自己jpg_2_tif

import ctypes
import platform
from pathlib import Path
import cv2
import uvicorn
from fastapi import FastAPI

app = FastAPI()

from libtiff import TIFF
import numpy as np
import os
import sys
import tifffile
import time
from shutil import copyfile

np.set_printoptions(threshold=np.sys.maxsize)
import base64


#


# jpg转tiff算法


def in_out_zone(midhang, midlie, s1, s2):
    if ((midhang - 5).any() <= 0):
        up_in = 1
    else:
        up_in = midhang - 5

    if ((midlie - 5).any() <= 0):
        left_in = 1
    else:
        left_in = midlie - 5

    if ((midhang + 5).any() >= s1):
        down_in = s1
    else:
        down_in = midhang + 5

    if ((midlie + 5).any() >= s2):
        right_in = s2
    else:
        right_in = midlie + 5

    if ((midhang - 15).any() <= 0):
        up_out = 1
    else:
        up_out = midhang - 15

    if ((midlie - 15).any() <= 0):
        left_out = 1
    else:
        left_out = midlie - 15

    if ((midhang + 15).any() >= s1):
        down_out = s1
    else:
        down_out = midhang + 15

    if ((midlie + 15).any() >= s2):
        right_out = s2
    else:
        right_out = midlie + 15

    return up_in, down_in, left_in, right_in, up_out, down_out, left_out, right_out


def flags(hang, lie, pixels, s1, s2):
    if ((hang - pixels).any() <= 0):
        up = 1
    else:
        up = hang - pixels

    if ((hang + pixels).any() > s1):
        down = s1
    else:
        down = hang + pixels

    if ((lie - pixels).any() <= 0):
        left = 1
    else:
        left = lie - pixels

    if ((lie + pixels).any() > s2):
        right = s2
    else:
        right = lie + pixels

    if type(up) == np.ndarray:
        up = up.tolist()
    else:
        up = [up]

    if type(down) == np.ndarray:
        down = down.tolist()
    else:
        down = [down]

    if type(left) == np.ndarray:
        left = left.tolist()
    else:
        left = [left]

    if type(right) == np.ndarray:
        right = right.tolist()
    else:
        right = [right]

    return up, down, left, right




def jpg_to_tiff( image_address, tiff_raw_out_address,):
    # jpg_to_tiff_return = {'raw_read':None, 'tiff_img_name':None}

    image_address = str(base64.b64decode(image_address.encode("utf-8")), "utf-8")
    tiff_raw_out_address = str(base64.b64decode(tiff_raw_out_address.encode("utf-8")), "utf-8")

    image_address_split = image_address.split('/')
    image_address_split_raw = image_address_split[-1][0:-3]
    dji_exe = 'F:/GuanYong/zzb/guanyong_pro/rgbtotiff_exe/utility/bin/windows/release_x64/dji_irp.exe' +' '+'-s'+' '+str(image_address)+' '+'-o'+' '+tiff_raw_out_address+'/'+image_address_split_raw+'raw'+' '+'-a measure   --measurefmt float32'  # .exe文件的路径【例如："E:/Desktop/C++/Debug/Project1.exe"】
    r_v = os.system(dji_exe)


    img = np.fromfile(tiff_raw_out_address+'/'+image_address_split_raw+'raw', dtype='float32')

    # print(img)
    img = img.reshape(512,640)
    # print(img.shape)
    img2 = img.T
    # print(img2)
    tifffile.imsave(tiff_raw_out_address+ '/' + image_address_split_raw + 'tiff', img2)
    for imagename in os.listdir(tiff_raw_out_address):
        if imagename.endswith(".raw"):
             os.remove(tiff_raw_out_address+ '/'+imagename)

    tiff_img_name = image_address_split_raw + 'tiff'

    return tiff_img_name
    # print(r_v, '返回值')








# 调用配准应用
def peizhun( thermal_tiff_img_path, thermal_rgb_img_path, visible_img_path, output_path):

    print('启动配准')
    # image_address_split_raw = image_address_split[-1][0:-3]
    peizhun_exe = 'E:/ProgramData/peizhun/peizhun/TAMM_package_noparelle/main_TAMM.exe' +' '+'--thermal_tiff_path'+' '+str(thermal_tiff_img_path)+' '+'--thermal_rgb_path'+' '+thermal_rgb_img_path+' '+'--visible_path'+' '+visible_img_path+' '+'--output_path'+' '+output_path  # .exe文件的路径【例如："E:/Desktop/C++/Debug/Project1.exe"】

    os.system(peizhun_exe)

    return output_path





def one_shibie(thermal_rgb_path, save_thermal_rgb_path ,  visible_rgb_path, save_visible_rgb_path , temperature_difference, temperature_threshold2, temperature_threshold):




# 验证应用通路服务

        # start = time.clock()


        thermal_rgb_path = str(base64.b64decode(thermal_rgb_path.encode("utf-8")), "utf-8")
        save_thermal_rgb_path = str(base64.b64decode(save_thermal_rgb_path.encode("utf-8")), "utf-8")


        visible_rgb_path = str(base64.b64decode(visible_rgb_path.encode("utf-8")), "utf-8")
        save_visible_rgb_path = str(base64.b64decode(save_visible_rgb_path.encode("utf-8")), "utf-8")

        start = time.clock()
        time_list = {'dji_time':None, 'method_time':None, 'suanfa_time':None}



        # thermal_rgb_path_zz = cv2.imdecode(np.fromfile(thermal_rgb_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)


        thermal_rgb_path2 = 'F:/GuanYong/zzb/zhongzhuan_files/zz.JPG' # E:/ProgramData/zhongzhuan2/zz.JPG'  中转
        copyfile(thermal_rgb_path, thermal_rgb_path2)




        # thermal_rgb_path = unicode()
        one_shibie_return = {'save_thermal_rgb_path': None,
                             'save_visible_rgb_path': None,
                             'shibie_result': None,
                             }

        image_address_split = thermal_rgb_path.split('/')
        image_address_split_raw = image_address_split[-1][0:-3]


        # cv2.imencode('.jpg', thermal_rgb_path_zz)[1].tofile('E:/ProgramData/zhongzhuan/'+image_address_split[-1][0:-3]+'jpg')

        # cv2.imwrite('E:/ProgramData/zhongzhuan/'+image_address_split[-1][0:-3]+'jpg', thermal_rgb_path_zz)

        # thermal_rgb_path2 = 'E:/ProgramData/zhongzhuan/'+image_address_split[-1][0:-3]+'JPG'
        #
        # cv2.imwrite(thermal_rgb_path2, thermal_rgb_path_zz)

        thermal_image_rgb = cv2.imread(thermal_rgb_path2)

        image_address_split2 = visible_rgb_path.split('/')
        # visable_image_rgb = cv2.imread(visible_rgb_path)


        visable_image_rgb = cv2.imdecode(np.fromfile(visible_rgb_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
        # # cv2.imencode('.jpg', visable_image_rgb)[1].tofile('测试/001.jpg')   E:\ProgramData\zhongzhuan   cv2.imencode('.jpg', visable_image_rgb)[1].tofile


        visable_image_rgb = cv2.resize(visable_image_rgb,(640, 512))
        cv2.imwrite(save_visible_rgb_path + '/' + image_address_split2[-1][0:-4] + 'output.JPG', visable_image_rgb)


        cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-4] + 'output.JPG',
            thermal_image_rgb)




        # copyfile(visible_rgb_path, save_visible_rgb_path + '/' + image_address_split2[-1][0:-4] + 'output.JPG')


        one_shibie_return['save_visible_rgb_path'] = save_visible_rgb_path + '/' + image_address_split2[-1][
                                                                                   0:-4] + 'output.JPG'

        #
        # img2 = np.zeros_like(thermal_image_rgb)

        end1 = time.clock()



        # start2 = time.clock()

        # print(path + '/' + image_address_split_raw + 'raw', 'image_address_split[-1]image_address_split[-1]image_address_split[-1]')

        dji_exe = 'F:/GuanYong/zzb/guanyong_pro/rgbtotiff_exe/utility/bin/windows/release_x64/dji_irp.exe' + ' ' + '-s' + ' ' + str(
            thermal_rgb_path2) + ' ' + '-o' + ' ' + save_thermal_rgb_path + '/' + image_address_split_raw + 'raw' + ' ' + '-a measure   --measurefmt float32'  # .exe文件的路径【例如："E:/Desktop/C++/Debug/Project1.exe"】
        r_v = os.system(dji_exe)
        try:
            img_raw = np.fromfile(save_thermal_rgb_path + '/' + image_address_split_raw + 'raw', dtype='float32')
        except:
            return "image_address Error, con't find image"

        img_raw2 = img_raw.reshape(512, 640)
        # print(path + '/' + image_address_split_raw + 'raw', 'image_address_split_rawimage_address_split_rawimage_address_split_raw')
        img_raw2_t = img_raw2.T

        #
        temperature = img_raw2_t

        os.remove(save_thermal_rgb_path + '/' + image_address_split_raw + 'raw')
        # os.remove(thermal_rgb_path2)
        end2 = time.clock()
        time_list['dji_time'] = end2-end1



        temperature = np.around(temperature, 4)

        # print(temperature, 'temperaturetemperaturetemperaturetemperature')
        s1, s2 = temperature.shape

        # 小于0 等于non
        for i in range(s1):
            for j in range(s2):

                if temperature[i][j] < 0:
                    # print(temperature[i][j])
                    temperature[i][j] = None

        # 参考温度计算

        diff1 = np.zeros((s1 - 1, s2 - 1))
        diff2 = np.zeros((s1 - 1, s2 - 1))
        LST = temperature

        for j in range(s2 - 1):
            for i in range(s1 - 1):
                if (LST[i, j] != None) & (LST[i, j + 1] != None):

                    diff1[i, j] = abs(LST[i, j] - LST[i, j + 1])
                    if diff1[i, j] > 10:  # 正常的影像，相邻2个像素温差是几乎不可能大于10K的。
                        print(diff2[i, j], '大小不同')
                        LST[i, j] = None
                        LST[i, j + 1] = None

                diff2[i, j] = abs(LST[i, j] - LST[i, j + 1])

                if diff2[i, j] > 10:  # 正常的影像，相邻2个像素温差是几乎不可能大于10K的。

                    LST[i, j] = None
                    LST[i + 1, j] = None


        temperature_min1 = np.nanmin(np.nanmin(LST))  # 最低温度。
        # print(temperature_min, '最低温度')

        temp = temperature.reshape([s1 * s2, 1])

        # index_h, index_l = np.where(np.isnan(temp))
        index_h, index_l = np.where(np.isnan(temp) == True)

        temp_dl = np.delete(temp, index_h)

        temp = np.sort(temp_dl)
        # print(temp)
        numtmp = len(temp)

        temperature_min2 = temp[int(0.0001 * numtmp)]  # % 假设最低温度出现在管涌的地方
        temperature_min = (temperature_min1 + temperature_min2) / 2

        # Step2, 潜在官涌区缓冲区
        zones = temperature < temperature_min + temperature_threshold

        # 如果缓冲区的像素超过一半，可能是湖泊或者其他均质地表，直接剔除掉。
        if (sum(sum(zones)) / s1 / s2 > 0.7):

            one_shibie_return['shibie_result'] = 0

            cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-5] + 'output.JPG',
                        thermal_image_rgb)
            # cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-4] + 'output.JPG',
            #             thermal_image_rgb)
            #
            # one_shibie_return['save_thermal_rgb_path'] = save_thermal_rgb_path + '/' + image_address_split[-1][
            #                                                                            0:-5] + 'output.JPG'

            end = time.clock()
            # print('No 管涌运行时间1', end - start)
        # % % 影像分块，识别。
        else:
            one_shibie_return['shibie_result'] = 1
            interval_h = int(s1 / 4)
            interval_l = int(s2 / 4)
            hang_begin = [1, interval_h, interval_h * 2, interval_h * 3]
            hang_end = [interval_h, interval_h * 2, interval_h * 3, s1]

            lie_begin = [1, interval_l, interval_l * 2, interval_l * 3]
            lie_end = [interval_l, interval_l * 2, interval_l * 3, s2]

            # print(hang_begin)
            # print(hang_end)

            for i in range(4):
                for j in range(4):

                    # % 判断区域内是否存在点，
                    hang_index1 = hang_begin[i]
                    hang_index2 = hang_end[i]
                    lie_index1 = lie_begin[j]
                    lie_index2 = lie_end[j]
                    zone_tmp = zones[hang_index1:hang_index2, lie_index1: lie_index2]
                    if (sum(sum(zone_tmp)) == 0):
                        continue
                    # end
                    temperature_zone = temperature[hang_index1:hang_index2, lie_index1: lie_index2]
                    minT = np.nanmin(np.nanmin(temperature_zone))
                    midhang, midlie = np.where(temperature_zone == minT)
                    size1, size2 = temperature_zone.shape
                    up_in, down_in, left_in, right_in, up_out, down_out, left_out, right_out = in_out_zone(midhang,
                                                                                                           midlie,
                                                                                                           size1,
                                                                                                           size2)

                    if type(up_in) == np.ndarray:
                        up_in = up_in.tolist()
                    else:
                        up_in = [up_in]

                    if type(down_in) == np.ndarray:
                        down_in = down_in.tolist()
                    else:
                        down_in = [down_in]

                    if type(left_in) == np.ndarray:
                        left_in = left_in.tolist()
                    else:
                        left_in = [left_in]

                    if type(right_in) == np.ndarray:
                        right_in = right_in.tolist()
                    else:
                        right_in = [right_in]

                    if type(up_out) == np.ndarray:
                        up_out = up_out.tolist()
                    else:
                        up_out = [up_out]

                    if type(down_out) == np.ndarray:
                        down_out = down_out.tolist()
                    else:
                        down_out = [down_out]

                    if type(left_out) == np.ndarray:
                        left_out = left_out.tolist()
                    else:
                        left_out = [left_out]

                    if type(right_out) == np.ndarray:
                        right_out = right_out.tolist()
                    else:
                        right_out = [right_out]

                    # print(up_out, down_out, left_out, right_out, 'up_out')
                    # temperature_inside = np.nanmean(np.nanmean(temperature_zone[up_in:down_in, left_in: right_in]))
                    # temperature_outside = np.nanmean(np.nanmean(temperature[up_out:down_out, left_out: right_out]))

                    temperature_inside = np.nanmean(
                        np.nanmean(temperature_zone[up_in[0]:down_in[0], left_in[0]:right_in[0]]))
                    temperature_outside = np.nanmean(
                        np.nanmean(temperature[abs(up_out[0]):down_out[0], abs(left_out[0]):right_out[0]]))

                    if (temperature_outside - temperature_inside < temperature_difference):
                        zones[hang_index1: hang_index2, lie_index1: lie_index2] = 0
                        continue
                    else:
                        zones[hang_index1: hang_index2, lie_index1: lie_index2] = 0
                        up, down, left, right = flags(hang_index1 + midhang, lie_index1 + midlie, 20, s1, s2)
                        # print(up,down, left,right, 'up: down, left: right')
                        zones[up[0]: down[0], left[0]: right[0]] = 1

            # print(zones.shape, 'zones')
            # print(temperature.shape, 'temperature')
            tmp2 = zones * temperature
            # print(tmp2.shape, 'tmp2')

            ww, hh = np.where((tmp2 < (temperature_min + temperature_threshold2)) & (tmp2 != 0))

            # print(ww, hh)

            tmp2[ww, hh] = 255
            ww1, hh1 = np.where(tmp2 != 255)
            tmp2[ww1, hh1] = 0
            end4 = time.clock()
            time_list['suanfa_time'] = end4-end2
            if (sum(sum(tmp2)) == 0):
                # print('没有管涌2')
                one_shibie_return['shibie_result'] = 0
                cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-5] + 'output.JPG',
                            thermal_image_rgb)



                # one_shibie_return['save_thermal_rgb_path'] = save_thermal_rgb_path + '/' + image_address_split[
                #                                                                                -1][
                #                                                                            0:-5] + 'output.JPG',

                # end = time.clock()
                # print('No 管涌运行时间2', end - start)
            else:
                # print('有管涌')
                # cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-4] + 'output.JPG',
                #             thermal_image_rgb)
                # kernel = np.ones(shape=[16, 16], dtype=np.uint8)  # 通过shape=[3,3]可以改变处理效果
                # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

                img2 = np.zeros([512, 640, 3], dtype=np.uint8)

                img2[:, :, 0] = 0
                img2[:, :, 1] = 255
                img2[:, :, 2] = 255
                # img2 = cv2.imread('D:/guanyongshibie/beiyong/data3/3/regis/thermal_rgb/DJI_20220817100157_0009_T.png')
                # print(thermal_image_rgb.shape, 'thermal_image_rgb形状')
                # print(img2.shape, 'img2x形状')
                # print(ww, hh, 'wwwwwwwhhhhhhhhhh')
                img_ROI2 = cv2.addWeighted(thermal_image_rgb, 1, img2, 0.3, 0)

                img_ROI2[hh, ww] = [0, 0, 255]

                cv2.imwrite(save_thermal_rgb_path + '/' + image_address_split[-1][0:-5] + 'output.JPG', img_ROI2)


                one_shibie_return['save_thermal_rgb_path'] = save_thermal_rgb_path + '/' + image_address_split[-1][
                                                                                           0:-5] + 'output.JPG'
                # print(len(ww), hh)

                # 膨胀，图像变粗
                # img2 = cv2.er(img2, kernel, iterations=1)
                # rows, cols, channels = img1.shape
                # img_ROI1 = img2[0:rows, 0:cols]

                # img_ROI2 = cv2.addWeighted(thermal_image_rgb, 1, img2, 0.3, 0)

                # img_ROI2[ww, hh] = [0, 0, 255]
                # print(len(ww))
                end3 = time.clock()
                time_list['method_time'] = end3-start
                # print('有管涌运行时间', end - start)
                # cv2.imshow('img_ROI2', img_ROI2)
                # cv2.waitKey(0)
        print(time_list)
        return one_shibie_return


@app.get("/")
def read_root():
    return {"Hello": "World"}


#jpg转tiff算法
@app.get("/jpg_to_tiff")
def read_item( image_address:str, tiff_raw_out_address:str):

    tiff_img_name = jpg_to_tiff( image_address, tiff_raw_out_address)
    return  {'tiff_img_name':tiff_img_name}



# 调用配准应用
@app.get("/peizhun")
def read_item( thermal_tiff_img_path:str, thermal_rgb_img_path:str, visible_img_path:str, output_path:str):
    peizhun_ok = peizhun( thermal_tiff_img_path, thermal_rgb_img_path, visible_img_path, output_path)
    return  output_path

temperature_difference = 1.5
temperature_threshold2 =4
temperature_threshold  = 3



#  单张管涌识别服务
@app.get("/one_shibie")
def read_item(thermal_rgb_path:str,  save_thermal_rgb_path:str, visible_rgb_path:str, save_visible_rgb_path:str):
    one_shibie_return = one_shibie(thermal_rgb_path, save_thermal_rgb_path, visible_rgb_path, save_visible_rgb_path, temperature_difference, temperature_threshold2, temperature_threshold)
    return  one_shibie_return








def start_server():
    # 解决windows下点击cmd窗口程序暂停问题
    if platform.system() == "Windows":
        kernel32 = ctypes.windll.kernel32
        kernel32.SetConsoleMode(kernel32.GetStdHandle(-10), 128)

    uvicorn.run(f"{Path(__file__).stem}:app", host="0.0.0.0", port=8000)



if __name__ == "__main__":
    start_server()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565