Users/aaron/Documents/temp/imageai/"
+
+
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.request("POST", API_URL, headers=headers, data=data)
+    return json.loads(response.content.decode("utf-8"))
+
+
+def queryObjectDetection(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.request("POST", API_OBJECT_URL, headers=headers, data=data, timeout=6)
+    print(response)
+    return json.loads(response.content.decode("utf-8"))
+
+
+def getImageSegmentation():
+    data = query(main_image_path)
+    print(data)
+    return data
+
+
+def crop_image(box):
+    # 打开图片
+    image = Image.open(main_image_path)
+
+    # 计算裁剪区域
+    crop_area = (box['xmin'], box['ymin'], box['xmax'], box['ymax'])
+
+    # 裁剪图片
+    cropped_image = image.crop(crop_area)
+
+    return cropped_image
+
+
+# # 示例
+# image_path = "path/to/your/image.jpg"
+# box = {'xmin': 186, 'ymin': 75, 'xmax': 252, 'ymax': 123}
+#
+# cropped_image = crop_image(image_path, box)
+# cropped_image.show()  # 显示裁剪后的图片
+# cropped_image.save("path/to/save/cropped_image.jpg")  # 保存裁剪后的图片
+
+# Press the green button in the gutter to run the script.
+# if __name__ == '__main__':
+#     data = getImageSegmentation()
+#     for item in data:
+#         box = item['box']
+#         cropped_image = crop_image(box)
+#         temp_image_path = temp_dir + str(int(datetime.datetime.now().timestamp() * 1000000)) + ".png"
+#         print(temp_image_path)
+#         cropped_image.save(temp_image_path)
+#         object_data = queryObjectDetection(temp_image_path)
+#         print(object_data)
+#         flag = False
+#         for obj in object_data:
+#             # 检查字典中是否包含 'error' 键
+#             if 'error' in obj and obj['error'] is not None:
+#                 flag = True
+#                 print("找到了一个包含 'error' 键的字典，且其值不为 None")
+#             else:
+#                 print("字典不包含 'error' 键，或其值为 None")
+#         if flag:
+#             continue
+#         item['label'] = object_data[0]['label']
+#     print(data)
+#
+#     ###下面就是画个图，和上面住流程无关，仅仅用于测试
+#     image = Image.open(main_image_path)
+#     draw = ImageDraw.Draw(image)
+#
+#     # 设置边框颜色和字体
+#     border_color = (255, 0, 0)  # 红色
+#     text_color = (255, 255, 255)  # 白色
+#     font = ImageFont.truetype("Geneva.ttf", 12)  # 使用 系统Geneva 字体，大小为 8
+#
+#     # 遍历对象列表，画边框和标签
+#     for obj in data:
+#         label = obj['label']
+#         box = obj['box']
+#         xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
+#
+#         # 画边框
+#         draw.rectangle([xmin, ymin, xmax, ymax], outline=border_color, width=2)
+#
+#         # 画标签
+#         text_size = draw.textsize(label, font=font)
+#         draw.rectangle([xmin, ymin, xmin + text_size[0], ymin + text_size[1]], fill=border_color)
+#         draw.text((xmin, ymin), label, font=font, fill=text_color)
+#
+#     image.show()
+
+
+import numpy as np
+from PIL import Image
+import gradio as gr
+import imageio
+
+
+def send_request_to_api(img_byte_arr, max_retries=3, wait_time=60):
+    retry_count = 0
+
+    while retry_count < max_retries:
+        response = requests.request("POST", API_SEGMENTATION_URL, headers=headers, data=img_byte_arr)
+        response_content = response.content.decode("utf-8")
+
+        # 检查响应是否包含错误
+        if "error" in response_content:
+            print(f"Error: {response_content}")
+            retry_count += 1
+            time.sleep(wait_time)
+        else:
+            json_obj = json.loads(response_content)
+            return json_obj
+
+    raise Exception("Failed to get a valid response from the API after multiple retries.")
+
+def getSegmentationMaskImage(input_img, blur_kernel_size=21):
+    # 调整输入图像的大小
+    target_width = 600
+    aspect_ratio = float(input_img.height) / float(input_img.width)
+    target_height = int(target_width * aspect_ratio)
+    input_img.thumbnail((target_width, target_height))
+
+    img_byte_arr = BytesIO()
+    input_img.save(img_byte_arr, format='PNG')
+    img_byte_arr = img_byte_arr.getvalue()
+    json_obj = send_request_to_api(img_byte_arr)
+    print(json_obj)
+
+    # 加载原始图像
+    original_image = input_img.copy()
+
+    # 如果原始图像不是RGBA模式，则将其转换为RGBA模式
+    if original_image.mode != 'RGBA':
+        original_image = original_image.convert('RGBA')
+
+    output_images = []
+    for item in json_obj:
+        label = item['label']
+
+        # 如果label以"LABEL"开头，则跳过此项
+        if label.startswith("LABEL"):
+            continue
+        mask_data = item['mask']
+
+        # 将Base64编码的mask数据解码为PNG图像
+        mask_image = Image.open(BytesIO(base64.b64decode(mask_data)))
+
+        # 将原始图像转换为OpenCV格式并应用高斯模糊
+        original_image_cv2 = cv2.cvtColor(np.array(original_image.convert('RGB')), cv2.COLOR_RGB2BGR)
+        blurred_image_cv2 = cv2.GaussianBlur(original_image_cv2, (blur_kernel_size, blur_kernel_size), 0)
+
+        # 将模糊图像转换回PIL格式，并将其转换回原始图像的颜色模式
+        blurred_image = Image.fromarray(cv2.cvtColor(blurred_image_cv2, cv2.COLOR_BGR2RGB)).convert(original_image.mode)
+
+        # 使用mask_image作为蒙版将原始图像的非模糊部分复制到模糊图像上
+        process_image = Image.composite(original_image, blurred_image, mask_image)
+
+        # 在mask位置添加红色文本和指向原始图像非模糊部分的红色线
+        draw = ImageDraw.Draw(process_image)
+        font = ImageFont.truetype("Geneva.ttf", 20)  # 您可以选择其他字体和大小
+        text_position = (10, 20)
+        draw.text(text_position, label, font=font, fill=(255, 0, 0))
+
+        # 计算mask的边界框
+        mask_bbox = mask_image.getbbox()
+
+        # 计算mask边界框的顶部中心点
+        mask_top_center_x = (mask_bbox[0] + mask_bbox[2]) // 2
+        mask_top_center_y = mask_bbox[1]
+
+        # 计算文本框的底部中心点
+        text_width, text_height = draw.textsize(label, font=font)
+        text_bottom_center_x = text_position[0] + text_width // 2
+        text_bottom_center_y = text_position[1] + text_height
+
+        # 绘制一条从文本框底部中心到mask边界框顶部中心的红色线
+        draw.line([(text_bottom_center_x, text_bottom_center_y), (mask_top_center_x, mask_top_center_y)],
+                  fill=(255, 0, 0), width=2)
+
+        output_images.append(process_image)
+    return output_images
+
+def sepia(input_img):
+    # 检查输入图像的数据类型和值范围
+    if input_img.dtype == np.float32 and np.max(input_img) <= 1.0:
+        input_img = (input_img * 255).astype(np.uint8)
+
+    input_img = Image.fromarray(input_img)
+    output_images = getSegmentationMaskImage(input_img)
+
+    # 将所有图像堆叠在一起
+    stacked_image = np.vstack([np.array(img) for img in output_images])
+    return stacked_image
+
+
+def imageDemo():
+    demo = gr.Interface(sepia, gr.Image(shape=None), gr.outputs.Image(label="Processed Images", type="numpy"),
+                        title='Image Processing Demo')
+    demo.launch(share=True)
+
+
+if __name__ == '__main__':
+    imageDemo()
+
+
+
+#######---------gif输出方式
+# def sepia(input_img):
+#     input_img = Image.fromarray((input_img * 255).astype(np.uint8))
+#
+#     output_images = getSegmentationMaskImage(input_img)
+#
+#     # 生成GIF动画
+#     buffered = BytesIO()
+#     output_images[0].save(buffered, format='GIF', save_all=True, append_images=output_images[1:], duration=3000, loop=0)
+#     gif_str = base64.b64encode(buffered.getvalue()).decode()
+#     return f'<img src="data:image/gif;base64,{gif_str}" width="400" />'
+#
+#
+# def imageDemo():
+#     demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), gr.outputs.HTML(label="Processed Animation"), title='Sepia Filter Demo')
+#     demo.launch()