bbox.py无法过编译 windows10系统

13120678025 commented 4 years ago

Using TensorFlow backend. Traceback (most recent call last): File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\demo.py", line 8, in import model File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\model.py", line 15, in from ctpn.text_detect import text_detect File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\text_detect.py", line 3, in from .ctpn.detectors import TextDetector File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\ctpn\detectors.py", line 10, in from ..lib.fast_rcnn.nms_wrapper import nms File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\lib__init.py", line 1, in from . import fast_rcnn File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\lib\fast_rcnn__init__.py", line 2, in from . import nms_wrapper File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\lib\fast_rcnn\nms_wrapper.py", line 2, in from ..utils.cython_nms import nms as cython_nms File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\lib\utils\init__.py", line 1, in from . import bbox File "C:\Users\WuDongLin\Desktop\图片对比\OCR_DeepLearning\ctpn\lib\utils\bbox.py", line 9 cimport numpy as np ^ SyntaxError: invalid syntax Press any key to continue . . .

yipenglinoe commented 4 years ago

同样的问题

QQ2737499951 commented 4 years ago

编译 windows10系统我已经解决，可加群755860371 共同研究

LycsHub commented 4 years ago

你好,请问一下是如何解决的?

lmw0320 commented 4 years ago

楼上那个群，还要付费加群。。晕死。。

lmw0320 commented 4 years ago

我把bbox.pyx的内容调整了下，另存为bbox.py文件，就可以调用了。。只是不知道这样更改有没错： `import numpy as np def bbox_overlaps(boxes,query_boxes): """ Parameters

boxes: (N, 4) ndarray of float
query_boxes: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
N = boxes.shape[0]
K = query_boxes.shape[0]
overlaps = np.zeros((N, K), dtype=np.float32)
for k in range(K):
    box_area = (
        (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
        (query_boxes[k, 3] - query_boxes[k, 1] + 1)
    )
    for n in range(N):
        iw = (
            min(boxes[n, 2], query_boxes[k, 2]) -
            max(boxes[n, 0], query_boxes[k, 0]) + 1
        )
        if iw > 0:
            ih = (
                min(boxes[n, 3], query_boxes[k, 3]) -
                max(boxes[n, 1], query_boxes[k, 1]) + 1
            )
            if ih > 0:
                ua = float(
                    (boxes[n, 2] - boxes[n, 0] + 1) *
                    (boxes[n, 3] - boxes[n, 1] + 1) +
                    box_area - iw * ih
                )
                overlaps[n, k] = iw * ih / ua
return overlaps

def bbox_intersections(boxes, query_boxes): """ For each query box compute the intersection ratio covered by boxes

Parameters
----------
boxes: (N, 4) ndarray of float
query_boxes: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray of intersec between boxes and query_boxes
"""
N = boxes.shape[0]
K = query_boxes.shape[0]
intersec = np.zeros((N, K), dtype=np.float32)
for k in range(K):
    box_area = (
        (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
        (query_boxes[k, 3] - query_boxes[k, 1] + 1)
    )
    for n in range(N):
        iw = (
            min(boxes[n, 2], query_boxes[k, 2]) -
            max(boxes[n, 0], query_boxes[k, 0]) + 1
        )
        if iw > 0:
            ih = (
                min(boxes[n, 3], query_boxes[k, 3]) -
                max(boxes[n, 1], query_boxes[k, 1]) + 1
            )
            if ih > 0:
                intersec[n, k] = iw * ih / box_area
return intersec`

lmw0320 commented 4 years ago

同理，cpython_nms.pyx文件也被我修改了下，修改后的完整内容如下： `import numpy as np def max(a, b): return a if a >= b else b

def min(a, b): return a if a <= b else b

def nms(dets, thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) (y2 - y1 + 1) order = scores.argsort()[::-1] ndets = dets.shape[0] suppressed = np.zeros((ndets), dtype=np.int) keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w h ovr = inter / (iarea + areas[j] - inter) if ovr >= thresh: suppressed[j] = 1 return keep

def nms_new(dets, thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) (y2 - y1 + 1) order = scores.argsort()[::-1] ndets = dets.shape[0] suppressed = np.zeros((ndets), dtype=np.int) keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w h ovr = inter / (iarea + areas[j] - inter) ovr1 = inter / iarea ovr2 = inter / areas[j] if ovr >= thresh or ovr1 > 0.95 or ovr2 > 0.95: suppressed[j] = 1 return keep`

QiTianDaShengDaShi commented 3 years ago

我也遇到了同样的问题

QiTianDaShengDaShi commented 3 years ago

File "E:\python_project\1229_OCR_detection\1229_OCR_detection_V110_Classify_CTPN_CRNN\CHINESE_OCR_master\ctpn\lib\utils\bbox.py", line 9 cimport numpy as np ^ SyntaxError: invalid syntax

QiTianDaShengDaShi commented 3 years ago

只是不知道这样更改有没错： `import numpy as np def bbox_overlaps（boxes，query_boxes）： “”“ 参数

框：（N，4）浮点数 ndquery_boxes：（K，4）浮点数ndarray 返回

重叠：（N，K）框间的重叠数ndarray和query_boxes “”” N = boxes.shape [0] K = query_boxes.shape [0] 重叠= np.zeros（（N，K），D型细胞= np.float32）对于k在范围（K）： box_area =（（query_boxes [k，2]-query_boxes [k，0] + 1）（query_boxes [k，3]-query_boxes [k，1] + 1））对于范围（N）中的n： iw =（ min（boxes [n，2]，query_boxes [k，2]）- max（boxes [n，0]，query_boxes [k，0]）+ 1 ）如果iw> 0： ih =（ min（boxes [n， 3]，query_boxes [k，3]）- max（boxes [n，1]，query_boxes [k，1]）+ 1 ）如果ih> 0： ua = float（（boxes [n，2] -box [n ，0] + 1）（boxes [n，3] -boxs [n，1] +1）+ box_area-iw ih ）重叠[n，k] = iw ih / ua 返回重叠

def bbox_intersections（boxes，query_boxes）： “”“ 对于每个查询框，计算框所覆盖的相交率

参数

框：（N，4）浮点数 ndquery_boxes的ndarray：（K，4）浮点数ndarray的返回

重叠：（N，K）框和query_boxes之间的相交的ndarray “”“ N = box.shape [0] K = query_boxes。 shape [0] intersec = np.zeros（（N，K），dtype = np.float32）对于范围（K）中的k： box_area =（（query_boxes [k，2]-query_boxes [k，0] + 1）（query_boxes [K，3] - query_boxes [K，1] + 1））在范围（N N）： IW =（ min（boxes [n，2]，query_boxes [k，2]）- max（boxes [n，0]，query_boxes [k，0]）+ 1 ）如果iw> 0： ih =（ min（boxes [n， 3]，query_boxes [k，3]）- max（boxes [n，1]，query_boxes [k，1]）+ 1 ）如果ih> 0： intersec [n，k] = iw ih / box_area 返回intersec`

老哥，有缩进版本吗

QiTianDaShengDaShi commented 3 years ago

我把bbox.pyx的内容调整了下，另存为bbox.py文件，就可以调用了。。只是不知道这样更改有没错： `import numpy as np def bbox_overlaps(boxes,query_boxes): """ Parameters

boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns

overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ N = boxes.shape[0] K = query_boxes.shape[0] overlaps = np.zeros((N, K), dtype=np.float32) for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float( (boxes[n, 2] - boxes[n, 0] + 1) (boxes[n, 3] - boxes[n, 1] + 1) + box_area - iw ih ) overlaps[n, k] = iw ih / ua return overlaps

def bbox_intersections(boxes, query_boxes): """ For each query box compute the intersection ratio covered by boxes

Parameters

boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns

overlaps: (N, K) ndarray of intersec between boxes and query_boxes """ N = boxes.shape[0] K = query_boxes.shape[0] intersec = np.zeros((N, K), dtype=np.float32) for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: intersec[n, k] = iw ih / box_area return intersec`

老哥，已经成功，感谢，上面那个让加群还收费的人真是让人可笑，笑掉大牙。

1Dreamer666 commented 1 year ago

我把bbox.pyx的内容调整了下，另存为bbox.py文件，就可以调用了。。只是不知道这样更改有没错： `import numpy as np def bbox_overlaps(boxes,query_boxes): """ Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ N = boxes.shape[0] K = query_boxes.shape[0] overlaps = np.zeros((N, K), dtype=np.float32) for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float( (boxes[n, 2] - boxes[n, 0] + 1) (boxes[n, 3] - boxes[n, 1] + 1) + box_area - iw ih ) overlaps[n, k] = iw ih / ua return overlaps

def bbox_intersections(boxes, query_boxes): """ For each query box compute the intersection ratio covered by boxes ---------- Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of intersec between boxes and query_boxes """ N = boxes.shape[0] K = query_boxes.shape[0] intersec = np.zeros((N, K), dtype=np.float32) for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: intersec[n, k] = iw ih / box_area return intersec`

老哥太感谢了！这就是开源精神！

QQ2737499951 commented 1 year ago

这是来自AAAAAAAAAAAAAA

xiaofengShi / CHINESE-OCR