Mask_rcnn: Error processing image, ValueError: Input image expected to be RGB, RGBA or gray.

Created on 7 May 2018 · 5Comments · Source: matterport/Mask_RCNN

I'm getting this error very intermittently during training (approx. 10 times per 1000 iterations). I have variable size images and masks, so I'm thinking this may be an issue with some of the very large images in my dataset (for example, sizes 5456x3632, 2592x1944, etc.). It continues to train without crashing due to the error, but I'm unsure if there will be any negative consequences later on. I have IMAGE_MAX_DIM=1024.

ERROR:root:Error processing image {'id': 3452, 'source': 'coco', 'path': '/home/docker_user/data/typeb_data/train2018/00001207.jpg', 'width': 5456, 'height': 3632, 'annotations': [{'id': 8215, 'image_id': 3452, 'category_id': 1, 'iscrowd': 0, 'area': 2581761, 'bbox': [2971.0, 657.0, 2217.0, 2258.0], 'segmentation': [[4691.0, 2914.5, 4657.0, 2912.5, 4640.0, 2909.5, 4595.0, 2909.5, 4571.0, 2903.5, 4522.0, 2879.5, 4503.0, 2841.5, 4491.0, 2839.5, 4475.0, 2833.5, 4446.5, 2818.0, 4443.5, 2767.0, 4438.5, 2735.0, 4432.5, 2722.0, 4416.5, 2710.0, 4411.5, 2696.0, 4400.5, 2676.0, 4397.5, 2662.0, 4400.5, 2642.0, 4408.0, 2632.5, 4424.0, 2631.5, 4431.0, 2639.5, 4455.0, 2643.5, 4470.0, 2643.5, 4508.0, 2630.5, 4510.5, 2627.0, 4511.5, 2617.0, 4516.5, 2603.0, 4539.5, 2552.0, 4523.5, 2530.0, 4501.0, 2512.5, 4454.0, 2491.5, 4420.0, 2471.5, 4399.5, 2449.0, 4388.5, 2427.0, 4375.5, 2394.0, 4362.5, 2368.0, 4352.0, 2360.5, 4348.5, 2355.0, 4343.5, 2324.0, 4355.5, 2302.0, 4370.5, 2280.0, 4363.0, 2251.5, 4343.5, 2256.0, 4337.5, 2282.0, 4322.5, 2312.0, 4298.5, 2340.0, 4323.5, 2445.0, 4323.0, 2447.5, 4316.0, 2449.5, 3752.0, 2482.5, 3726.0, 2481.5, 3725.5, 2475.0, 3729.5, 2470.0, 3795.5, 2398.0, 3817.5, 2370.0, 3819.5, 2347.0, 3819.5, 2338.0, 3817.5, 2331.0, 3808.0, 2324.5, 3786.0, 2319.5, 3731.5, 2383.0, 3698.5, 2411.0, 3692.5, 2460.0, 3699.5, 2481.0, 3708.5, 2486.0, 3717.5, 2504.0, 3715.5, 2533.0, 3717.5, 2566.0, 3715.5, 2579.0, 3714.5, 2614.0, 3710.5, 2630.0, 3669.5, 2660.0, 3658.0, 2684.5, 3627.0, 2707.5, 3589.0, 2727.5, 3548.0, 2758.5, 3481.0, 2758.5, 3381.0, 2704.5, 3343.5, 2657.0, 3318.0, 2632.5, 3296.0, 2617.5, 3251.0, 2623.5, 3196.0, 2619.5, 3161.0, 2606.5, 3119.0, 2583.5, 3094.5, 2547.0, 3051.5, 2510.0, 3027.5, 2478.0, 3009.5, 2442.0, 3000.5, 2408.0, 2977.5, 2379.0, 2972.5, 2333.0, 2970.5, 2262.0, 2973.5, 2150.0, 2996.0, 2127.5, 3021.5, 2108.0, 3037.5, 2080.0, 3045.5, 2059.0, 3058.0, 2039.5, 3095.0, 2021.5, 3116.0, 2004.5, 3139.0, 1963.5, 3204.5, 1931.0, 3200.5, 1922.0, 3185.5, 1910.0, 3192.5, 1882.0, 3203.5, 1859.0, 3208.5, 1853.0, 3241.5, 1817.0, 3266.0, 1795.5, 3281.5, 1779.0, 3284.5, 1748.0, 3294.5, 1730.0, 3336.0, 1710.5, 3351.0, 1706.5, 3392.0, 1683.5, 3450.0, 1671.5, 3448.5, 1651.0, 3455.5, 1631.0, 3480.0, 1618.5, 3561.0, 1609.5, 3584.0, 1595.5, 3642.0, 1595.5, 3665.5, 1593.0, 3663.0, 1574.5, 3652.0, 1574.5, 3632.0, 1580.5, 3612.0, 1570.5, 3584.5, 1543.0, 3564.5, 1502.0, 3545.5, 1477.0, 3540.5, 1465.0, 3519.5, 1403.0, 3499.5, 1350.0, 3479.5, 1286.0, 3475.5, 1208.0, 3482.5, 1080.0, 3518.5, 940.0, 3556.5, 887.0, 3617.5, 816.0, 3699.0, 751.5, 3815.0, 697.5, 3909.0, 669.5, 4044.0, 664.5, 4087.0, 656.5, 4171.0, 664.5, 4261.0, 682.5, 4266.0, 684.5, 4354.0, 748.5, 4426.5, 823.0, 4504.5, 1012.0, 4522.5, 1098.0, 4517.5, 1195.0, 4480.5, 1320.0, 4391.5, 1451.0, 4354.5, 1513.0, 4347.5, 1531.0, 4305.5, 1662.0, 4328.0, 1669.5, 4390.0, 1682.5, 4433.0, 1697.5, 4477.0, 1725.5, 4500.0, 1754.5, 4524.5, 1772.0, 4538.5, 1851.0, 4574.0, 1871.5, 4661.5, 1963.0, 4672.5, 1975.0, 4701.5, 2025.0, 4728.0, 2035.5, 4764.0, 2059.5, 4820.0, 2100.5, 4858.5, 2147.0, 4886.0, 2187.5, 4928.0, 2208.5, 4930.5, 2211.0, 4972.0, 2273.5, 4996.0, 2279.5, 5020.0, 2282.5, 5102.0, 2338.5, 5156.5, 2406.0, 5183.5, 2459.0, 5187.5, 2496.0, 5157.5, 2543.0, 5145.5, 2587.0, 5133.5, 2603.0, 5124.5, 2628.0, 5122.0, 2631.5, 5090.0, 2648.5, 5077.0, 2653.5, 5058.0, 2686.5, 5037.0, 2700.5, 5011.0, 2724.5, 4989.0, 2741.5, 4983.0, 2744.5, 4947.0, 2751.5, 4919.5, 2792.0, 4853.0, 2868.5, 4795.0, 2895.5, 4771.0, 2902.5, 4737.0, 2909.5, 4709.0, 2903.5, 4691.0, 2914.5]], 'width': 5456, 'height': 3632}, {'id': 8216, 'image_id': 3452, 'category_id': 2, 'iscrowd': 0, 'area': 2590712, 'bbox': [2968.0, 658.0, 2218.0, 2256.0], 'segmentation': [[4669.0, 2913.5, 4641.0, 2909.5, 4588.0, 2909.5, 4553.0, 2901.5, 4527.5, 2874.0, 4510.0, 2848.5, 4498.0, 2839.5, 4470.0, 2831.5, 4459.5, 2812.0, 4439.5, 2800.0, 4437.5, 2785.0, 4437.5, 2728.0, 4419.5, 2718.0, 4400.5, 2677.0, 4390.5, 2663.0, 4402.0, 2642.5, 4424.0, 2631.5, 4436.0, 2644.5, 4439.0, 2644.5, 4469.0, 2639.5, 4512.0, 2628.5, 4517.5, 2617.0, 4524.5, 2582.0, 4543.5, 2558.0, 4532.0, 2539.5, 4507.0, 2528.5, 4481.0, 2508.5, 4432.0, 2484.5, 4412.5, 2462.0, 4400.5, 2440.0, 4381.5, 2414.0, 4370.5, 2373.0, 4368.0, 2368.5, 4348.5, 2355.0, 4343.5, 2336.0, 4344.5, 2316.0, 4354.5, 2298.0, 4359.5, 2285.0, 4362.5, 2263.0, 4362.5, 2259.0, 4360.0, 2255.5, 4342.5, 2254.0, 4344.5, 2264.0, 4334.5, 2300.0, 4311.5, 2322.0, 4303.5, 2338.0, 4322.5, 2443.0, 4322.0, 2445.5, 4314.0, 2446.5, 3715.0, 2484.5, 3718.5, 2479.0, 3789.5, 2400.0, 3804.5, 2376.0, 3817.5, 2359.0, 3821.5, 2346.0, 3811.0, 2334.5, 3790.0, 2319.5, 3720.0, 2385.5, 3696.5, 2412.0, 3697.5, 2437.0, 3695.5, 2477.0, 3702.5, 2491.0, 3719.5, 2510.0, 3719.5, 2538.0, 3714.5, 2547.0, 3719.5, 2558.0, 3719.5, 2562.0, 3715.5, 2581.0, 3711.5, 2637.0, 3669.0, 2661.5, 3649.0, 2698.5, 3631.0, 2707.5, 3603.0, 2717.5, 3592.0, 2727.5, 3560.0, 2747.5, 3524.0, 2761.5, 3487.0, 2753.5, 3456.0, 2752.5, 3402.0, 2722.5, 3369.5, 2697.0, 3343.5, 2641.0, 3332.5, 2626.0, 3308.0, 2612.5, 3287.0, 2613.5, 3252.0, 2619.5, 3206.0, 2624.5, 3200.0, 2624.5, 3165.0, 2617.5, 3127.0, 2581.5, 3099.0, 2560.5, 3079.0, 2550.5, 3056.0, 2536.5, 3032.5, 2495.0, 3010.5, 2472.0, 2999.5, 2420.0, 2972.5, 2340.0, 2972.5, 2303.0, 2967.5, 2229.0, 2976.5, 2154.0, 2983.5, 2140.0, 3012.5, 2106.0, 3017.0, 2101.5, 3041.5, 2089.0, 3039.5, 2080.0, 3045.5, 2069.0, 3050.5, 2048.0, 3069.0, 2028.5, 3105.0, 2020.5, 3129.0, 1978.5, 3157.0, 1960.5, 3179.0, 1943.5, 3206.5, 1928.0, 3199.5, 1923.0, 3191.5, 1913.0, 3186.5, 1894.0, 3199.5, 1877.0, 3206.5, 1862.0, 3235.5, 1824.0, 3272.5, 1787.0, 3275.5, 1770.0, 3289.0, 1739.5, 3305.0, 1729.5, 3344.0, 1711.5, 3360.0, 1698.5, 3371.0, 1692.5, 3399.0, 1680.5, 3436.0, 1669.5, 3444.0, 1669.5, 3461.0, 1621.5, 3509.0, 1612.5, 3551.0, 1611.5, 3569.0, 1606.5, 3586.0, 1596.5, 3661.5, 1593.0, 3659.0, 1568.5, 3632.0, 1583.5, 3605.5, 1566.0, 3577.5, 1524.0, 3552.5, 1491.0, 3508.5, 1387.0, 3492.5, 1355.0, 3476.5, 1278.0, 3476.5, 1218.0, 3473.5, 1161.0, 3502.5, 979.0, 3504.5, 973.0, 3510.5, 964.0, 3599.0, 835.5, 3786.0, 705.5, 3867.0, 673.5, 4048.0, 662.5, 4088.0, 661.5, 4125.0, 657.5, 4153.0, 660.5, 4187.0, 673.5, 4287.0, 695.5, 4317.0, 715.5, 4346.0, 739.5, 4421.5, 810.0, 4466.5, 885.0, 4496.5, 941.0, 4516.5, 985.0, 4529.5, 1155.0, 4505.5, 1248.0, 4492.5, 1323.0, 4475.0, 1333.5, 4465.0, 1338.5, 4455.0, 1340.5, 4452.5, 1343.0, 4402.5, 1444.0, 4385.5, 1462.0, 4371.5, 1503.0, 4352.5, 1527.0, 4342.5, 1568.0, 4298.5, 1669.0, 4333.0, 1669.5, 4374.0, 1672.5, 4408.0, 1684.5, 4448.0, 1704.5, 4500.0, 1741.5, 4544.5, 1788.0, 4545.5, 1847.0, 4547.5, 1850.0, 4564.0, 1870.5, 4609.5, 1897.0, 4710.5, 2035.0, 4780.0, 2068.5, 4832.0, 2110.5, 4835.5, 2114.0, 4883.5, 2196.0, 4976.0, 2268.5, 4979.0, 2270.5, 5025.0, 2274.5, 5103.0, 2328.5, 5146.5, 2378.0, 5165.5, 2427.0, 5185.5, 2464.0, 5179.5, 2512.0, 5175.0, 2518.5, 5159.5, 2531.0, 5155.5, 2571.0, 5153.5, 2578.0, 5139.5, 2600.0, 5092.5, 2649.0, 5054.0, 2692.5, 4998.0, 2735.5, 4946.0, 2756.5, 4931.5, 2775.0, 4913.5, 2803.0, 4875.0, 2839.5, 4839.0, 2866.5, 4802.0, 2882.5, 4777.0, 2895.5, 4746.0, 2905.5, 4695.0, 2905.5, 4669.0, 2913.5]], 'width': 5456, 'height': 3632}]}
Traceback (most recent call last):
  File "/root/anaconda3/lib/python3.6/site-packages/mask_rcnn-2.1-py3.6.egg/mrcnn/model.py", line 1695, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "/root/anaconda3/lib/python3.6/site-packages/mask_rcnn-2.1-py3.6.egg/mrcnn/model.py", line 1209, in load_image_gt
    image = dataset.load_image(image_id)
  File "/root/anaconda3/lib/python3.6/site-packages/mask_rcnn-2.1-py3.6.egg/mrcnn/utils.py", line 367, in load_image
    image = skimage.color.gray2rgb(image)
  File "/root/anaconda3/lib/python3.6/site-packages/skimage/color/colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

Source

austinmw

Most helpful comment

@austinmw , I found that ''skimage.io.imread'' cannot correctly output large images' shape(for example, sizes 4000*6000 ), but ''cv2.imread'' can.
And in in mrcnn/utils.py , there is ''skimage.io.imread'', maybe it caused "ValueError: Input image expected to be RGB, RGBA or gray."

nemonameless on 9 May 2018

🎉2 👍1

All 5 comments

nemonameless on 9 May 2018

🎉2 👍1

Hello, I have encountered the same problem, can tell me how to solve it, thank you.

TanChong1112 on 24 Jul 2019

I set 20 epoch, each epoch 20,000 steps, but when I run the tenth epoch, I get an error, showing ValueError:Input image expected to RGB,RGBA or gray.and terminated the network training.

TanChong1112 on 24 Jul 2019

Does anyone know how to handle this error? I get the error with image size 'width': 640, 'height': 480. So it happens to smaller images too.

Starting at epoch 0. LR=0.001

Checkpoint Path: ./img_set/logs/k01\k1_gpu1_epoch40_step8001_20200226T1210\mask_rcnn_k1_gpu1_epoch40_step8001__{epoch:04d}.h5
Selecting layers to train
fpn_c5p5               (Conv2D)
fpn_c4p4               (Conv2D)
fpn_c3p3               (Conv2D)
fpn_c2p2               (Conv2D)
fpn_p5                 (Conv2D)
fpn_p2                 (Conv2D)
fpn_p3                 (Conv2D)
fpn_p4                 (Conv2D)
In model:  rpn_model
    rpn_conv_shared        (Conv2D)
    rpn_class_raw          (Conv2D)
    rpn_bbox_pred          (Conv2D)
mrcnn_mask_conv1       (TimeDistributed)
mrcnn_mask_bn1         (TimeDistributed)
mrcnn_mask_conv2       (TimeDistributed)
mrcnn_mask_bn2         (TimeDistributed)
mrcnn_class_conv1      (TimeDistributed)
mrcnn_class_bn1        (TimeDistributed)
mrcnn_mask_conv3       (TimeDistributed)
mrcnn_mask_bn3         (TimeDistributed)
mrcnn_class_conv2      (TimeDistributed)
mrcnn_class_bn2        (TimeDistributed)
mrcnn_mask_conv4       (TimeDistributed)
mrcnn_mask_bn4         (TimeDistributed)
mrcnn_bbox_fc          (TimeDistributed)
mrcnn_mask_deconv      (TimeDistributed)
mrcnn_class_logits     (TimeDistributed)
mrcnn_mask             (TimeDistributed)

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\tensorflow\python\ops\gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "

Epoch 1/30
2163/8001 [=======>......................] - ETA: 42:43 - loss: 1.2738 - rpn_class_loss: 0.0385 - rpn_bbox_loss: 0.6715 - mrcnn_class_loss: 0.0863 - mrcnn_bbox_loss: 0.2515 - mrcnn_mask_loss: 0.2260
ERROR:root:Error processing image {'id': 5584, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1004255.jpg', 'width': 2592, 'height': 1944, 'annotations': [{'id': 26685, 'image_id': 5584, 'category_id': 1, 'segmentation': [[...]], 'area': 34080, 'bbox': [227, 240, 96, 355], 'iscrowd': 0}, {'id': 26686, 'image_id': 5584, 'category_id': 1, 'segmentation': [[...]], 'area': 148000, 'bbox': [663, 662, 296, 500], 'iscrowd': 0}, {'id': 26687, 'image_id': 5584, 'category_id': 1, 'segmentation': [[...]], 'area': 141230, 'bbox': [1113, 801, 290, 487], 'iscrowd': 0}, {'id': 26688, 'image_id': 5584, 'category_id': 1, 'segmentation': [[...]], 'area': 128506, 'bbox': [1594, 835, 274, 469], 'iscrowd': 0}, {'id': 26689, 'image_id': 5584, 'category_id': 1, 'segmentation': [[...]], 'area': 110004, 'bbox': [1998, 849, 267, 412], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

2379/8001 [=======>......................] - ETA: 41:07 - loss: 1.2606 - rpn_class_loss: 0.0377 - rpn_bbox_loss: 0.6646 - mrcnn_class_loss: 0.0884 - mrcnn_bbox_loss: 0.2461 - mrcnn_mask_loss: 0.2238
C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\PIL\TiffImagePlugin.py:756: UserWarning: Corrupt EXIF data.  Expecting to read 4 bytes but only got 2. 
  warnings.warn(str(msg))

2543/8001 [========>.....................] - ETA: 39:44 - loss: 1.2472 - rpn_class_loss: 0.0370 - rpn_bbox_loss: 0.6582 - mrcnn_class_loss: 0.0900 - mrcnn_bbox_loss: 0.2403 - mrcnn_mask_loss: 0.2216
ERROR:root:Error processing image {'id': 3802, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1001872.jpg', 'width': 640, 'height': 480, 'annotations': [{'id': 18241, 'image_id': 3802, 'category_id': 1, 'segmentation': [[...]], 'area': 16610, 'bbox': [175, 41, 110, 151], 'iscrowd': 0}, {'id': 18242, 'image_id': 3802, 'category_id': 1, 'segmentation': [[...]], 'area': 18096, 'bbox': [258, 123, 116, 156], 'iscrowd': 0}, {'id': 18243, 'image_id': 3802, 'category_id': 1, 'segmentation': [[...]], 'area': 17589, 'bbox': [316, 224, 123, 143], 'iscrowd': 0}, {'id': 18244, 'image_id': 3802, 'category_id': 1, 'segmentation': [[...]], 'area': 12198, 'bbox': [379, 321, 107, 114], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

2826/8001 [=========>....................] - ETA: 37:43 - loss: 1.2293 - rpn_class_loss: 0.0358 - rpn_bbox_loss: 0.6491 - mrcnn_class_loss: 0.0922 - mrcnn_bbox_loss: 0.2329 - mrcnn_mask_loss: 0.2193 ETA: 39:19 - loss: 1.2435 - rpn_class_loss: 0.0367 - rpn_bbox_loss: 0.6557 - mrcnn_class_loss: 0. - ETA: 39:10 - loss: 1.2421 - rpn_class_loss: 0.0367 - rpn_bbox - ETA: 38:19 - lo
ERROR:root:Error processing image {'id': 4319, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1002564.jpg', 'width': 3840, 'height': 2160, 'annotations': [{'id': 20662, 'image_id': 4319, 'category_id': 1, 'segmentation': [[...]], 'area': 170996, 'bbox': [1415, 443, 394, 434], 'iscrowd': 0}, {'id': 20663, 'image_id': 4319, 'category_id': 1, 'segmentation': [[...]], 'area': 190953, 'bbox': [1636, 795, 433, 441], 'iscrowd': 0}, {'id': 20664, 'image_id': 4319, 'category_id': 1, 'segmentation': [[...]], 'area': 218088, 'bbox': [1742, 1244, 466, 468], 'iscrowd': 0}, {'id': 20665, 'image_id': 4319, 'category_id': 1, 'segmentation': [[...]], 'area': 129204, 'bbox': [1927, 1679, 388, 333], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

3594/8001 [============>.................] - ETA: 32:10 - loss: 1.1909 - rpn_class_loss: 0.0334 - rpn_bbox_loss: 0.6261 - mrcnn_class_loss: 0.1019 - mrcnn_bbox_loss: 0.2162 - mrcnn_mask_loss: 0.2134
ERROR:root:Error processing image {'id': 2824, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1000532.jpg', 'width': 1024, 'height': 768, 'annotations': [{'id': 13636, 'image_id': 2824, 'category_id': 1, 'segmentation': [[...]], 'area': 78769, 'bbox': [248, 540, 347, 227], 'iscrowd': 0}, {'id': 13637, 'image_id': 2824, 'category_id': 1, 'segmentation': [[...]], 'area': 40172, 'bbox': [177, 255, 166, 242], 'iscrowd': 0}, {'id': 13638, 'image_id': 2824, 'category_id': 1, 'segmentation': [[...]], 'area': 45312, 'bbox': [365, 175, 177, 256], 'iscrowd': 0}, {'id': 13639, 'image_id': 2824, 'category_id': 1, 'segmentation': [[...]], 'area': 43065, 'bbox': [589, 174, 165, 261], 'iscrowd': 0}, {'id': 13640, 'image_id': 2824, 'category_id': 1, 'segmentation': [[...]], 'area': 43520, 'bbox': [795, 316, 160, 272], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

3657/8001 [============>.................] - ETA: 31:42 - loss: 1.1884 - rpn_class_loss: 0.0332 - rpn_bbox_loss: 0.6244 - mrcnn_class_loss: 0.1023 - mrcnn_bbox_loss: 0.2153 - mrcnn_mask_loss: 0.2131
ERROR:root:Error processing image {'id': 2457, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1000047.jpg', 'width': 1024, 'height': 768, 'annotations': [{'id': 11928, 'image_id': 2457, 'category_id': 1, 'segmentation': [[...]], 'area': 29106, 'bbox': [123, 255, 154, 189], 'iscrowd': 0}, {'id': 11929, 'image_id': 2457, 'category_id': 1, 'segmentation': [[...]], 'area': 35322, 'bbox': [325, 250, 174, 203], 'iscrowd': 0}, {'id': 11930, 'image_id': 2457, 'category_id': 1, 'segmentation': [[...]], 'area': 33234, 'bbox': [541, 247, 174, 191], 'iscrowd': 0}, {'id': 11931, 'image_id': 2457, 'category_id': 1, 'segmentation': [[...]], 'area': 21888, 'bbox': [809, 309, 144, 152], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

5817/8001 [====================>.........] - ETA: 15:44 - loss: 1.1038 - rpn_class_loss: 0.0285 - rpn_bbox_loss: 0.5633 - mrcnn_class_loss: 0.1214 - mrcnn_bbox_loss: 0.1878 - mrcnn_mask_loss: 0.2027
ERROR:root:Error processing image {'id': 5649, 'source': 'coco_like', 'path': 'C:\\work\\my_dir\\images\\img_1004344.jpg', 'width': 480, 'height': 339, 'annotations': [{'id': 26992, 'image_id': 5649, 'category_id': 1, 'segmentation': [[...]], 'area': 7176, 'bbox': [277, 129, 69, 104], 'iscrowd': 0}, {'id': 26993, 'image_id': 5649, 'category_id': 1, 'segmentation': [[...]], 'area': 6432, 'bbox': [191, 158, 67, 96], 'iscrowd': 0}, {'id': 26994, 'image_id': 5649, 'category_id': 1, 'segmentation': [[...]], 'area': 6596, 'bbox': [120, 132, 68, 97], 'iscrowd': 0}, {'id': 26995, 'image_id': 5649, 'category_id': 1, 'segmentation': [[...]], 'area': 4920, 'bbox': [61, 163, 60, 82], 'iscrowd': 0}]}
Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1704, in data_generator
    use_mini_mask=config.USE_MINI_MASK)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py", line 1218, in load_image_gt
    image = dataset.load_image(image_id)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py", line 371, in load_image
    image = skimage.color.gray2rgb(image)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py", line 862, in gray2rgb
    raise ValueError("Input image expected to be RGB, RGBA or gray.")
ValueError: Input image expected to be RGB, RGBA or gray.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-7896f54e788b> in <module>()
      9             learning_rate=config.LEARNING_RATE,
     10             epochs=30,
---> 11             layers='heads')
     12 end_train = time.time()
     13 minutes = round((end_train - start_train) / 60, 2)

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py in train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation)
   2350             max_queue_size=100,
   2351             workers=workers,
-> 2352             use_multiprocessing=True,
   2353         )
   2354         self.epoch = max(self.epoch, epochs)

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1413             use_multiprocessing=use_multiprocessing,
   1414             shuffle=shuffle,
-> 1415             initial_epoch=initial_epoch)
   1416 
   1417     @interfaces.legacy_generator_methods_support

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    175             batch_index = 0
    176             while steps_done < steps_per_epoch:
--> 177                 generator_output = next(output_generator)
    178 
    179                 if not hasattr(generator_output, '__len__'):

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py in data_generator(dataset, config, shuffle, augment, augmentation, random_rois, batch_size, detection_targets)
   1702                 load_image_gt(dataset, config, image_id, augment=augment,
   1703                               augmentation=augmentation,
-> 1704                               use_mini_mask=config.USE_MINI_MASK)
   1705 
   1706             # Skip images that have no instances. This can happen in cases

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\model.py in load_image_gt(dataset, config, image_id, augment, augmentation, use_mini_mask)
   1216     """
   1217     # Load image and mask
-> 1218     image = dataset.load_image(image_id)
   1219     mask, class_ids = dataset.load_mask(image_id)
   1220     original_shape = image.shape

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\mrcnn\utils.py in load_image(self, image_id)
    369         # If grayscale. Convert to RGB for consistency.
    370         if image.ndim != 3:
--> 371             image = skimage.color.gray2rgb(image)
    372         # If has an alpha channel, remove it for consistency
    373         if image.shape[-1] == 4:

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\skimage\color\colorconv.py in gray2rgb(image, alpha)
    860 
    861     else:
--> 862         raise ValueError("Input image expected to be RGB, RGBA or gray.")
    863 
    864 grey2rgb = gray2rgb

ValueError: Input image expected to be RGB, RGBA or gray.

iidashu on 27 Feb 2020

OK. I opened all the images with openCv and exported as a new set. All the error above (including Corrupt EXIF data) is gone now.

I still have no idea what had caused the error tho. OpenCv adjusted color spaces or erased some of the unnecessary data in the process, maybe. Size in memory reduced 10 - 20%. The file names and resolutions are same.

iidashu on 28 Feb 2020

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