upload .pth and python script

mexico_5_column_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6e09ef4426541bc2d42b583f15e1e32c7a458d228dbe292d4ff12b9dc602f5e
+size 351208539

mexico_census_5column_segment_josh.py

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+# Need to set up out dir and also how it is being split. Bounding boxes?
+
+# Some basic setup
+# Setup detectron2 logger
+from sys import argv
+import detectron2
+from detectron2.utils.logger import setup_logger
+setup_logger()
+
+# import some common libraries
+import matplotlib.pyplot as plt
+import numpy as np
+#from google.cloud import storage
+from io import BytesIO
+import cv2
+from glob import glob
+import subprocess
+from shlex import quote
+import csv
+from tqdm import tqdm
+
+
+
+# import some common detectron2 utilities
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog, DatasetCatalog
+from detectron2.structures import BoxMode #I added this
+from detectron2.evaluation import COCOEvaluator, inference_on_dataset
+from detectron2.data import build_detection_test_loader
+import statistics 
+
+import random
+from detectron2.engine import DefaultTrainer
+from detectron2.config import get_cfg
+import os
+import traceback
+
+numdir = argv[1]
+album = argv[2]
+
+
+# Set Up Models
+# the cfg object here is an instantiation of the model. The merge_from_file function gets arguments from a default YAML 
+# file to configure the model. The functions that follow update certain arguments that were set to default from the YAML file.
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")) # define model
+cfg.MODEL.WEIGHTS = r"C:\Users\Chase\OneDrive\Documents\service-project\mexico_5_column_weights.pth" # SET UP WEIGHTS HERE
+cfg.MODEL.DEVICE = 'cpu'
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 5 # 5 classes (5 columns in this instance, but you may have more depending on what you are doing)
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8  # set the testing threshold for this model (confidence threshold)
+predictor = DefaultPredictor(cfg)
+
+cfg2 = get_cfg()
+cfg2.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg2.MODEL.WEIGHTS = r"C:\Users\Chase\OneDrive\Documents\service-project\mexico_5_column_weights.pth" # SET UP WEIGHTS HERE
+cfg2.MODEL.DEVICE = 'cpu'
+cfg2.MODEL.ROI_HEADS.NUM_CLASSES = 1  # 1 class (Cause of Death in this instance, but you may have more depending on what you are doing)
+cfg2.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8  # set the testing threshold for this model
+predictor2 = DefaultPredictor(cfg2)
+
+#FUNCTIONS
+#This function returns a list of vertical lines found within the image passed to the function. 
+def get_vertical_lines(img, width=385, line_height=2000, circle = 155): #this function takes as parameter an image and default integers. It returns a list. 
+  '''This function takes an image and default integers as parameters and outputs a list.'''
+  ys=[]
+  keepers=[]
+  n=0
+  # convert between RGB/BGR and grayscale
+  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+  # use an Adaptive Thresholding approach where the threshold value = Gaussian weighted sum of the neighborhood values - constant value. 
+  # In other words, it is a weighted sum of the blockSize^2 neighborhood of a point minus the constant.
+  # in this example, we are setting the maximum threshold value as 255 with the block size of 155 (as set in the "circle" parameter) and the
+  # constant is 2 (as specified in the last argument)
+  edges = ~cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,circle,2)
+  # create a 3x3 matrix of ones.
+  # An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.
+  kernel = np.ones((3, 3), np.uint8)
+  # The basic idea of erosion is just like soil erosion only, it erodes away the boundaries of foreground object (Always try to keep foreground in white). It is normally performed on binary images. It needs two inputs, one is our original image, second one is called structuring element or kernel which decides the nature of operation. A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded (made to zero).
+  th2 = cv2.erode(edges, kernel, iterations=1)
+  # create a 1x7 matrix of ones.
+  kernel = np.ones((1, 7), np.uint8)
+  # The basic idea of dilation is accentuating the features of the images. Whereas erosion is used to reduce the amount of noise in the image, dilation is used to enhance the features of the image. 
+  th3 = cv2.dilate(th2, kernel, iterations=1)
+  # The Hough Transform is a method that is used in image processing to detect any shape, if that shape can be represented in mathematical form. It can detect the shape even if it is broken or distorted a little bit. 
+  # Any line can be represented in these two terms, (r, ?). Let rows denote the r and columns denote the (?) theta.
+  # First parameter, Input image should be a binary image, so apply threshold edge detection before finding applying hough transform. In this instance, the "th3" variable will represent our edges.
+  # Second and third parameters are r and ?(theta) accuracies respectively.
+  lines = cv2.HoughLines(th3,1,np.pi/180, line_height) 
+  for line in range(len(lines)):
+      if lines[line][0][1]>-.1 and lines[line][0][1]<.1:
+          keepers.append(lines[line])
+          n+=1
+  for line2 in range(n):
+      for rho,theta in keepers[line2]:
+          b = np.sin(theta)
+          y0 = b*rho
+          a = np.cos(theta)
+          x0 = a*rho
+          x1 = int(x0 + 30*(-b))
+          y1 = int(y0 + 30*(a))
+          x2 = int(x0 - 30*(-b))
+          y2 = int(y0 - 30*(a))
+          slope = (y2-y1) / (x2-x1)
+          intercept = y1 - (slope * x1)
+          side = slope * width + intercept
+          ys.append(intercept)
+          ys.append(side)
+  return ys
+
+#This function returns a list of horizontal lines found in the image passed into the function. 
+def get_horizontal_lines(img, width=385, line_width=150, circle = 155): #this function takes as parameter and image and default integers. It returns a list. 
+  ys=[]
+  keepers=[]
+  n=0
+  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #converts image to grayscale
+  edges = ~cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,circle,2)#applies threshold on image
+  kernel = np.ones((3, 3), np.uint8)
+  th2 = cv2.erode(edges, kernel, iterations=1)
+  kernel = np.ones((7, 1), np.uint8)
+  th3 = cv2.dilate(th2, kernel, iterations=1)
+  lines = cv2.HoughLines(th3,1,np.pi/180, line_width)
+  for line in range(len(lines)):
+      if lines[line][0][1]>1.45 and lines[line][0][1]<1.7:
+          keepers.append(lines[line])
+          n+=1
+  for line2 in range(n):
+      for rho,theta in keepers[line2]:
+          b = np.sin(theta)
+          y0 = b*rho
+          a = np.cos(theta)
+          x0 = a*rho
+          x1 = int(x0 + 30*(-b))
+          y1 = int(y0 + 30*(a))
+          x2 = int(x0 - 30*(-b))
+          y2 = int(y0 - 30*(a))
+          slope = (y2-y1) / (x2-x1)
+          intercept = y1 - (slope * x1)
+          side = slope * width + intercept
+          ys.append(intercept)
+          ys.append(side)
+  return ys
+
+
+def crop_bot(img, width = 385, line_width_crop = 300):
+    temp=img[-50:,0:width]
+    try:
+      ys = get_horizontal_lines(temp, line_width = line_width_crop)
+      return img[:img.shape[0]-50+int(np.mean(ys)),0:width]
+    except:
+      return img
+
+
+def make_snippets(img, ys, rows = 50, pixels_per_row = 60, pixels_on_either_side = 15, file_path = "", column = "lit", add_to_end = 0):
+  start = 0
+  for y in range(rows):
+    finish = start + pixels_per_row
+    x_check = start - pixels_on_either_side
+    x_check2 = start + pixels_on_either_side
+    y_check = finish - pixels_on_either_side
+    y_check2 = finish + pixels_on_either_side
+    newlist = [x for x in ys if (x > x_check) & (x < x_check2)]
+    newlist2 = [x for x in ys if (x > y_check) & (x < y_check2)]
+    if len(newlist)!=0:
+      start = round(statistics.median(newlist))
+    if len(newlist2)!=0:
+      finish = round(statistics.median(newlist2))
+    if y==rows-1:
+      snippet=img[start:]
+    elif y!=rows-1:
+      snippet=img[start:finish]
+    start = finish
+    cv2.imwrite(file_path + "_" + column + "_row_" + str(y+1) + ".jpg", snippet)
+
+# CODE THAT DOES THE SEGMENTATION
+bad=[]
+files = os.listdir(r'C:/Users/Chase/OneDrive/Documents/34/d32/')[:24]
+#files = random.sample(os.listdir(), 4)
+for d in tqdm(files):
+    if d[-4:] == ".jpg":
+      try:
+        out_dir = "C:/Users/Chase/OneDrive/Documents/service-project/{}".format(numdir + "/" + album)
+        im = cv2.imread(d)
+        outputs = predictor(im)
+        objects = outputs["instances"].pred_classes
+        boxes = outputs["instances"].pred_boxes
+        masks = outputs["instances"].pred_masks
+        boxes_np = boxes.tensor.cpu().numpy()
+        obj_np = objects.cpu().numpy()
+        masks_np = masks.cpu().numpy()
+        m = 0
+        for box in range(len(boxes_np)):
+          left = int(boxes_np[box][0])
+          top = int(boxes_np[box][1])
+          right = int(boxes_np[box][2])
+          bottom = int(boxes_np[box][3])
+          cropped_array = im[top:bottom,left:right]
+          mask = masks_np[m][top:bottom,left:right]
+          h , w = mask.shape
+          tl = int(np.argwhere(mask[200]==True)[0])
+          bl = int(np.argwhere(mask[h-200]==True)[0])
+          white1 = np.zeros([h,w,3],dtype=np.uint8)
+          white1.fill(255)
+          white2 = np.zeros([h,w,3],dtype=np.uint8)
+          white2.fill(255)
+          change = (tl-bl)/h
+          white3= (cropped_array * mask[..., None]) + (white1 * ~mask[..., None]) 
+          for i in range(h):
+            start = int(tl - i*change)
+            if len(np.argwhere(mask[i]==True))>0:
+              last = int(np.argwhere(mask[i]==True)[-1])
+            elif len(np.argwhere(mask[i]==True))==0:
+              last = w-start
+            white2[i][0:last-start] = white3[i][start:last]
+          if obj_np[m] == 0:
+              white3=white2[:,0:60]
+              outputs2 = predictor2(white3)
+              boxes2 = outputs2["instances"].pred_boxes
+              boxes_np2 = boxes2.tensor.cpu().numpy()
+              bottom2 = int(boxes_np2[0][3])
+              no_top=white3[bottom2:,:]
+              no_bot_or_top = crop_bot(no_top, width = 60, line_width_crop= 45)
+              no_bot_or_top = cv2.resize(no_bot_or_top,(60,3000))
+              ys = get_horizontal_lines(no_bot_or_top,width=60, line_width=45)
+              make_snippets(no_bot_or_top, ys, rows=50, pixels_per_row=60,  pixels_on_either_side = 15, file_path = out_dir + "/" + d[:-4], column= 'lit1')
+          elif obj_np[m] == 1:
+              white3=white2[:,0:60]
+              outputs2 = predictor2(white3)
+              boxes2 = outputs2["instances"].pred_boxes
+              boxes_np2 = boxes2.tensor.cpu().numpy()
+              bottom2 = int(boxes_np2[0][3])
+              no_top=white3[bottom2:,:]
+              no_bot_or_top = crop_bot(no_top, width = 60, line_width_crop= 45)
+              no_bot_or_top = cv2.resize(no_bot_or_top,(60,3000))
+              ys = get_horizontal_lines(no_bot_or_top,width=60, line_width=45)
+              make_snippets(no_bot_or_top, ys, rows=50, pixels_per_row=60,  pixels_on_either_side = 15, file_path = out_dir + "/" + d[:-4], column= 'lit2')
+          elif obj_np[m] == 2:
+              white3=white2[:,0:60]
+              outputs2 = predictor2(white3)
+              boxes2 = outputs2["instances"].pred_boxes
+              boxes_np2 = boxes2.tensor.cpu().numpy()
+              bottom2 = int(boxes_np2[0][3])
+              no_top=white3[bottom2:,:]
+              no_bot_or_top = crop_bot(no_top, width = 60, line_width_crop= 45)
+              no_bot_or_top = cv2.resize(no_bot_or_top,(60,3000))
+              ys = get_horizontal_lines(no_bot_or_top,width=60, line_width=45)
+              make_snippets(no_bot_or_top, ys, rows=50, pixels_per_row=60,  pixels_on_either_side = 15, file_path = out_dir + "/" + d[:-4], column= 'lang1')
+          elif obj_np[m] == 3:
+              white3=white2[:,0:350]
+              outputs2 = predictor2(white3)
+              boxes2 = outputs2["instances"].pred_boxes
+              boxes_np2 = boxes2.tensor.cpu().numpy()
+              bottom2 = int(boxes_np2[0][3])
+              no_top=white3[bottom2:,:]
+              no_bot_or_top = crop_bot(no_top, line_width_crop=265)
+              no_bot_or_top = cv2.resize(no_bot_or_top,(350,3000))
+              ys = get_horizontal_lines(no_bot_or_top,width=350, line_width=265)
+              make_snippets(no_bot_or_top, ys, rows=50, pixels_per_row=60, pixels_on_either_side = 15, file_path = out_dir + "/" + d[:-4], column= 'lang2')
+          elif obj_np[m] == 4:
+              white3=white2[:,0:225]
+              outputs2 = predictor2(white3)
+              boxes2 = outputs2["instances"].pred_boxes
+              boxes_np2 = boxes2.tensor.cpu().numpy()
+              bottom2 = int(boxes_np2[0][3])  
+              no_top=white3[bottom2:,:]
+              no_bot_or_top = crop_bot(no_top, line_width_crop=300)
+              no_bot_or_top = cv2.resize(no_bot_or_top,(225,3000))
+              ys = get_horizontal_lines(no_bot_or_top,width=225, line_width=150)
+              make_snippets(no_bot_or_top, ys, rows=50, pixels_per_row=60, pixels_on_either_side = 15, file_path = out_dir + "/" + d[:-4], column= 'rel')
+          m += 1
+      except KeyboardInterrupt:
+        exit(1)
+      except:
+        bad.append(d)
+        traceback.print_exc() 
+        print("image failed: " + d)
+        pass
+
+print("Percent Error: " + str(len(bad)/len(files)))
+print(bad)
+with open(f'C:/Users/Chase/OneDrive/Documents/service-project/{numdir}.csv', 'a') as output:
+ # /home/jmorri33/fsl_groups/fslg_census/compute/projects/Mexico_Census/error_img/mexico_error_62.csv
+# ../../../../error_img
+    writer = csv.writer(output, delimiter=',')
+    writer.writerow(bad)