Finish color band detection
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49b5eef6b2
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acec365fbe
122
3/contours.py
122
3/contours.py
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@ -13,35 +13,34 @@
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import copy
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import cv2 as cv
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import itertools as it
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import numpy as np
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def main():
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for k in range(1, 11):
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# for k in [5]:
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filename = 'set/A' + str(k) + '.png'
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src = cv.imread(filename)
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# filename = 'set/A' + str(k) + '.png'
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filename = raw_input()
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src = cv.imread(filename)
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box = find_resistor(src)
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box = find_resistor(src)
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if box is None:
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continue
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if box is None:
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print '221G'
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return
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src = crop_resistor(src, box)
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src = crop_resistor(src, box)
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if len(src) is 0:
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continue
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if len(src) is 0:
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print '221G'
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return
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simple = simplify_image(src)
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find_bands(simple)
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simple = simplify_image(src)
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code = find_bands(simple)
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if code[0] is 'G':
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code = code[::-1]
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# src = src.mean(3)
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# print src[0]
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# s = None
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# cv.reduce(src, s, 0, cv.REDUCE_SUM, cv.CV_32S)
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cv.imshow(str(k), simple)
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cv.waitKey()
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# cv.imwrite(str(k) + '.png', simple)
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code = code.replace("G", "") + '888'
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code = code[:3] + 'G'
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print code
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def rotate_pt(box, off, phi):
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@ -83,12 +82,12 @@ def find_resistor(img):
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kernel = np.ones((9, 9), np.uint8)
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img = cv.dilate(img, kernel, 2)
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img = cv.erode(img, kernel, 2)
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img, contours, hierarchy = cv.findContours(img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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img, contours, _ = cv.findContours(img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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rect = None
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if len(contours) is not 0:
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max_index, max_area = max(enumerate([cv.contourArea(x) for x in contours]), key = lambda x: x[1])
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max_contour = contours[max_index]
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ind, _ = max(enumerate([cv.contourArea(x) for x in contours]), key = lambda x: x[1])
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max_contour = contours[ind]
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rect = cv.minAreaRect(max_contour)
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rect = cv.boxPoints(rect)
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@ -121,6 +120,8 @@ def crop_resistor(img, box):
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h = box[1][1] - box[0][1]
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y += int(0.2 * h)
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h = int(0.6 * h)
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x += int(0.15 * w)
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w = int(0.7 * w)
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img = img[y:(y+h), x:(x+w)]
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return img
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@ -131,7 +132,7 @@ def simplify_image(img):
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# img = cv.filter2D(img, -1, kernel)
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h, w, c = img.shape
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simple = np.zeros((10, w, 3), np.uint8)
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simple = np.zeros((1, w, 3), np.uint8)
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for i in range(w):
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sumB = 0
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sumR = 0
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@ -141,8 +142,9 @@ def simplify_image(img):
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sumR += img[j, i, 1]
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sumG += img[j, i, 2]
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for k in range(10):
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simple[k, i] = [ sumB/h, sumR/h, sumG/h ]
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simple[0, i] = [ sumB/h, sumR/h, sumG/h ]
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# for k in range(10):
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# simple[k, i] = [ sumB/h, sumR/h, sumG/h ]
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N = 16;
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simple /= N;
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@ -156,17 +158,53 @@ def find_bands(img):
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hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV);
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h, w, c = img.shape
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# check_color(hsv[0, 0])
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# while hsv[0, i][0] >= 0 and i < w:
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while i < w:
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check_color(hsv[0, i])
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i += 1
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band_colors = []
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for i in range(w):
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band_colors.append(check_color(hsv[0, i]))
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band_colors = [k for k, g in it.groupby(band_colors) if sum(1 for _ in g) > 2]
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band_colors = filter(None, band_colors)
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return ''.join(band_colors)
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def check_color(px):
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print px
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pColors = {}
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pColors['0'] = np.mean(check_color_range(px[0], px[1], px[2], 'black') or[ 0 ])
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pColors['1'] = np.mean(check_color_range(px[0], px[1], px[2], 'brown') or[ 0 ])
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pColors['2'] = np.mean(check_color_range(px[0], px[1], px[2], 'red') or[ 0 ])
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pColors['3'] = np.mean(check_color_range(px[0], px[1], px[2], 'orange') or[ 0 ])
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pColors['4'] = np.mean(check_color_range(px[0], px[1], px[2], 'yellow') or[ 0 ])
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pColors['5'] = np.mean(check_color_range(px[0], px[1], px[2], 'green') or[ 0 ])
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pColors['6'] = np.mean(check_color_range(px[0], px[1], px[2], 'blue') or[ 0 ])
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pColors['7'] = np.mean(check_color_range(px[0], px[1], px[2], 'purple') or[ 0 ])
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pColors['8'] = np.mean(check_color_range(px[0], px[1], px[2], 'gray') or[ 0 ])
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pColors['9'] = np.mean(check_color_range(px[0], px[1], px[2], 'white') or[ 0 ])
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pColors['G'] = np.mean(check_color_range(px[0], px[1], px[2], 'gold') or[ 0 ])
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best = max(pColors, key=pColors.get)
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best = None if pColors[best] < 0.1 else best
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return best
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# def check_color_range(h, s, v, col):
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# if (h > col.lowH)
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def check_color_range(h, s, v, color_str):
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col = colors[color_str]
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if h >= col['lowH'] and h <= col['highH'] and s >= col['lowS'] and s <= col['highS'] and v >= col['lowV'] and v <= col['highV']:
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if color_str == 'red':
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diff = 180 - col['lowH']
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h = calculate_offset(h + diff, col['highH'] + diff, 0)
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else:
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h = calculate_offset(h, col['highH'], col['lowH'])
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s = calculate_offset(s, col['highS'], col['lowS'])
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v = calculate_offset(v, col['highV'], col['lowV'])
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return [h, s, v]
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return None
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def calculate_offset(x, high, low):
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m = (high - low) / 2.0
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return 1 - abs((x - m) / (high - m))
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colors = {
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@ -174,9 +212,9 @@ colors = {
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'lowH': 0,
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'highH': 179,
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'lowS': 0,
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'highS': 121,
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'highS': 10,
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'lowV': 0,
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'highV': 57,
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'highV': 40,
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},
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'brown': {
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'lowH': 3,
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@ -204,7 +242,7 @@ colors = {
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},
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'yellow': {
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'lowH': 20,
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'highH': 30,
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'highH': 32,
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'lowS': 190,
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'highS': 255,
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'lowV': 100,
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@ -213,7 +251,7 @@ colors = {
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'green': {
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'lowH': 45,
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'highH': 80,
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'lowS': 120,
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'lowS': 80,
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'highS': 255,
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'lowV': 30,
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'highV': 150,
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@ -238,9 +276,9 @@ colors = {
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'lowH': 0,
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'highH': 80,
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'lowS': 0,
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'highS': 85,
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'lowV': 100,
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'highV': 190,
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'highS': 30,
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'lowV': 90,
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'highV': 220,
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},
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'white': {
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'lowH': 0,
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@ -251,12 +289,12 @@ colors = {
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'highV': 255,
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},
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'gold': {
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'lowH': 15,
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'highH': 30,
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'lowH': 10,
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'highH': 20,
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'lowS': 160,
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'highS': 230,
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'lowV': 100,
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'highV': 200,
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'highV': 160,
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},
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}
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