malaria_api.py

from __future__ import division
from flask import Flask, request, jsonify
from werkzeug.utils import secure_filename
import os
import cv2
import numpy as np
import sys
import pickle
from optparse import OptionParser
import time
from keras_frcnn import config
from keras import backend as K
from keras.layers import Input
from keras.models import Model
from keras_frcnn import roi_helpers
import tensorflow as tf
import base64

app = Flask(__name__)
UPLOAD_FOLDER = os.path.dirname('uploads')
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

# sys.setrecursionlimit(40000)

# parser = OptionParser()
#
# parser.add_option("-p", "--path", dest="test_path", help="Path to test data.")
# parser.add_option("-n", "--num_rois", type="int", dest="num_rois",
#                   help="Number of ROIs per iteration. Higher means more memory use.", default=32)
# parser.add_option("--config_filename", dest="config_filename", help=
# "Location to read the metadata related to the training (generated when training).",
#                   default="config.pickle")
# parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.",
#                   default='resnet50')
#
# (options, args) = parser.parse_args()
#
# if not options.test_path:  # if filename is not given
#     parser.error('Error: path to test data must be specified. Pass --path to command line')

config_output_filename = "config.pickle"

with open(config_output_filename, 'rb') as f_in:
    C = pickle.load(f_in)

if C.network == 'resnet50':
    import keras_frcnn.resnet as nn
elif C.network == 'vgg':
    import keras_frcnn.vgg as nn

# turn off any data augmentation at test time
C.use_horizontal_flips = False
C.use_vertical_flips = False
C.rot_90 = False

def format_img_size(img, C):
    """ formats the image size based on config """
    img_min_side = float(C.im_size)
    (height, width, _) = img.shape

    if width <= height:
        ratio = img_min_side / width
        new_height = int(ratio * height)
        new_width = int(img_min_side)
    else:
        ratio = img_min_side / height
        new_width = int(ratio * width)
        new_height = int(img_min_side)
    img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
    return img, ratio

def format_img_channels(img, C):
    """ formats the image channels based on config """
    img = img[:, :, (2, 1, 0)]
    img = img.astype(np.float32)
    img[:, :, 0] -= C.img_channel_mean[0]
    img[:, :, 1] -= C.img_channel_mean[1]
    img[:, :, 2] -= C.img_channel_mean[2]
    img /= C.img_scaling_factor
    img = np.transpose(img, (2, 0, 1))
    img = np.expand_dims(img, axis=0)
    return img

def format_img(img, C):
    """ formats an image for model prediction based on config """
    img, ratio = format_img_size(img, C)
    img = format_img_channels(img, C)
    return img, ratio

# Method to transform the coordinates of the bounding box to its original size
def get_real_coordinates(ratio, x1, y1, x2, y2):
    real_x1 = int(round(x1 // ratio))
    real_y1 = int(round(y1 // ratio))
    real_x2 = int(round(x2 // ratio))
    real_y2 = int(round(y2 // ratio))

    return (real_x1, real_y1, real_x2, real_y2)

class_mapping = C.class_mapping

if 'bg' not in class_mapping:
    class_mapping['bg'] = len(class_mapping)

class_mapping = {v: k for k, v in class_mapping.items()}
print(class_mapping)
class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}
C.num_rois = 32

if C.network == 'resnet50':
    num_features = 1024
elif C.network == 'vgg':
    num_features = 512

if K.image_dim_ordering() == 'th':
    input_shape_img = (3, None, None)
    input_shape_features = (num_features, None, None)
else:
    input_shape_img = (None, None, 3)
    input_shape_features = (None, None, num_features)

img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(C.num_rois, 4))
feature_map_input = Input(shape=input_shape_features)

# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)

# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn_layers = nn.rpn(shared_layers, num_anchors)

classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping),
                           trainable=True)

# def get_model():
#     global model_rpn
#     global model_classifier_only
model_rpn = Model(img_input, rpn_layers)
model_classifier_only = Model([feature_map_input, roi_input], classifier)

model_classifier = Model([feature_map_input, roi_input], classifier)

print('Loading weights from {}'.format(C.model_path))
model_rpn.load_weights(C.model_path, by_name=True)
model_classifier.load_weights(C.model_path, by_name=True)

model_rpn.compile(optimizer='sgd', loss='mse')
model_classifier.compile(optimizer='sgd', loss='mse')

graph = tf.get_default_graph()

all_imgs = []

classes = {}

bbox_threshold = 0.8

visualise = True


@app.route('/uploader', methods=['GET', 'POST'])
def upload_file():
    if request.method == 'POST':

        file = request.files['file']
        f = os.path.join(UPLOAD_FOLDER, "temp.jpg")
        file.save(secure_filename(f))

        img_name = f

        print(img_name)
        st = time.time()
        # filepath = os.path.join(img_path, img_name)

        img = cv2.imread(img_name)

        # img = cv2.resize(img,None,fx=0.5,fy=0.5)

        X, ratio = format_img(img, C)

        if K.image_dim_ordering() == 'tf':
            X = np.transpose(X, (0, 2, 3, 1))

        # model_rpn._make_predict_function()

        # get_model()
        with graph.as_default():
            # get the feature maps and output from the RPN
            [Y1, Y2, F] = model_rpn.predict(X)

        R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7)

        # convert from (x1,y1,x2,y2) to (x,y,w,h)
        R[:, 2] -= R[:, 0]
        R[:, 3] -= R[:, 1]

        # apply the spatial pyramid pooling to the proposed regions
        bboxes = {}
        probs = {}

        for jk in range(R.shape[0] // C.num_rois + 1):
            ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)
            if ROIs.shape[1] == 0:
                break

            if jk == R.shape[0] // C.num_rois:
                # pad R
                curr_shape = ROIs.shape
                target_shape = (curr_shape[0], C.num_rois, curr_shape[2])
                ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)
                ROIs_padded[:, :curr_shape[1], :] = ROIs
                ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :]
                ROIs = ROIs_padded

            with graph.as_default():
                [P_cls, P_regr] = model_classifier_only.predict([F, ROIs])

            for ii in range(P_cls.shape[1]):

                if np.max(P_cls[0, ii, :]) < bbox_threshold or np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1):
                    continue

                cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]

                if cls_name not in bboxes:
                    bboxes[cls_name] = []
                    probs[cls_name] = []

                (x, y, w, h) = ROIs[0, ii, :]

                cls_num = np.argmax(P_cls[0, ii, :])
                try:
                    (tx, ty, tw, th) = P_regr[0, ii, 4 * cls_num:4 * (cls_num + 1)]
                    tx /= C.classifier_regr_std[0]
                    ty /= C.classifier_regr_std[1]
                    tw /= C.classifier_regr_std[2]
                    th /= C.classifier_regr_std[3]
                    x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)
                except:
                    pass
                bboxes[cls_name].append(
                    [C.rpn_stride * x, C.rpn_stride * y, C.rpn_stride * (x + w), C.rpn_stride * (y + h)])
                probs[cls_name].append(np.max(P_cls[0, ii, :]))

        all_dets = []

        for key in bboxes:
            bbox = np.array(bboxes[key])

            new_boxes, new_probs = roi_helpers.non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5)
            for jk in range(new_boxes.shape[0]):
                (x1, y1, x2, y2) = new_boxes[jk, :]

                (real_x1, real_y1, real_x2, real_y2) = get_real_coordinates(ratio, x1, y1, x2, y2)

                cv2.rectangle(img, (real_x1, real_y1), (real_x2, real_y2),
                              (int(class_to_color[key][0]), int(class_to_color[key][1]), int(class_to_color[key][2])),
                              2)

                textLabel = '{}: {}'.format(key, int(100 * new_probs[jk]))
                all_dets.append((key, 100 * new_probs[jk]))

                (retval, baseLine) = cv2.getTextSize(textLabel, cv2.FONT_HERSHEY_COMPLEX, 1, 1)
                textOrg = (real_x1, real_y1 - 0)

                cv2.rectangle(img, (textOrg[0] - 5, textOrg[1] + baseLine - 5),
                              (textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (0, 0, 0), 2)
                cv2.rectangle(img, (textOrg[0] - 5, textOrg[1] + baseLine - 5),
                              (textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (255, 255, 255), -1)
                cv2.putText(img, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1)

        print('Elapsed time = {}'.format(time.time() - st))
        print(all_dets)
        print(len(all_dets))
        cv2.imwrite('temp_infected.jpg',img)
        # cv2.imshow('img', img)
        # cv2.waitKey(0)
        result = {}

        if len(all_dets) > 0:
            result['count'] = len(all_dets)
        else:
            result['count'] = 0

        with open("temp_infected.jpg", "rb") as imageFile:
            str = base64.b64encode(imageFile.read())
            print(str)
            result['image'] = str.decode("utf-8")

        return jsonify(result)


if __name__ == '__main__':
    app.debug = False
    app.run(host='192.168.43.109')