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CS2-large.py
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CS2-large.py
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#!/usr/bin/env python
# coding: utf-8
# # Most of the code is shamefully copied from [theAIGuysCode](https://github.com/theAIGuysCode/YOLOv4-Cloud-Tutorial)
# In[1]:
from google.colab import drive
drive.mount('/content/drive')
# In[2]:
get_ipython().system('ln -s /content/drive/MyDrive /mygdrive')
# In[3]:
get_ipython().system('ls /mygdrive')
# In[4]:
# clone darknet repo
get_ipython().system('git clone https://github.com/AlexeyAB/darknet')
# In[5]:
# change makefile to have GPU and OPENCV enabled
get_ipython().run_line_magic('cd', 'darknet')
get_ipython().system('rm -rf /content/darknet/src/yolo_layer.c')
get_ipython().system('rm -rf /content/darknet/src/network.c')
get_ipython().system('rm -rf /content/darknet/src/network_kernels.cu')
get_ipython().system('cp /mygdrive/CS2/vedai/yolo/yolo_layer.c ./src/')
get_ipython().system('cp /mygdrive/CS2/vedai/yolo/network.c ./src/')
get_ipython().system('cp /mygdrive/CS2/vedai/yolo/network_kernels.cu ./src/')
get_ipython().system("sed -i 's/OPENCV=0/OPENCV=1/' Makefile")
#!sed -i 's/GPU=0/GPU=1/' Makefile
#!sed -i 's/CUDNN=0/CUDNN=1/' Makefile
#!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile
# In[6]:
# verify CUDA
get_ipython().system('/usr/local/cuda/bin/nvcc --version')
# In[7]:
# make darknet (builds darknet so that you can then use the darknet executable file to run or train object detectors)
get_ipython().system('make')
# In[8]:
# define helper functions
def imShow(path):
import cv2
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
image = cv2.imread(path)
height, width = image.shape[:2]
resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)
fig = plt.gcf()
fig.set_size_inches(18, 10)
plt.axis("off")
plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
plt.show()
# use this to upload files
def upload():
from google.colab import files
uploaded = files.upload()
for name, data in uploaded.items():
with open(name, 'wb') as f:
f.write(data)
print ('saved file', name)
# use this to download a file
def download(path):
from google.colab import files
files.download(path)
# In[9]:
import os
os.getcwd()
# In[10]:
# this is where my datasets are stored within my Google Drive (I created a yolov4 folder to store all important files for custom training)
get_ipython().system('ls /mygdrive/CS2/vedai/')
# In[11]:
# copy over both datasets into the root directory of the Colab VM (comment out test.zip if you are not using a validation dataset)
get_ipython().system('cp /mygdrive/CS2/vedai/data/sample_train_tiled.zip ../')
get_ipython().system('cp /mygdrive/CS2/vedai/data/sample_cv_tiled.zip ../')
# In[ ]:
# unzip the datasets and their contents so that they are now in /darknet/data/ folder
get_ipython().system('unzip ../sample_cv_tiled.zip -d data/')
get_ipython().system('unzip ../sample_train_tiled.zip -d data/')
get_ipython().system('rm -rf ../sample_cv_tiled.zip')
get_ipython().system('rm -rf ../sample_train_tiled.zip')
# In[ ]:
# download cfg to google drive and change its name
#!cp cfg/yolov4-custom.cfg /mygdrive/CS2/yolo/yolov4-obj.cfg
# In[ ]:
# to download to local machine (change its name to yolov4-obj.cfg once you download)
#download('cfg/yolov4-custom.cfg')
# Now you need to edit the .cfg to fit your needs based on your object detector. Open it up in a code or text editor to do so.
#
# If you downloaded cfg to google drive you can use the built in **Text Editor** by going to your google drive and double clicking on yolov4-obj.cfg and then clicking on the **Open with** drop down and selectin **Text Editor**.
#
# **(Image from previous tutorial so don't mind different file name)**
#
# 
#
# I recommend having **batch = 64** and **subdivisions = 16** for ultimate results. If you run into any issues then up subdivisions to 32.
#
# Make the rest of the changes to the cfg based on how many classes you are training your detector on.
#
# **Note:**
# I set my **max_batches = 6000**, **steps = 4800, 5400**, I changed the **classes = 1** in the three YOLO layers and **filters = 18** in the three convolutional layers before the YOLO layers.
#
# How to Configure Your Variables:
#
# width = 416
#
# height = 416
# **(these can be any multiple of 32, 416 is standard, you can sometimes improve results by making value larger like 608 but will slow down training)**
#
# max_batches = (# of classes) * 2000
# **(but no less than 6000 so if you are training for 1, 2, or 3 classes it will be 6000, however detector for 5 classes would have max_batches=10000)**
#
# steps = (80% of max_batches), (90% of max_batches)
# **(so if your max_batches = 10000, then steps = 8000, 9000)**
#
# filters = (# of classes + 5) * 3
# **(so if you are training for one class then your filters = 18, but if you are training for 4 classes then your filters = 27)**
#
#
# **Optional:** If you run into memory issues or find the training taking a super long time. In each of the three yolo layers in the cfg, change one line from random = 1 to **random = 0** to speed up training but slightly reduce accuracy of model. Will also help save memory if you run into any memory issues.
#
# In[13]:
# upload the custom .cfg back to cloud VM from Google Drive
get_ipython().system('cp /mygdrive/CS2/vedai/yolo/yolov4-obj.cfg ./cfg')
# In[14]:
# upload the obj.names and obj.data files to cloud VM from Google Drive
get_ipython().system('cp /mygdrive/CS2/vedai/obj.names ./data')
get_ipython().system('cp /mygdrive/CS2/vedai/obj.data ./data')
# In[15]:
get_ipython().system('cp /mygdrive/CS2/vedai/sample_train_tiled.txt ./data')
get_ipython().system('cp /mygdrive/CS2/vedai/sample_cv_tiled.txt ./data')
# In[16]:
# verify that the newly generated train.txt and test.txt can be seen in our darknet/data folder
get_ipython().system('ls data/')
# If you are uncertain whether generating the files worked and want to double check that everything went as planned, double click on **train.txt** on the left side File Explorer and it should look like this.
#
# 
#
# It will contain one line for each training image path.
# In[ ]:
get_ipython().system('wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137')
# In[ ]:
# train your custom detector! (uncomment %%capture below if you run into memory issues or your Colab is crashing)
# %%capture
get_ipython().system('./darknet detector train data/obj.data cfg/yolov4-obj.cfg yolov4.conv.137 -dont_show -map')
# **TRICK**: If for some reason you get an error or your Colab goes idle during training, you have not lost your partially trained model and weights! Every 100 iterations a weights file called **yolov4-obj_last.weights** is saved to **mydrive/yolov4/backup/** folder (wherever your backup folder is). This is why we created this folder in our Google drive and not on the cloud VM. If your runtime crashes and your backup folder was in your cloud VM you would lose your weights and your training progress.
#
# We can kick off training from our last saved weights file so that we don't have to restart! WOOHOO! Just run the following command but with your backup location.
# ```
# !./darknet detector train data/obj.data cfg/yolov4-obj.cfg /mydrive/yolov4/backup/yolov4-obj_last.weights -dont_show
# ```
# In[ ]:
# kick off training from where it last saved
get_ipython().system('./darknet detector train data/obj.data cfg/yolov4-obj.cfg /mygdrive/CS2/vedai/yolo/backup/yolov4-obj_last.weights -dont_show -map')
# # Step 6: Checking the Mean Average Precision (mAP) of Your Model
# If you didn't run the training with the '-map- flag added then you can still find out the mAP of your model after training. Run the following command on any of the saved weights from the training to see the mAP value for that specific weight's file. I would suggest to run it on multiple of the saved weights to compare and find the weights with the highest mAP as that is the most accurate one!
#
# **NOTE:** If you think your final weights file has overfitted then it is important to run these mAP commands to see if one of the previously saved weights is a more accurate model for your classes.
# In[17]:
get_ipython().system('./darknet detector map data/obj.data cfg/yolov4-obj.cfg /mygdrive/CS2/vedai/yolo/backup/yolov4-obj_best.weights')
# # Step 7: Run Your Custom Object Detector!!!
# You have done it! You now have a custom object detector to make your very own detections. Time to test it out and have some fun!
# In[28]:
# need to set our custom cfg to test mode
get_ipython().run_line_magic('cd', 'cfg')
get_ipython().system("sed -i 's/batch=64/batch=1/' yolov4-obj.cfg")
get_ipython().system("sed -i 's/subdivisions=16/subdivisions=1/' yolov4-obj.cfg")
get_ipython().run_line_magic('cd', '..')
# In[53]:
def convert_yolo_to_pascal(textfile, size, i, j, scale_h, scale_w):
annotations = pd.read_csv(textfile, sep = " ", names = ["class", "xc", "yc", "yolo_w", "yolo_h", "obj_name", "confidence"])
print(annotations.shape)
annotations["w"] = annotations["yolo_w"]*size
annotations["h"] = annotations["yolo_h"]*size
annotations["x"] = annotations["xc"]*size - annotations["w"]/2 + j*size
annotations["y"] = annotations["yc"]*size - annotations["h"]/2 + i*size
annotations["w"] = np.round_(annotations["w"]/scale_w)
annotations["h"] = np.round_(annotations["h"]/scale_h)
annotations["x"] = np.round_(annotations["x"]/scale_w)
annotations["y"] = np.round_(annotations["y"]/scale_h)
annotations = annotations[["class", "x", "y", "w", "h", "obj_name", "confidence"]]
return annotations
# In[60]:
get_ipython().run_cell_magic('time', '', '\nimport cv2\nimport os\nimport numpy as np\nimport pandas as pd\nimport json\nimport math\nimport time\n\n\n!rm -rf "/content/tiled_images/"\n!mkdir "/content/tiled_images/"\n\nim_name = "00000040.png"\n\nim = cv2.imread("/content/test_image/" + im_name)\n\nh, w, _ = im.shape\nsize = 512\n\ntiled_ims_list = []\n\nfor i in range(h//size):\n for j in range(w//size):\n tiled = im[i*size : (i+1)*size, j*size : (j+1)*size, :]\n tiled_im_name = "/content/tiled_images/" + im_name.split(".")[0] + "_" + str(i) + "_" + str(j) + ".png"\n tiled_ims_list.append(tiled_im_name)\n df = pd.DataFrame(tiled_ims_list)\n df.to_csv("/content/tiled_images.txt", index = False, header = False)\n cv2.imwrite(tiled_im_name, tiled)\n\n\nstart = time.time()\n!./darknet detector test data/obj.data cfg/yolov4-obj.cfg /mygdrive/CS2/vedai/yolo/backup/yolov4-obj_best.weights -ext_output -dont_show -out /content/result.json < /content/tiled_images.txt \nend = time.time()\n\nf = open("/content/result.json", )\nresult = json.load(f)\n\n\nfor res in result:\n #print(res)\n if len(res["objects"]) > 0:\n \n annot_path = res["filename"].replace(".png", ".txt")\n lines = []\n #print(annot_path)\n for obj in res["objects"]:\n line = []\n object_class = obj["class_id"]\n line.append(object_class)\n coords = obj["relative_coordinates"]\n line.extend([coords["center_x"], coords["center_y"], coords["width"], coords["height"], obj["name"], obj["confidence"]])\n lines.append(line)\n #print(lines) \n df = pd.DataFrame(lines)\n df.to_csv(annot_path, sep = " ", index = False, header = False)\n\n\n\nname = os.listdir("/content/test_image")[0]\nim = cv2.imread("/content/test_image/" + name)\n\nh, w, _ = im.shape\nh_new = math.ceil(h/size) * size\nw_new = math.ceil(w/size) * size\nscale_h = h_new/h\nscale_w = w_new/w\n\n\n\ntiled_images = "/content/tiled_images/"\n\n\nannotations = []\ncol_list = []\nfor i in range(math.ceil(h/size)):\n \n row_list = []\n for j in range(math.ceil(w/size)):\n tiled_image_name = tiled_images + name.split(".")[0] + "_" + str(i) + "_" + str(j) + ".png"\n #print(tiled_image_name)\n im_tiled = cv2.imread(tiled_image_name)\n annot_path = tiled_image_name.replace(".png", ".txt")\n if os.path.exists(annot_path):\n annotations.append(convert_yolo_to_pascal(annot_path, size, i, j, scale_h, scale_w))\n row_list.append(im_tiled)\n # Reference: https://www.geeksforgeeks.org/concatenate-images-using-opencv-in-python/\n row_im = cv2.hconcat(row_list)\n col_list.append(row_im)\n\nscaled_image = cv2.vconcat(col_list)\n#print(scaled_image.shape)\nreconstructed_image = cv2.resize(scaled_image, (w, h), interpolation = cv2.INTER_AREA)\n\ncv2.imwrite("reconstructed_" + name + ".png", reconstructed_image)\ndf = pd.concat(annotations, axis = 0)\ndf.to_csv("reconstructed_" + name + ".txt", sep=" ", index = False, header = False) \n#print(df.shape) \n\nfor index, row in df.iterrows():\n cv2.rectangle(reconstructed_image, (int(row["x"]), int(row["y"])), (int(row["x"]+row["w"]) , int(row["y"]+row["h"])), (255, 0, 0), 2)\n cv2.putText(reconstructed_image, row["obj_name"], (int(row["x"])-4, int(row["y"])-4), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)\ncv2_imshow(reconstructed_image)\n\nprint("time taken for detection", end-start)')