python - 如何在Flask中上传多个文件？

Question

我有一个flask应用程序，该应用程序用于将用户输入作为图像并运行模型并将其保存在文件夹中，它最初会获取单个图像并运行该应用程序。但是我希望我的应用程序拍摄多个图像，在这些图像上运行模型，然后将其保存到文件夹中。
在这个StackOverflow问题上找到了所有答案:Uploading multiple files with Flask，没有一个适合我的用例。
请帮助我阐明我要去哪里。
这是我的 flask 文件

from flask import Flask, render_template, url_for, session, redirect, request
from image_initial import Image_tensorflow

app = Flask(__name__)
app.config['SECRET_KEY'] = 'mykeyhere'

@app.route('/', methods =['GET', 'POST'])
def test():
    if "file_urls" not in session:
        session['file_urls'] = []
    file_urls = session['file_urls']
    if(request.method == 'POST'):
        file_obj = request.form['username']
        session['file_urls'] = file_obj
        return redirect(url_for('results'))
    return render_template("test.html")

@app.route('/results')
def results():
    if "file_urls" not in session or session['file_urls']  == []:
        print('session is not created')
        return redirect(url_for('test'))
    file_urls = session['file_urls']
    Image_tensorflow(file_urls,file_urls)
    session.pop('file_urls', None)
    #print(request.form)
    return render_template('results.html', file_urls=file_urls)

if __name__ == "__main__":
    app.run(host='0.0.0.0')

和我的HTML页面

<form action = "" method = "POST">
            <p>Upload your file here.</p>
            <p>
              <input type='file' name='username' multiple='multiple' class="btn btn-primary"/>
            </p>
            <p>
              <input type='submit' value='Upload' class="btn btn-secondary"/>

            </p>

这是我的image_initial.py文件

import numpy as np
import os
import sys
import tensorflow as tf
import json
from PIL import Image

sys.path.append("..")
from object_detection.utils import ops as utils_ops

from utils import label_map_util
from utils import visualization_utils as vis_util

def Image_tensorflow(xa,ya):
    PATH_TO_FROZEN_GRAPH = 'frozen_inference_graph.pb'
    PATH_TO_LABELS = 'object-detection.pbtxt'
    NUM_CLASSES = 4

    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)


    def load_image_into_numpy_array(image):
        (im_width, im_height) = image.size
        return np.array(image.getdata()).reshape(
            (im_height, im_width, 3)).astype(np.uint8)


    def image_url(xa, ya):
        file_path = 'images/'
        file_name = ya
        image = xa
        f = open((file_path + str(file_name) + ".json"), "w")
        f.close
        return_dict = {'image': image, 'file': f};
        return return_dict


    get_image_data = image_url(xa,ya)
    image_path= get_image_data['image']

    IMAGE_SIZE = (12, 8)


    def run_inference_for_single_image(image, graph):
        with graph.as_default():
            with tf.Session() as sess:
                # Get handles to input and output tensors
                ops = tf.get_default_graph().get_operations()
                all_tensor_names = {output.name for op in ops for output in op.outputs}
                tensor_dict = {}
                for key in [
                    'num_detections', 'detection_boxes', 'detection_scores',
                    'detection_classes', 'detection_masks'
                ]:
                    tensor_name = key + ':0'
                    if tensor_name in all_tensor_names:
                        tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
                            tensor_name)
                if 'detection_masks' in tensor_dict:
                    # The following processing is only for single image
                    detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                    detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                    # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                    real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                    detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                    detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                        detection_masks, detection_boxes, image.shape[0], image.shape[1])
                    detection_masks_reframed = tf.cast(
                        tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                    tensor_dict['detection_masks'] = tf.expand_dims(
                        detection_masks_reframed, 0)
                image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

                output_dict = sess.run(tensor_dict,
                                       feed_dict={image_tensor: np.expand_dims(image, 0)})

                output_dict['num_detections'] = int(output_dict['num_detections'][0])
                output_dict['detection_classes'] = output_dict[
                    'detection_classes'][0].astype(np.uint8)
                output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
                output_dict['detection_scores'] = output_dict['detection_scores'][0]
                if 'detection_masks' in output_dict:
                    output_dict['detection_masks'] = output_dict['detection_masks'][0]
        return output_dict


    for img in xa:
        image = Image.open(img)
    image_np = load_image_into_numpy_array(image)
    image_np_expanded = np.expand_dims(image_np, axis=0)
    # Actual detection.
    output_dict = run_inference_for_single_image(image_np, detection_graph)
    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=8)

    # get_image_data = image_url(sys.argv[1],sys.argv[2])
    # image_file = get_image_data['image']


    # pass values


    import cv2 as cv

    image_file = image_path
    img = cv.imread('image_file')
    i = 0
    j = 0
    limiter = 0.3

    while (i < 100):
        if (output_dict['detection_scores'][i] > limiter):
            j = j + 1
        i = i + 1

    # In[17]:


    # store the pass values in lists
    i = 0
    detection_classes = []
    detection_boxes = [[]] * j
    detection_scores = []
    while (i < j):
        detection_classes.append(output_dict['detection_classes'][i])
        detection_scores.append(output_dict['detection_scores'][i])
        detection_boxes[i].append(output_dict['detection_boxes'][i])
        i = i + 1

    list1 = []
    for items in detection_classes:
        if items == 1:
            list1.append("Angry")
        elif items == 2:
            list1.append("Sad")
        elif items == 3:
            list1.append("Neutral")
        elif items == 4:
            list1.append("Happy")

    final_dict = {'DETECTION': list1}

    file_to_write_to = get_image_data['file'].name
    file_to_write_to = str(file_to_write_to)
    text_file = open(file_to_write_to, "w")
    text_file.write(json.dumps(final_dict))
    text_file.close()
    final_path = "images/" + str(ya) + "_annotated" + ".jpg"

    # draw bounding boxes
    img = cv.imread('xa')
    i = 0
    for item in detection_classes:
        width, height = image.size
        ymin = int(detection_boxes[0][i][0] * height)
        xmin = int(detection_boxes[0][i][1] * width)
        ymax = int(detection_boxes[0][i][2] * height)
        xmax = int(detection_boxes[0][i][3] * width)
        font = cv.FONT_HERSHEY_SIMPLEX
        panel_colour = (182, 182, 42)
        bumper_colour = (241, 239, 236)
        damage_colour = (0, 255, 0)
        text_colour = (255, 255, 255)
        bumper_text = (0, 0, 0)
        buffer = int(5 * width / 1000)
        if (detection_classes[i] == 1):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'angry', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 2):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'sad', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 3):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), bumper_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), bumper_colour, -1)
            cv.putText(img, 'neutral', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), bumper_text,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 4):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'happy', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        i = i + 1

    final_path = "/home/mayureshk/PycharmProjects/ImageDetection/venv/models/research/object_detection/images/" + str(ya) + "_annotated" + ".jpg"
    cv.imwrite(final_path, img)

这是堆栈跟踪:

Traceback (most recent call last):
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/app.py", line 2446, in wsgi_app
    response = self.full_dispatch_request()
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/app.py", line 1951, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/app.py", line 1820, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise
    raise value
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/app.py", line 1949, in full_dispatch_request
    rv = self.dispatch_request()
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/lib/python3.7/site-packages/flask/app.py", line 1935, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "mayuresh.py", line 25, in results
    Image_tensorflow(file_urls,file_urls)
  File "/home/mayureshk/PycharmProjects/ImageDetection/venv/models/research/object_detection/image_initial.py", line 209, in Image_tensorflow
    cv.imwrite(final_path, img)
cv2.error: OpenCV(4.2.0) /io/opencv/modules/imgcodecs/src/loadsave.cpp:715: error: (-215:Assertion failed) !_img.empty() in function 'imwrite'

Answer 1

您没有得到文件列表，这就是为什么您没有得到多个文件的原因。您需要使用来自用户名输入的表单访问文件列表。

from flask import Flask, render_template, url_for, session, redirect, request

from image_initial import Image_tensorflow

app = Flask(__name__, template_folder='templates')
app.config['SECRET_KEY'] = 'mykeyhere'


@app.route('/', methods=['GET', 'POST'])
def test():
    if "file_urls" not in session:
        session['file_urls'] = []
    file_urls = session['file_urls']
    if request.method == 'POST':
        file_obj = request.form.getlist("username")
        session['file_urls'] = file_obj
        return redirect(url_for('results'))
    return render_template("test.html")


@app.route('/results')
def results():
    if "file_urls" not in session or session['file_urls'] == []:
        print('session is not created')
        return redirect(url_for('test'))
    file_urls = session['file_urls']
    Image_tensorflow(file_urls, file_urls)
    session.pop('file_urls', None)
    return render_template('results.html', file_urls=file_urls)


if __name__ == "__main__":
    app.run(host='0.0.0.0')

上面的代码将为您提供文件列表。但是，我尚未对它进行整体测试，因此如果某些其他操作不起作用，您可能需要进行一些修改

编辑:

在堆栈跟踪中，看来问题出在线 Image.open(xa)这里的xa是图像列表，而 Image.open()并不希望有一个列表，您可以遍历每个图像并将其打开。

for img in xa:
    Image.open(img)