if subject_ids == []:
continue
s = subject_ids[0]
for task in annotations:
if task["task"] == "T2":
try:
m = task["value"][0]["points"]
if s not in markings:
markings[s] = [m]
else:
markings[s].append(m)
except (KeyError,IndexError) as e:
pass
for subject_id,points in markings.items():
fname = jungle.__image_setup__(subject_id)
image_file = cbook.get_sample_data(fname)
image = plt.imread(image_file)
fig, ax1 = plt.subplots(1, 1)
ax1.imshow(image)
all_points = []
for a in points:
for b in a:
all_points.append((b["x"],b["y"]))
if len(all_points) < 6:
plt.close()
continue
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
import numpy as np
import csv
import json
from aggregation_api import AggregationAPI
import matplotlib.cbook as cbook
import sys
# subject_id = int(sys.argv[1])
# minimum_users = int(sys.argv[2])
subject_id = 511723
project = AggregationAPI(348,public_panoptes_connection=True)
subject_image = project.__image_setup__(subject_id)
for minimum_users in [8]:
print minimum_users
fig, ax = plt.subplots()
image_file = cbook.get_sample_data(subject_image)
image = plt.imread(image_file)
# fig, ax = plt.subplots()
im = ax.imshow(image)
all_vertices = []
with open("/tmp/348/4_ComplexAMOS/vegetation_polygons_heatmap.csv","rb") as f:
polygon_reader = csv.reader(f)
def closest(aim,points):
distance = float("inf")
best_point = None
for p in points:
d = math.sqrt((aim[0]-p[0])**2+(aim[1]-p[1])**2)
if d < distance:
distance = d
best_point = p
return tuple(best_point)
# fname = butterflies.__image_setup__(1120709)[0]
for subject_id in butterflies.__get_subjects_in_workflow__(874):
fname = butterflies.__image_setup__(subject_id)[0]
print(fname)
image_file = cbook.get_sample_data(fname)
image = plt.imread(image_file)
plt.imshow(image)
plt.show()
image = np.asarray(image,dtype=np.int)
d1 = np.power(image[:,:,0] - 211,2)
d2 = np.power(image[:,:,1] - 44,2)
d3 = np.power(image[:,:,2] - 124,2)
overall_d = np.power(d1+d2+d3,0.5)
overall_d = np.uint8(255 - cv2.normalize(overall_d,overall_d,0,255,cv2.NORM_MINMAX))
from __future__ import print_function
import matplotlib
matplotlib.use('WXAgg')
from aggregation_api import AggregationAPI
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import cv2
import numpy as np
butterflies = AggregationAPI(1150, "development")
butterflies.__setup__()
# fname = butterflies.__image_setup__(1120709)[0]
fname = butterflies.__image_setup__(1500825)[0]
image_file = cbook.get_sample_data(fname)
image = plt.imread(image_file)
res = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# kernely = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2))
# dy = cv2.Sobel(res,cv2.CV_16S,0,2)
# dy = cv2.convertScaleAbs(dy)
# cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX)
# ret,close = cv2.threshold(dy,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#
# t= image
# _,contour, hier = cv2.findContours(close.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# for cnt in contour:
# x,y,w,h = cv2.boundingRect(cnt)
# if ((w/h)>5) and (w>130) and (w < 160):
__author__ = 'ggdhines'
from aggregation_api import AggregationAPI
import matplotlib.cbook as cbook
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from sklearn.cluster import DBSCAN
import numpy as np
import math
import cv2
# subject_id = 918463
subject_id = 917160
project = AggregationAPI(11,"development")
fname = project.__image_setup__(subject_id)
#478758
#917091
def convex_hull(points):
"""Computes the convex hull of a set of 2D points.
Input: an iterable sequence of (x, y) pairs representing the points.
Output: a list of vertices of the convex hull in counter-clockwise order,
starting from the vertex with the lexicographically smallest coordinates.
Implements Andrew's monotone chain algorithm. O(n log n) complexity.
"""
# Sort the points lexicographically (tuples are compared lexicographically).
# Remove duplicates to detect the case we have just one unique point.
points = sorted(list(set(points)))
