feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
This commit is contained in:
wangzhengyang
333
3rdparty/opencv-4.5.4/samples/dnn/tf_text_graph_common.py
vendored
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333
3rdparty/opencv-4.5.4/samples/dnn/tf_text_graph_common.py
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def tokenize(s):
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tokens = []
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token = ""
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isString = False
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isComment = False
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for symbol in s:
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isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
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if isComment:
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continue
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if symbol == ' ' or symbol == '\t' or symbol == '\r' or symbol == '\'' or \
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symbol == '\n' or symbol == ':' or symbol == '\"' or symbol == ';' or \
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symbol == ',':
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if (symbol == '\"' or symbol == '\'') and isString:
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tokens.append(token)
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token = ""
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else:
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if isString:
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token += symbol
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elif token:
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tokens.append(token)
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token = ""
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isString = (symbol == '\"' or symbol == '\'') ^ isString
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elif symbol == '{' or symbol == '}' or symbol == '[' or symbol == ']':
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if token:
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tokens.append(token)
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token = ""
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tokens.append(symbol)
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else:
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token += symbol
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if token:
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tokens.append(token)
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return tokens
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def parseMessage(tokens, idx):
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msg = {}
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assert(tokens[idx] == '{')
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isArray = False
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while True:
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if not isArray:
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idx += 1
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if idx < len(tokens):
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fieldName = tokens[idx]
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else:
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return None
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if fieldName == '}':
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break
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idx += 1
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fieldValue = tokens[idx]
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if fieldValue == '{':
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embeddedMsg, idx = parseMessage(tokens, idx)
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if fieldName in msg:
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msg[fieldName].append(embeddedMsg)
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else:
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msg[fieldName] = [embeddedMsg]
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elif fieldValue == '[':
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isArray = True
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elif fieldValue == ']':
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isArray = False
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else:
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if fieldName in msg:
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msg[fieldName].append(fieldValue)
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else:
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msg[fieldName] = [fieldValue]
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return msg, idx
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def readTextMessage(filePath):
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if not filePath:
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return {}
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with open(filePath, 'rt') as f:
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content = f.read()
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tokens = tokenize('{' + content + '}')
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msg = parseMessage(tokens, 0)
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return msg[0] if msg else {}
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def listToTensor(values):
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if all([isinstance(v, float) for v in values]):
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dtype = 'DT_FLOAT'
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field = 'float_val'
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elif all([isinstance(v, int) for v in values]):
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dtype = 'DT_INT32'
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field = 'int_val'
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else:
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raise Exception('Wrong values types')
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msg = {
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'tensor': {
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'dtype': dtype,
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'tensor_shape': {
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'dim': {
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'size': len(values)
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}
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}
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}
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}
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msg['tensor'][field] = values
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return msg
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def addConstNode(name, values, graph_def):
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node = NodeDef()
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node.name = name
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node.op = 'Const'
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node.addAttr('value', values)
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graph_def.node.extend([node])
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def addSlice(inp, out, begins, sizes, graph_def):
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beginsNode = NodeDef()
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beginsNode.name = out + '/begins'
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beginsNode.op = 'Const'
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beginsNode.addAttr('value', begins)
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graph_def.node.extend([beginsNode])
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sizesNode = NodeDef()
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sizesNode.name = out + '/sizes'
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sizesNode.op = 'Const'
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sizesNode.addAttr('value', sizes)
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graph_def.node.extend([sizesNode])
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sliced = NodeDef()
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sliced.name = out
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sliced.op = 'Slice'
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sliced.input.append(inp)
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sliced.input.append(beginsNode.name)
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sliced.input.append(sizesNode.name)
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graph_def.node.extend([sliced])
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def addReshape(inp, out, shape, graph_def):
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shapeNode = NodeDef()
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shapeNode.name = out + '/shape'
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shapeNode.op = 'Const'
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shapeNode.addAttr('value', shape)
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graph_def.node.extend([shapeNode])
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reshape = NodeDef()
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reshape.name = out
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reshape.op = 'Reshape'
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reshape.input.append(inp)
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reshape.input.append(shapeNode.name)
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graph_def.node.extend([reshape])
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def addSoftMax(inp, out, graph_def):
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softmax = NodeDef()
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softmax.name = out
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softmax.op = 'Softmax'
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softmax.addAttr('axis', -1)
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softmax.input.append(inp)
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graph_def.node.extend([softmax])
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def addFlatten(inp, out, graph_def):
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flatten = NodeDef()
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flatten.name = out
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flatten.op = 'Flatten'
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flatten.input.append(inp)
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graph_def.node.extend([flatten])
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class NodeDef:
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def __init__(self):
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self.input = []
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self.name = ""
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self.op = ""
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self.attr = {}
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def addAttr(self, key, value):
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assert(not key in self.attr)
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if isinstance(value, bool):
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self.attr[key] = {'b': value}
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elif isinstance(value, int):
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self.attr[key] = {'i': value}
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elif isinstance(value, float):
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self.attr[key] = {'f': value}
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elif isinstance(value, str):
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self.attr[key] = {'s': value}
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elif isinstance(value, list):
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self.attr[key] = listToTensor(value)
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else:
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raise Exception('Unknown type of attribute ' + key)
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def Clear(self):
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self.input = []
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self.name = ""
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self.op = ""
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self.attr = {}
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class GraphDef:
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def __init__(self):
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self.node = []
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def save(self, filePath):
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with open(filePath, 'wt') as f:
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def printAttr(d, indent):
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indent = ' ' * indent
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for key, value in sorted(d.items(), key=lambda x:x[0].lower()):
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value = value if isinstance(value, list) else [value]
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for v in value:
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if isinstance(v, dict):
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f.write(indent + key + ' {\n')
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printAttr(v, len(indent) + 2)
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f.write(indent + '}\n')
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else:
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isString = False
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if isinstance(v, str) and not v.startswith('DT_'):
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try:
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float(v)
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except:
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isString = True
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if isinstance(v, bool):
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printed = 'true' if v else 'false'
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elif v == 'true' or v == 'false':
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printed = 'true' if v == 'true' else 'false'
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elif isString:
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printed = '\"%s\"' % v
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else:
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printed = str(v)
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f.write(indent + key + ': ' + printed + '\n')
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for node in self.node:
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f.write('node {\n')
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f.write(' name: \"%s\"\n' % node.name)
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f.write(' op: \"%s\"\n' % node.op)
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for inp in node.input:
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f.write(' input: \"%s\"\n' % inp)
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for key, value in sorted(node.attr.items(), key=lambda x:x[0].lower()):
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f.write(' attr {\n')
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f.write(' key: \"%s\"\n' % key)
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f.write(' value {\n')
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printAttr(value, 6)
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f.write(' }\n')
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f.write(' }\n')
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f.write('}\n')
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def parseTextGraph(filePath):
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msg = readTextMessage(filePath)
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graph = GraphDef()
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for node in msg['node']:
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graphNode = NodeDef()
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graphNode.name = node['name'][0]
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graphNode.op = node['op'][0]
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graphNode.input = node['input'] if 'input' in node else []
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if 'attr' in node:
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for attr in node['attr']:
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graphNode.attr[attr['key'][0]] = attr['value'][0]
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graph.node.append(graphNode)
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return graph
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# Removes Identity nodes
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def removeIdentity(graph_def):
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identities = {}
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for node in graph_def.node:
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if node.op == 'Identity' or node.op == 'IdentityN':
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inp = node.input[0]
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if inp in identities:
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identities[node.name] = identities[inp]
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else:
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identities[node.name] = inp
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graph_def.node.remove(node)
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for node in graph_def.node:
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for i in range(len(node.input)):
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if node.input[i] in identities:
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node.input[i] = identities[node.input[i]]
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def removeUnusedNodesAndAttrs(to_remove, graph_def):
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unusedAttrs = ['T', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
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'Index', 'Tperm', 'is_training', 'Tpaddings']
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removedNodes = []
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for i in reversed(range(len(graph_def.node))):
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op = graph_def.node[i].op
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name = graph_def.node[i].name
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if to_remove(name, op):
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if op != 'Const':
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removedNodes.append(name)
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del graph_def.node[i]
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else:
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for attr in unusedAttrs:
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if attr in graph_def.node[i].attr:
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del graph_def.node[i].attr[attr]
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# Remove references to removed nodes except Const nodes.
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for node in graph_def.node:
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for i in reversed(range(len(node.input))):
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if node.input[i] in removedNodes:
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del node.input[i]
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def writeTextGraph(modelPath, outputPath, outNodes):
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try:
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import cv2 as cv
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cv.dnn.writeTextGraph(modelPath, outputPath)
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except:
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import tensorflow as tf
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from tensorflow.tools.graph_transforms import TransformGraph
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with tf.gfile.FastGFile(modelPath, 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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graph_def = TransformGraph(graph_def, ['image_tensor'], outNodes, ['sort_by_execution_order'])
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for node in graph_def.node:
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if node.op == 'Const':
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if 'value' in node.attr and node.attr['value'].tensor.tensor_content:
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node.attr['value'].tensor.tensor_content = b''
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tf.train.write_graph(graph_def, "", outputPath, as_text=True)
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