How to use is_parallel method in molecule
Best Python code snippet using molecule_python
test_pruning.py
Source:test_pruning.py 
1#2# Copyright (c) 2018 Intel Corporation3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8# http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15#16from collections import namedtuple17import numpy as np18import logging19import torch20import os21import sys22try:23 import distiller24except ImportError:25 module_path = os.path.abspath(os.path.join('..'))26 sys.path.append(module_path)27 import distiller28import common29import pytest30from models import create_model31from apputils import save_checkpoint, load_checkpoint32# Logging configuration33logging.basicConfig(level=logging.INFO)34fh = logging.FileHandler('test.log')35logger = logging.getLogger()36logger.addHandler(fh)37NetConfig = namedtuple("test_config", "arch dataset bn_name module_pairs")38#39# Model configurations40#41def simplenet(is_parallel):42 if is_parallel:43 return NetConfig(arch="simplenet_cifar", dataset="cifar10",44 module_pairs=[("module.conv1", "module.conv2")],45 bn_name=None)46 else:47 return NetConfig(arch="simplenet_cifar", dataset="cifar10",48 module_pairs=[("conv1", "conv2")],49 bn_name=None)50def resnet20_cifar(is_parallel):51 if is_parallel:52 return NetConfig(arch="resnet20_cifar", dataset="cifar10",53 module_pairs=[("module.layer1.0.conv1", "module.layer1.0.conv2")],54 bn_name="module.layer1.0.bn1")55 else:56 return NetConfig(arch="resnet20_cifar", dataset="cifar10",57 module_pairs=[("layer1.0.conv1", "layer1.0.conv2")],58 bn_name="layer1.0.bn1")59def vgg19_imagenet(is_parallel):60 if is_parallel:61 return NetConfig(arch="vgg19", dataset="imagenet",62 module_pairs=[("features.module.0", "features.module.2"),63 ("features.module.21", "features.module.23"),64 ("features.module.23", "features.module.25"),65 ("features.module.25", "features.module.28"),66 ("features.module.28", "features.module.30"),67 ("features.module.30", "features.module.32"),68 ("features.module.32", "features.module.34")],69 bn_name=None)70 else:71 return NetConfig(arch="vgg19", dataset="imagenet",72 module_pairs=[("features.21", "features.23"),73 ("features.23", "features.25"),74 ("features.25", "features.28"),75 ("features.28", "features.30"),76 ("features.30", "features.32"),77 ("features.32", "features.34")],78 bn_name=None)79def vgg16_cifar(is_parallel):80 if is_parallel:81 return NetConfig(arch="vgg16_cifar", dataset="cifar10",82 module_pairs=[("features.module.0", "features.module.2")],83 bn_name=None)84 else:85 return NetConfig(arch="vgg16_cifar", dataset="cifar10",86 module_pairs=[("features.0", "features.2")],87 bn_name=None)88@pytest.fixture(params=[True, False])89def parallel(request):90 return request.param91def test_ranked_filter_pruning(parallel):92 ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=0.1, is_parallel=parallel)93 ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=0.5, is_parallel=parallel)94 ranked_filter_pruning(simplenet(parallel), ratio_to_prune=0.5, is_parallel=parallel)95 ranked_filter_pruning(vgg19_imagenet(parallel), ratio_to_prune=0.1, is_parallel=parallel)96 model, zeros_mask_dict = ranked_filter_pruning(vgg19_imagenet(parallel),97 ratio_to_prune=0.1,98 is_parallel=parallel)99 test_conv_fc_interface(parallel, model, zeros_mask_dict)100def test_prune_all_filters(parallel):101 """Pruning all of the filteres in a weights tensor of a Convolution102 is illegal and should raise an exception.103 """104 with pytest.raises(ValueError):105 ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=1.0, is_parallel=parallel)106def ranked_filter_pruning(config, ratio_to_prune, is_parallel):107 """Test L1 ranking and pruning of filters.108 First we rank and prune the filters of a Convolutional layer using109 a L1RankedStructureParameterPruner. Then we physically remove the110 filters from the model (via "thining" process).111 """112 model, zeros_mask_dict = common.setup_test(config.arch, config.dataset, is_parallel)113 for pair in config.module_pairs:114 # Test that we can access the weights tensor of the first convolution in layer 1115 conv1_p = distiller.model_find_param(model, pair[0] + ".weight")116 assert conv1_p is not None117 num_filters = conv1_p.size(0)118 # Test that there are no zero-filters119 assert distiller.sparsity_3D(conv1_p) == 0.0120 # Create a filter-ranking pruner121 pruner = distiller.pruning.L1RankedStructureParameterPruner("filter_pruner",122 group_type="Filters",123 desired_sparsity=ratio_to_prune,124 weights=pair[0] + ".weight")125 pruner.set_param_mask(conv1_p, pair[0] + ".weight", zeros_mask_dict, meta=None)126 conv1 = common.find_module_by_name(model, pair[0])127 assert conv1 is not None128 # Test that the mask has the correct fraction of filters pruned.129 # We asked for 10%, but there are only 16 filters, so we have to settle for 1/16 filters130 expected_cnt_removed_filters = int(ratio_to_prune * conv1.out_channels)131 expected_pruning = expected_cnt_removed_filters / conv1.out_channels132 masker = zeros_mask_dict[pair[0] + ".weight"]133 assert masker is not None134 assert distiller.sparsity_3D(masker.mask) == expected_pruning135 # Use the mask to prune136 assert distiller.sparsity_3D(conv1_p) == 0137 masker.apply_mask(conv1_p)138 assert distiller.sparsity_3D(conv1_p) == expected_pruning139 # Remove filters140 conv2 = common.find_module_by_name(model, pair[1])141 assert conv2 is not None142 assert conv1.out_channels == num_filters143 assert conv2.in_channels == num_filters144 # Test thinning145 distiller.remove_filters(model, zeros_mask_dict, config.arch, config.dataset, optimizer=None)146 assert conv1.out_channels == num_filters - expected_cnt_removed_filters147 assert conv2.in_channels == num_filters - expected_cnt_removed_filters148 return model, zeros_mask_dict149def test_arbitrary_channel_pruning(parallel):150 arbitrary_channel_pruning(resnet20_cifar(parallel),151 channels_to_remove=[0, 2],152 is_parallel=parallel)153 arbitrary_channel_pruning(simplenet(parallel),154 channels_to_remove=[0, 2],155 is_parallel=parallel)156 arbitrary_channel_pruning(vgg19_imagenet(parallel),157 channels_to_remove=[0, 2],158 is_parallel=parallel)159 arbitrary_channel_pruning(vgg16_cifar(parallel),160 channels_to_remove=[0, 2],161 is_parallel=parallel)162def test_prune_all_channels(parallel):163 """Pruning all of the channels in a weights tensor of a Convolution164 is illegal and should raise an exception.165 """166 with pytest.raises(ValueError):167 arbitrary_channel_pruning(resnet20_cifar(parallel),168 channels_to_remove=[ch for ch in range(16)],169 is_parallel=parallel)170def test_channel_pruning_conv_bias(parallel):171 arbitrary_channel_pruning(simplenet(parallel),172 channels_to_remove=[0, 1],173 is_parallel=parallel)174def create_channels_mask(conv_p, channels_to_remove):175 assert conv_p.dim() == 4176 num_filters = conv_p.size(0)177 num_channels = conv_p.size(1)178 kernel_height = conv_p.size(2)179 kernel_width = conv_p.size(3)180 # Let's build our 4D mask.181 # We start with a 1D mask of channels, with all but our specified channels set to one182 channels = torch.ones(num_channels)183 for ch in channels_to_remove:184 channels[ch] = 0185 # Now let's expand back up to a 4D mask186 mask = channels.expand(num_filters, num_channels)187 mask.unsqueeze_(-1)188 mask.unsqueeze_(-1)189 mask = mask.expand(num_filters, num_channels, kernel_height, kernel_width).contiguous().cuda()190 assert mask.shape == conv_p.shape191 return mask192def run_forward_backward(model, optimizer, dummy_input):193 criterion = torch.nn.CrossEntropyLoss().cuda()194 model.train()195 output = model(dummy_input)196 target = torch.LongTensor(1).random_(2).cuda()197 loss = criterion(output, target)198 optimizer.zero_grad()199 loss.backward()200 optimizer.step()201def arbitrary_channel_pruning(config, channels_to_remove, is_parallel):202 """Test removal of arbitrary channels.203 The test receives a specification of channels to remove.204 Based on this specification, the channels are pruned and then physically205 removed from the model (via a "thinning" process).206 """207 model, zeros_mask_dict = common.setup_test(config.arch, config.dataset, is_parallel)208 pair = config.module_pairs[0]209 conv2 = common.find_module_by_name(model, pair[1])210 assert conv2 is not None211 # Test that we can access the weights tensor of the first convolution in layer 1212 conv2_p = distiller.model_find_param(model, pair[1] + ".weight")213 assert conv2_p is not None214 assert conv2_p.dim() == 4215 num_channels = conv2_p.size(1)216 cnt_nnz_channels = num_channels - len(channels_to_remove)217 mask = create_channels_mask(conv2_p, channels_to_remove)218 assert distiller.density_ch(mask) == (conv2.in_channels - len(channels_to_remove)) / conv2.in_channels219 # Cool, so now we have a mask for pruning our channels.220 # Use the mask to prune221 zeros_mask_dict[pair[1] + ".weight"].mask = mask222 zeros_mask_dict[pair[1] + ".weight"].apply_mask(conv2_p)223 all_channels = set([ch for ch in range(num_channels)])224 nnz_channels = set(distiller.find_nonzero_channels_list(conv2_p, pair[1] + ".weight"))225 channels_removed = all_channels - nnz_channels226 logger.info("Channels removed {}".format(channels_removed))227 # Now, let's do the actual network thinning228 distiller.remove_channels(model, zeros_mask_dict, config.arch, config.dataset, optimizer=None)229 conv1 = common.find_module_by_name(model, pair[0])230 assert conv1231 assert conv1.out_channels == cnt_nnz_channels232 assert conv2.in_channels == cnt_nnz_channels233 assert conv1.weight.size(0) == cnt_nnz_channels234 assert conv2.weight.size(1) == cnt_nnz_channels235 if config.bn_name is not None:236 bn1 = common.find_module_by_name(model, config.bn_name)237 assert bn1.running_var.size(0) == cnt_nnz_channels238 assert bn1.running_mean.size(0) == cnt_nnz_channels239 assert bn1.num_features == cnt_nnz_channels240 assert bn1.bias.size(0) == cnt_nnz_channels241 assert bn1.weight.size(0) == cnt_nnz_channels242 dummy_input = common.get_dummy_input(config.dataset)243 optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.1)244 run_forward_backward(model, optimizer, dummy_input)245 # Let's test saving and loading a thinned model.246 # We save 3 times, and load twice, to make sure to cover some corner cases:247 # - Make sure that after loading, the model still has hold of the thinning recipes248 # - Make sure that after a 2nd load, there no problem loading (in this case, the249 # - tensors are already thin, so this is a new flow)250 # (1)251 save_checkpoint(epoch=0, arch=config.arch, model=model, optimizer=None)252 model_2 = create_model(False, config.dataset, config.arch, parallel=is_parallel)253 model(dummy_input)254 model_2(dummy_input)255 conv2 = common.find_module_by_name(model_2, pair[1])256 assert conv2 is not None257 with pytest.raises(KeyError):258 model_2, compression_scheduler, start_epoch = load_checkpoint(model_2, 'checkpoint.pth.tar')259 compression_scheduler = distiller.CompressionScheduler(model)260 hasattr(model, 'thinning_recipes')261 run_forward_backward(model, optimizer, dummy_input)262 # (2)263 save_checkpoint(epoch=0, arch=config.arch, model=model, optimizer=None, scheduler=compression_scheduler)264 model_2, compression_scheduler, start_epoch = load_checkpoint(model_2, 'checkpoint.pth.tar')265 assert hasattr(model_2, 'thinning_recipes')266 logger.info("test_arbitrary_channel_pruning - Done")267 # (3)268 save_checkpoint(epoch=0, arch=config.arch, model=model_2, optimizer=None, scheduler=compression_scheduler)269 model_2, compression_scheduler, start_epoch = load_checkpoint(model_2, 'checkpoint.pth.tar')270 assert hasattr(model_2, 'thinning_recipes')271 logger.info("test_arbitrary_channel_pruning - Done 2")272def test_conv_fc_interface(is_parallel=parallel, model=None, zeros_mask_dict=None):273 """A special case of convolution filter-pruning occurs when the next layer is274 fully-connected (linear). This test is for this case and uses VGG16.275 """276 arch = "vgg19"277 dataset = "imagenet"278 ratio_to_prune = 0.1279 if is_parallel:280 conv_name = "features.module.34"281 else:282 conv_name = "features.34"283 fc_name = "classifier.0"284 dummy_input = torch.randn(1, 3, 224, 224).cuda()285 if model is None or zeros_mask_dict is None:286 model, zeros_mask_dict = common.setup_test(arch, dataset, is_parallel)287 # Run forward and backward passes, in order to create the gradients and optimizer params288 optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.1)289 run_forward_backward(model, optimizer, dummy_input)290 conv = common.find_module_by_name(model, conv_name)291 assert conv is not None292 conv_p = distiller.model_find_param(model, conv_name + ".weight")293 assert conv_p is not None294 assert conv_p.dim() == 4295 # Create a filter-ranking pruner296 pruner = distiller.pruning.L1RankedStructureParameterPruner("filter_pruner",297 group_type="Filters",298 desired_sparsity=ratio_to_prune,299 weights=conv_name + ".weight")300 pruner.set_param_mask(conv_p, conv_name + ".weight", zeros_mask_dict, meta=None)301 # Use the mask to prune302 masker = zeros_mask_dict[conv_name + ".weight"]303 assert masker is not None304 masker.apply_mask(conv_p)305 num_filters = conv_p.size(0)306 expected_cnt_removed_filters = int(ratio_to_prune * conv.out_channels)307 # Remove filters308 fc = common.find_module_by_name(model, fc_name)309 assert fc is not None310 # Test thinning311 fm_size = fc.in_features // conv.out_channels312 num_nnz_filters = num_filters - expected_cnt_removed_filters313 distiller.remove_filters(model, zeros_mask_dict, arch, dataset, optimizer)314 assert conv.out_channels == num_nnz_filters315 assert fc.in_features == fm_size * num_nnz_filters316 # Run again, to make sure the optimizer and gradients shapes were updated correctly317 run_forward_backward(model, optimizer, dummy_input)318 run_forward_backward(model, optimizer, dummy_input)319def test_threshold_mask():320 # Create a 4-D tensor of 1s321 a = torch.ones(3, 64, 32, 32)322 # Change one element323 a[1, 4, 17, 31] = 0.2324 # Create and apply a mask325 mask = distiller.threshold_mask(a, threshold=0.3)326 assert np.sum(distiller.to_np(mask)) == (distiller.volume(a) - 1)327 assert mask[1, 4, 17, 31] == 0328 assert common.almost_equal(distiller.sparsity(mask), 1/distiller.volume(a))329def test_magnitude_pruning():330 # Create a 4-D tensor of 1s331 a = torch.ones(3, 64, 32, 32)332 # Change one element333 a[1, 4, 17, 31] = 0.2334 # Create a masks dictionary and populate it with one ParameterMasker335 zeros_mask_dict = {}336 masker = distiller.ParameterMasker('a')337 zeros_mask_dict['a'] = masker338 # Try to use a MagnitudeParameterPruner with defining a default threshold339 with pytest.raises(AssertionError):340 pruner = distiller.pruning.MagnitudeParameterPruner("test", None)341 # Now define the default threshold342 thresholds = {"*": 0.4}343 pruner = distiller.pruning.MagnitudeParameterPruner("test", thresholds)344 assert distiller.sparsity(a) == 0345 # Create a mask for parameter 'a'346 pruner.set_param_mask(a, 'a', zeros_mask_dict, None)347 assert common.almost_equal(distiller.sparsity(zeros_mask_dict['a'].mask), 1/distiller.volume(a))348 # Let's now use the masker to prune a parameter349 masker = zeros_mask_dict['a']350 masker.apply_mask(a)351 assert common.almost_equal(distiller.sparsity(a), 1/distiller.volume(a))352 # We can use the masker on other tensors, if we want (and if they have the correct shape).353 # Remember that the mask was created already, so we're not thresholding - we are pruning354 b = torch.ones(3, 64, 32, 32)355 b[:] = 0.3356 masker.apply_mask(b)357 assert common.almost_equal(distiller.sparsity(b), 1/distiller.volume(a))358if __name__ == '__main__':359 for is_parallel in [True, False]:360 test_ranked_filter_pruning(is_parallel)361 test_arbitrary_channel_pruning(is_parallel)362 test_prune_all_channels(is_parallel)363 model, zeros_mask_dict = ranked_filter_pruning(vgg19_imagenet(is_parallel),364 ratio_to_prune=0.1,365 is_parallel=is_parallel)366 test_conv_fc_interface(is_parallel, model, zeros_mask_dict)367 arbitrary_channel_pruning(vgg19_imagenet(parallel),368 channels_to_remove=[0, 2],...
main.py
Source:main.py
1#æ ¡åå¡å®ä½ä»¥åå¨æç«æ£23import cv24import numpy as np5from numpy.core.fromnumeric import shape, sort678def my_cross_point(line1, line2, size):9 '''10 æ±ä¸¤æ¡ç´çº¿ç交ç¹åæ¯å¦å¹³è¡11 line1,line2:æ¯æ¡lineç¨ä¸¤ä¸ªç¹çxyåæ 表示12 sizeï¼å¾åçäºç»´å°ºå¯¸13 out_x,out_yï¼äº¤ç¹çåæ ãå¦æ两ç´çº¿äº¤ç¹è¶
åºå¾åèå´åè¿å-114 is_parallel:True-两ç´çº¿è¿ä¼¼å¹³è¡ï¼äº¤ç¹å¨å¾åå¤ï¼Falseï¼ä¸¤ç´çº¿å¨å¾åå
æ交ç¹15 '''1617 [[x1, y1, x2, y2]] = line118 [[x3, y3, x4, y4]] = line219 x_max, y_max = size20 '''21 è¿éå ä¸ä¸ªæç为0ææ¯æçä¸åå¨çå¤æ22 '''23 if(abs(x1 - x2) < 1 or abs(x3 - x4) < 1): # 两æ¡çº¿ä¸è³å°ä¸æ¡æ¯åç´çº¿24 if(abs(x1 - x2) < 1 and abs(x3-x4) > 1): # åªæline1æ¯åç´çº¿25 out_x = x126 out_y = (y3-y4)/(x3-x4)*(x1-x3)+y3 # ä¹å°±æ¯k3*(x-x3)+y327 is_parallel = False28 # return out_x, out_y, is_parallel29 elif(abs(x1 - x2) > 1 and abs(x3-x4) < 1): # åªæline2æ¯åç´çº¿30 out_x = x331 out_y = (y1-y2)/(x1-x2)*(x3-x1)+y1 # ä¹å°±æ¯k1*(x-x1)+y132 is_parallel = False3334 else: # 两æ¡é½æ¯åç´çº¿35 out_x = -136 out_y = -137 is_parallel = True38 else: # 两æ¡é½ä¸æ¯åç´çº¿ æçé½åå¨ ä»£ç´çº¿å
¬å¼è§£åæ 39 k1 = (y1-y2)/(x1-x2)40 k3 = (y3-y4)/(x3-x4)41 is_parallel = False # é»è®¤ä¸å¹³è¡4243 # k1 = k3æ¶ ç´æ¥è¿åå¹³è¡ï¼ä¸éè¦å计ç®44 if(abs(k1-k3) < 1):45 out_x = -146 out_y = -147 is_parallel = True4849 return out_x, out_y, is_parallel5051 # k1!=k352 out_x = (k1*x1-k3*x3-y1+y3)/(k1-k3)53 out_y = k1*(out_x-x1)+y15455 # å¦æè¾åºçxyè¶
åºäºå¾åæ¬èº«çèå´ è¯´æ两è
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³ç³»56 if(out_x <= 0 or out_y <= 0 or out_x > x_max or out_y > y_max):57 out_x = -158 out_y = -159 is_parallel = True60 # return out_x, out_y, is_parallel6162 return out_x, out_y, is_parallel636465cap = cv2.VideoCapture("1.mp4")66while(cap.isOpened()):67 print("open")68 ret, videoframe = cap.read()69 cv2.imshow('videoframe', videoframe)70 if cv2.waitKey(25) & 0xFF == ord('q'):71 break72 # 对videoframeåå¤ç73 img = videoframe74 # ---- å¾åå¤ç ----7576 # 使ç¨é¢è²HSVè¿è¡æå77 mat = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)78 # ç±äºæ ¡åå¡ä¼åå¨ä¸åç¨åº¦çè¤ªè² å æ¤åªåå®äºè²è°èå´ï¼äº®åº¦å饱å度é½ä¸æå®79 low_blue = np.array([78, 0, 0])80 upper_blue = np.array([124, 255, 255])81 mask = cv2.inRange(mat, low_blue, upper_blue)82 ROI = mask83 SE = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))84 ROI_open = cv2.morphologyEx(ROI, cv2.MORPH_CLOSE, (5, 5))85 # ç¨å°SEè
èï¼åååå¾åå·®ï¼å¾å°å¤è½®å»86 SE_small = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))87 ROI_erode = cv2.erode(ROI_open, SE_small)88 ROI_edge = ROI_open-ROI_erode89 # å¼éæä½ å»é¤å°éå¤ç«ç¹90 ROI_edge = cv2.morphologyEx(ROI_edge, cv2.MORPH_CLOSE, SE)91 ROI_edge = cv2.morphologyEx(92 ROI_edge, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)))93 # å¾åä¸å¯è½æå
¶ä»æç© å æ¤éè¦ä»ææè½®å»ä¸æ¾å°æ大çé£ä¸ªä½ä¸ºæ ¡åå¡çè½®å»94 contours_none = cv2.findContours(95 ROI_edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)96 biggest = sorted(contours_none, key=lambda x: cv2.contourArea(97 x), reverse=True) # æ ¹æ®contourçé¢ç§¯å¤§å°æåº98 drawing_none = cv2.drawContours(img, biggest, 0, (0, 255, 0), 3)99 #cv2.imshow("å¤çå", img)100101 # ---- æ¾å°ç®æ é¡¶ç¹ ----102103 # ç¨å¤è¾¹å½¢æåæ ¡åå¡çå¤è½®å» å
¶ä¸epsilonæ¯åæ°ï¼å¯ä»¥è°æ´æåç精度104 epsilon = 0.01105 approx = cv2.approxPolyDP(106 biggest[0], epsilon * cv2.arcLength(biggest[0], True), True)107 boxPoints = approx.reshape(-1, 2)108 # ç»åºæåå¤è¾¹å½¢ç顶ç¹åè¾¹109 num = len(approx) # 顶ç¹æ°ç® = è¾¹æ°110 for i in range(0, num):111 cv2.line(img, boxPoints[i], boxPoints[(i+1) % num], (255, 100, 100), 4)112 for i in range(0, num):113 cv2.circle(img, boxPoints[i], 10, (240, 240, 200), -1)114 # cv2.imshow("å¤è¾¹å½¢",img)115 # æåå¾å°çå¤è¾¹å½¢ä¸ä¸å®æ¯å边形ï¼å¯è½æå¾å¤æ¡è¾¹ãæ们éæ©é¿åº¦æé¿çåæ¡è¾¹ä½ä¸ºæ ¡åå¡çåæ¡è¾¹116 mysidelen = np.ones((num, 1))117 for i in range(0, num):118 mysidelen[i] = np.linalg.norm(boxPoints[(i+1) % num]-boxPoints[i])119120 four_side_len = sorted(mysidelen, reverse=True)[0:4]121 # å¾å°æé¿çåæ¡è¾¹ç顶ç¹ç¼å·ï¼[ç¼å·-ç¼å·+1]å³æ¯è¯¥è¾¹çä¸¤ä¸ªç«¯ç¹ %numæ¯ä¸ºäºå¤çè¾¹çæ
åµ122 pointID = sorted(range(num), key=lambda k: mysidelen[k], reverse=True)[0:4]123 four_side = np.ones((4, 1, 4)) # 4è¡4å æ¯ä¸ä¸ªå
ç´ æ¯ææä¸æ¡ç´çº¿ç两个ç¹çåæ åé¢ç®äº¤ç¹è¦ç¨124 for i in range(0, 4):125 cv2.line(img, boxPoints[pointID[i]],126 boxPoints[(pointID[i]+1) % num], (0, 0, 255), 4)127 four_side[i] = np.array([boxPoints[pointID[i]][0], boxPoints[pointID[i]][1], boxPoints[(128 pointID[i]+1) % num][0], boxPoints[(pointID[i]+1) % num][1]])129130 # æ ¡åå¡çé¿å®½æ¯å¤§æ¦æ¯1.57å·¦å³ï¼å æ¤å¤§é¨åçæ
åµä¸ï¼å¾åä¸æé¿çè¾¹ä¸è¬å°±æ¯æ ¡åå¡çé¿131 # four_sideæ¯æé¿åº¦éåºæåºçãå æ¤four_side[0]æ示çä¸å®æ¯æé¿è¾¹132 # å
¶ä»çè¾¹æ æ³ç¨é¿åº¦ç¡®å®ï¼å 为å¨éè§ææ²å¾å¾ä¸¥éçæ
åµä¸ï¼é¿è¾¹å¯è½ä¼æ¯å®½è¾¹ç133 # å¤ææé¿è¾¹åå
¶ä»ä¸æ¡è¾¹å¤ææ¯å¦å¹³è¡ãè¥ä¸å¹³è¡åæ¯å®½è¾¹ï¼å¯ä»¥æ¾å°ä¸¤ä¸ªäº¤ç¹134 # è¥å¹³è¡åæ¯é¿è¾¹ï¼åæ¥ä¸æ¥ç¨å¹³è¡çè¿æ¡è¾¹å两æ¡å®½è¾¹ç¸äº¤ï¼æ¾å°å©ä¸ç两个交ç¹135136 cross_points = np.zeros((4, 2))137 j = 0138 for i in range(1, 4):139 temp_x, temp_y, is_parallel = my_cross_point(140 four_side[0], four_side[i], (1080, 1440)) # (np.shape(img)[1], np.shape(img)[0]))141 if(is_parallel): # æ¾å°å¹³è¡è¾¹142 parallel_index = i143 continue144 cross_points[j, :] = temp_x, temp_y145 j += 1146 # parallel_indexï¼åé¿è¾¹å¹³è¡çé£æ¡è¾¹çç¼å·147148 # æ¾å¹³è¡è¾¹åå
¶ä»ä¸¤æ¡è¾¹ç交ç¹149 j = 2150 for i in range(1, 4):151 if(i == parallel_index):152 continue153 temp_x, temp_y, is_parallel = my_cross_point(154 four_side[parallel_index], four_side[i], (1080, 1440)) # (np.shape(img)[1], np.shape(img)[0]))155 cross_points[j, :] = temp_x, temp_y156 j += 1157158 # å¨å¾ä¸æ åºå个ç¹159 for i in range(0, 4):160 cv2.circle(img, np.array(161 [cross_points[i][0], cross_points[i][1]], dtype=np.int32), 5, (0, 0, 0), -1)162 #cv2.imshow("box2", img)163164 # éè¦æ¾å°å个交ç¹å¨å¾åä¸çç¸å¯¹æ¹ä½ï¼æç
§âå·¦ä¸ å·¦ä¸ å³ä¸ å³ä¸âç顺åºæå165 # æ¹æ³ï¼å
æåæåºï¼å¯ä»¥æ顶ç¹åæå·¦å³ä¸¤ç»ï¼åå¨ç»å
æè¡æåºï¼ååºä¸ä¸166 sorted_cross_points = sorted(cross_points, key=lambda x: x[0])167 sorted_cross_points = sorted(sorted_cross_points[0:2], key=lambda x: x[1]) + sorted(168 sorted_cross_points[2:4], key=lambda x: x[1])169170 # ç±äºç®æ æ å°çç¹éè¦æç
§é¡ºæ¶éæåï¼å³âå·¦ä¸ å³ä¸ å³ä¸ å·¦ä¸â171 # ç¶èç±äºå¡å¯è½æ¯ç«çæ¾çï¼å æ¤å¾åä¸çå·¦ä¸è§ä¸ä¸å®æ¯å¡çå·¦ä¸è§ï¼æ以éè¦åç±»å¤ç172 # horizontalï¼å¡æ¯æ¨ªçæ¾ç verticalï¼å¡æ¯ç«çæ¾ç173 indices_horizontal = [0, 2, 3, 1] # å·¦ä¸ å³ä¸ å³ä¸ å·¦ä¸ï¼é¡ºæ¶éï¼ç顺åº174 indices_vertical = [1, 0, 2, 3]175176 # å¤ææ åï¼å·¦ä¸ç¦»å·¦ä¸æ´è¿ä¸ºæ¨ªçæ¾ å·¦ä¸ç¦»å³ä¸æ´è¿-ç«çæ¾177 h_or_v = np.linalg.norm(sorted_cross_points[0]-sorted_cross_points[1]) < np.linalg.norm(178 sorted_cross_points[0]-sorted_cross_points[2])179 if(h_or_v): # 横çæ¾180 sorted_cross_points = np.array(sorted_cross_points)[181 indices_horizontal[:]]182 for i in range(0, 4): #ç»çº¿183 cv2.line(img, np.array(cross_points[indices_horizontal[i]], dtype=np.int32), np.array(184 cross_points[indices_horizontal[(i+1) % 4]], dtype=np.int32), (0, 0, 255), 4)185186 else: # ç«çæ¾187 sorted_cross_points = np.array(sorted_cross_points)[188 indices_vertical[:]]189 for i in range(0, 4): #ç»çº¿190 cv2.line(img, np.array(cross_points[indices_horizontal[i]], dtype=np.int32), np.array(191 cross_points[indices_horizontal[(i+1) % 4]], dtype=np.int32), (0, 0, 255), 4)192193 cv2.imshow("fixed",img)194 # ---- éè§åæ¢ ----195196 # æ ¡åå¡ç尺寸大æ¦æ¯850mm*540mm197 # æ±åºéè§ç©éµå¹¶åéè§åæ¢198 dstPoints = np.array(199 [[0, 0], [850, 0], [850, 540], [0, 540]], dtype=np.int32)200 M = cv2.findHomography(sorted_cross_points, dstPoints)201 Ma = np.array(M[0], dtype=np.float64)202203 # ---- ç»æå±ç¤º ----204 dst_img = img205 img_f64 = np.float64(dst_img)206 output = cv2.warpPerspective(dst_img, M[0], (850, 540))207208 cv2.imshow("outpu", output)209 210211cap.release()212cv2.destroyAllWindows()213
...
vector.py
Source:vector.py
...58 return acos(dot_prod)59 def get_angle_deg(self, other):60 degrees_per_rad = 180. / pi61 return degrees_per_rad * self.get_angle_rad(other)62 def is_parallel(self, other):63 return (self.is_zero() or other.is_zero() or64 self.get_angle_rad(other) in [0, pi])65 def is_orthogonal(self, other):66 return round(self.dot_product(other), 3) == 067 def get_projected_vector(self, other):68 """69 Gets projection of vector v in b70 """71 b_normalized = other.normalize()72 return b_normalized.times_scalar(self.dot_product(b_normalized))73 def get_orthogonal_vector(self, other):74 return self.minus(self.get_projected_vector(other))75 def cross_product(self, other):76 [x1, y1, z1] = self.coordinates77 [x2, y2, z2] = other.coordinates78 x = (y1 * z2) - (y2 * z1)79 y = -((x1 * z2) - (x2 * z1))80 z = (x1 * y2) - (x2 * y1)81 return Vector([x, y, z])82 def area_parallelogram(self, other):83 return self.cross_product(other).magnitude()84 def area_triangle(self, other):85 return self.cross_product(other).magnitude() / 286if __name__ == '__main__':87 v = Vector([8.218, -9.341])88 w = Vector([-1.129, 2.111])89 addition = v.plus(w)90 print 'addition: {}'.format(addition)91 v = Vector([7.119, 8.215])92 w = Vector([-8.223, 0.878])93 subtraction = v.minus(w)94 print 'subtraction: {}'.format(subtraction)95 v = Vector([1.671, -1.012, -0.318])96 multiplication = v.times_scalar(7.41)97 print 'multiplication: {}'.format(multiplication)98 # *****************99 v = Vector([-0.221, 7.437])100 first_magintude = v.magnitude()101 print 'first_magintude: {}'.format(round(first_magintude, 3))102 v = Vector([8.813, -1.331, -6.247])103 second_magintude = v.magnitude()104 print 'second_magintude: {}'.format(round(second_magintude, 3))105 v = Vector([5.581, -2.136])106 first_normalization = v.normalize()107 print 'first_normailization: {}'.format(first_normalization)108 v = Vector([1.996, 3.108, -4.554])109 second_normalization = v.normalize()110 print 'second_normailization: {}'.format(second_normalization)111 # *****************112 v = Vector([7.887, 4.138])113 w = Vector([-8.802, 6.776])114 dot_product = v.dot_product(w)115 print 'first_dot_product: {}'.format(round(dot_product, 3))116 v = Vector([-5.955, -4.904, -1.874])117 w = Vector([-4.496, -8.755, 7.103])118 dot_product = v.dot_product(w)119 print 'second_dot_product: {}'.format(round(dot_product, 3))120 # *****************121 v = Vector([3.183, -7.627])122 w = Vector([-2.668, 5.319])123 angle_rads = v.get_angle_rad(w)124 print 'first_angle_rads: {}'.format(angle_rads)125 v = Vector([7.35, 0.221, 5.188])126 w = Vector([2.751, 8.259, 3.985])127 angle_degrees = v.get_angle_deg(w)128 print 'first_angle_rads: {}'.format(angle_degrees)129 # *****************130 v = Vector([-7.579, -7.88])131 w = Vector([22.737, 23.64])132 is_parallel = v.is_parallel(w)133 is_orthogonal = v.is_orthogonal(w)134 print '1 parallel: {}, orthogonal: {}'.format(is_parallel, is_orthogonal)135 v = Vector([-2.029, 9.97, 4.172])136 w = Vector([-9.231, -6.639, -7.245])137 is_parallel = v.is_parallel(w)138 is_orthogonal = v.is_orthogonal(w)139 print '2 parallel: {}, orthogonal: {}'.format(is_parallel, is_orthogonal)140 v = Vector([-2.328, -7.284, -1.214])141 w = Vector([-1.821, 1.072, -2.94])142 is_parallel = v.is_parallel(w)143 is_orthogonal = v.is_orthogonal(w)144 print '3 parallel: {}, orthogonal: {}'.format(is_parallel, is_orthogonal)145 v = Vector([2.118, 4.827])146 w = Vector([0, 0])147 is_parallel = v.is_parallel(w)148 is_orthogonal = v.is_orthogonal(w)149 print '4 parallel: {}, orthogonal: {}'.format(is_parallel, is_orthogonal)150 # *****************151 v = Vector([3.039, 1.879])152 w = Vector([0.825, 2.036])153 projected_vector = v.get_projected_vector(w)154 print 'projected vector is: {}'.format(projected_vector)155 v = Vector([-9.88, -3.264, -8.159])156 w = Vector([-2.155, -9.353, -9.473])157 orthogonal_vector = v.get_orthogonal_vector(w)158 print 'orthogonal vector is: {}'.format(orthogonal_vector)159 v = Vector([3.009, -6.172, 3.692, -2.51])160 w = Vector([6.404, -9.144, 2.759, 8.718])161 projected_vector = v.get_projected_vector(w)...
test_641_construction_ray.py
Source:test_641_construction_ray.py
...22 ray1 = ConstructionRay((10, 1), (20, 10))23 ray2 = ConstructionRay((17, -7), (-10, 3))24 point = ray1.intersect(ray2)25 assert point.isclose(Vec3(5.7434, -2.8309), abs_tol=1e-4)26 assert ray1.is_parallel(ray2) is False27 def test_ray2d_parallel(self):28 ray1 = ConstructionRay((17, -8), (-10, 2))29 ray2 = ConstructionRay((-10, 3), (17, -7))30 ray3 = ConstructionRay((-10, 4), (17, -6))31 assert ray2.is_parallel(ray3) is True32 assert ray1.is_parallel(ray3) is True33 with pytest.raises(ParallelRaysError):34 _ = ray2.intersect(ray3)35 def test_ray2d_intersect_with_vertical(self):36 ray1 = ConstructionRay((10, 1), (10, -7))37 ray2 = ConstructionRay((-10, 3), (17, -7))38 point = ray1.intersect(ray2)39 assert point.x == 1040 assert point.isclose(Vec3(10.0, -4.4074), abs_tol=1e-4)41 with pytest.raises(ArithmeticError):42 _ = ray1.yof(1)43 def test_ray2d_intersect_with_horizontal(self):44 ray1 = ConstructionRay((-10, 10), (10, 10))45 ray2 = ConstructionRay((-10, 20), (10, 0))46 point = ray1.intersect(ray2)47 assert point.y == 1048 assert point.isclose(Vec3(0.0, 10.0), abs_tol=1e-4)49 def test_ray2d_intersect_with_vertical_and_horizontal(self):50 ray1 = ConstructionRay((-10, 10), (10, 10))51 ray2 = ConstructionRay((5, 0), (5, 20))52 point = ray1.intersect(ray2)53 assert point.y == 1054 assert point.x == 555 assert point.isclose(Vec3(5.0, 10.0), abs_tol=1e-4)56 def test_ray2d_parallel_vertical(self):57 ray1 = ConstructionRay((10, 1), (10, -7))58 ray2 = ConstructionRay((11, 0), angle=HALF_PI)59 ray3 = ConstructionRay((12, -10), (12, 7))60 ray4 = ConstructionRay((0, 0), (1, 1))61 ray5 = ConstructionRay((0, 0), angle=0)62 with pytest.raises(ParallelRaysError):63 _ = ray1.intersect(ray3)64 assert ray1.is_parallel(ray3) is True65 assert ray1.is_parallel(ray2) is True66 assert ray2.is_parallel(ray2) is True67 assert ray1.is_parallel(ray4) is False68 assert ray2.is_parallel(ray4) is False69 assert ray3.is_parallel(ray4) is False70 assert ray1.is_parallel(ray5) is False71 assert ray2.is_parallel(ray5) is False72 assert ray3.is_parallel(ray5) is False73 # vertical rays can't calc a y-value74 with pytest.raises(ArithmeticError):75 _ = ray1.yof(-1.0)76 def test_ray2d_normal_vertical(self):77 ray = ConstructionRay((10, 1), (10, -7)) # vertical line78 ortho = ray.orthogonal((3, 3))79 point = ray.intersect(ortho)80 assert point.isclose(Vec3(10, 3))81 def test_ray2d_normal(self):82 ray = ConstructionRay((-10, 3), (17, -7))83 ortho = ray.orthogonal((3, 3))84 point = ray.intersect(ortho)85 assert point.isclose(Vec3(1.4318, -1.234), abs_tol=1e-4)86 def test_ray2d_normal_horizontal(self):87 ray = ConstructionRay((10, 10), (20, 10)) # horizontal line88 ortho = ray.orthogonal((3, 3))89 point = ray.intersect(ortho)90 assert point.isclose(Vec3(3, 10))91 def test_ray2d_angle(self):92 ray = ConstructionRay((10, 10), angle=HALF_PI)93 assert ray._is_vertical is True94 ray = ConstructionRay((10, 10), angle=0)95 assert ray._is_horizontal is True96 ray = ConstructionRay((10, 10), angle=math.pi / 4)97 assert math.isclose(ray._slope, 1.0)98 def test_bisectrix(self):99 ray1 = ConstructionRay((10, 10), angle=math.pi / 3)100 ray2 = ConstructionRay((3, -5), angle=math.pi / 2)101 ray3 = ConstructionRay((1, 1), angle=math.pi / 3)102 a = ray1.bisectrix(ray2)103 assert math.isclose(a._angle, 1.309, abs_tol=1e-4)104 assert math.isclose(a.yof(7), 12.80385, abs_tol=1e-4)105 with pytest.raises(ParallelRaysError):106 _ = ray1.bisectrix(ray3)107 def test_two_close_horizontal_rays(self):108 p1 = (39340.75302672016, 32489.73349764998)109 p2 = (39037.75302672119, 32489.73349764978)110 p3 = (38490.75302672015, 32489.73349764997)111 ray1 = ConstructionRay(p1, p2)112 ray2 = ConstructionRay(p2, p3)113 assert ray1.is_horizontal is True114 assert ray2.is_horizontal is True115 assert ray1.is_parallel(ray2) is True116 assert (117 math.isclose(ray1.slope, ray2.slope) is False...
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