in progress

src/rove_opi/package.xml

@@ -8,6 +8,7 @@
   <license>TODO: License declaration</license>
 
   <exec_depend>rclpy</exec_depend>
+  <exec_depend>geometry_msgs</exec_depend>
   <test_depend>ament_copyright</test_depend>
   <test_depend>ament_flake8</test_depend>
   <test_depend>ament_pep257</test_depend>

src/rove_opi/rove_opi/main.old.py

@@ -0,0 +1,208 @@
+import json
+from typing import List
+import cv2
+import numpy as np
+import rclpy
+
+
+from cinput import cinput, ynValidator
+from common import CONVERTED_PATH, KEY_ESC, KEY_LEFT, KEY_RIGHT, STATS_PATH, AccuracyStatsDict
+from image_processors import OPIFinder, contrasters, edge_detectors, edge_filters, normalizers, shape_identifiers, shape_postprocessors, shape_selectors, trapezoid_finders, trapezoid_rectifiers, thresholders
+from metadata import ImageBrowser
+from old_processing import filterThresh, final, noProcess, normalize, ocr, orangeness1, orangeness1Thresh, red, straighten
+
+from utils import convert, ensureExists
+
+class ImageBrowserBehavior:
+    def __init__(self, imgs: List[cv2.Mat], behaviors:OPIFinder):
+        self.imgs = imgs
+        self.index = 0
+        self.stepIndex = 0
+        self.colors = []
+        self.img = imgs[0]
+        self.behaviors = behaviors
+        # v
+        self.subscription = self.create_subscription(
+            Image,
+            'Image',
+            self.listener_callback,
+            10)
+        self.subscription
+
+    def listener_callback(self, msg):
+        self.get_logger().info('I heard: "%s"' % msg.data)
+
+
+    def mouseEvent(self, event, x, y, flags, param):
+        if event == cv2.EVENT_LBUTTONDBLCLK:
+            self.colors.append(self.img[y,x])
+            arr = np.array(self.colors)
+            print(arr.mean(0))
+
+    def loop(self):
+        k = cv2.waitKey(10)
+        if k == -1:
+            return
+        b = self.behaviors.getStepNumber(self.stepIndex)
+        if k == KEY_LEFT:
+            self.index = len(self.imgs) - 1 if self.index == 0 else self.index - 1
+        if k == KEY_RIGHT:
+            self.index = (self.index + 1) % len(self.imgs)
+        # if k == KEY_DOWN:
+        #     # if not b.debug:
+        #     #     b.destroyWindow()
+        #     while True:
+        #         self.stepIndex = len(self.behaviors.choices) - 1 if self.stepIndex == 0 else self.stepIndex - 1
+        #         b = self.behaviors.getStepNumber(self.stepIndex)
+        #         if b is not None:
+        #             break
+        # if k == KEY_UP:
+        #     # if not b.debug:
+        #     #     b.destroyWindow()
+        #     while True:
+        #         self.stepIndex = (self.stepIndex + 1) % len(self.behaviors.choices)
+        #         b = self.behaviors.getStepNumber(self.stepIndex)
+        #         if b is not None:
+        #             break
+        # if k == ord(' '):
+            # b.notifiedDebug ^= True
+        if k == KEY_ESC:
+            exit(0)
+
+        # dbg = b.debug
+        # b.debug = True
+        self.behaviors.find2(self.imgs[self.index])
+        # b.debug = dbg
+
+def main(args=None):
+    rclpy.init(args=args)
+
+    publisher = ImageBrowserBehavior()
+
+    # Mettre loop ici genre ros spin subscriber
+    # utiliser rcutie pour verifier
+    # Get image from ROS
+    img = ...
+    best = None
+
+    _, (indices, trapezoids, scores) = finder.find2(img)
+    if len(trapezoids) > 0:
+        selectedScores = np.array([scores[i] for i in indices])
+
+        bestIdx = np.where(selectedScores == selectedScores.max())
+        best = trapezoids[bestIdx]
+
+
+    # Send trapezoid to ROS if found
+
+    rclpy.spin(publisher)
+
+    # Destroy the node explicitly
+    # (optional - otherwise it will be done automatically
+    # when the garbage collector destroys the node object)
+    publisher.destroy_node()
+    rclpy.shutdown()
+
+if __name__ == "__main__":
+    ensureExists()
+    convert()
+
+    imgs = [cv2.imread(str(p)) for p in CONVERTED_PATH.iterdir()]
+
+    # ib = ImageBrowser(imgs)
+    # ib.processingMethods.append(noProcess)
+    # ib.processingMethods.append(normalize)
+    # # ib.processingMethods.append(thresh)
+    # # ib.processingMethods.append(gray)
+    # ib.processingMethods.append(red)
+    # ib.processingMethods.append(orangeness1)
+    # ib.processingMethods.append(orangeness1Thresh)
+    # # ib.processingMethods.append(orangeness2)
+    # # ib.processingMethods.append(canny)
+    # ib.processingMethods.append(filterThresh)
+    # ib.processingMethods.append(straighten)
+    # ib.processingMethods.append(ocr)
+    # ib.processingMethods.append(final)
+
+    finder = OPIFinder()
+    finder.steps['normalize']['simple'] = normalizers.SimpleNormalizer()
+
+    finder.steps['orangeness']['meanRGBOffset'] = contrasters.MeanRGBOffset()
+
+    finder.steps['threshold']['simple'] = thresholders.SimpleThreshold(50)
+
+    finder.steps['edgeDetect']['simple'] = edge_detectors.SimpleEdgeDetect()
+
+    finder.steps['edgeFilter']['simple'] = edge_filters.SimpleEdgeFilter(0.008, 0.9, 0.001)
+
+    finder.steps['postProcessShapes']['fillExpand'] = shape_postprocessors.FillPostProcess(finder.steps['edgeDetect']['simple'], finder.steps['edgeFilter']['simple'], 1.1)
+    finder.steps['postProcessShapes']['rectMerge'] = shape_postprocessors.RectMergePostProcess(finder.steps['edgeFilter']['simple'], 1.1)
+
+    finder.steps['findTrapezoids']['simple'] = trapezoid_finders.NearestContour()
+
+    finder.steps['rectifyTrapezoids']['simple'] = trapezoid_rectifiers.BasicRectify()
+
+    redContraster = contrasters.Red(normalizers.SimpleNormalizer())
+
+    finder.steps['scoreShapes']['simple'] = shape_identifiers.BasicShapeIdentifier(0.035, 0.04, redContraster)
+
+    # finder.steps['selectShapes']['logistic'] = LogisticRegressionSelector()
+
+    factors1 = {
+        'lineBorder' : 0.4,
+        'noLineBorder' : 0.2,
+        'topBottomBorders' : 0.1,
+        'noTopBottomBorders' : 0.1,
+        'bordersLighter' : 0.1,
+        'noBordersLighter' : 0.1,
+        'topOCR' : 0.5,
+        'bottomOCR' : 0.8,
+        'valid' : 1.51,
+    }
+
+    factors2 = {
+        'lineBorder' : 0.5,
+        'noLineBorder' : 0.1,
+        'topBottomBorders' : 0.4,
+        'noTopBottomBorders' : 0.1,
+        'bordersLighter' : 0.2,
+        'noBordersLighter' : 0.1,
+        'topOCR' : 0.2,
+        'bottomOCR' : 0.4,
+        'valid' : 1.51,
+    }
+
+    finder.steps['selectShapes']['hardCoded'] = shape_selectors.HardCodedSelector(factors2, 1.51)
+    finder.steps['selectShapes']['hardCoded'] = shape_selectors.HardCodedSelector(factors1, 1.51)
+    with (STATS_PATH / 'accuracy.json').open('r') as rd:
+        try:
+            results: AccuracyStatsDict = json.load(rd)
+            weights = {(k): v['f1_score'] for k, v in results['results'].items()}
+            finder.steps['selectShapes']['aggressive'] = shape_selectors.AggressiveLowFalsePos(0.1, weights, 1.51)
+            # finder.steps['selectShapes']['aggressive'].debug = True
+            # vv = cinput("Calibration desired precision (defailt = 0.2): ]0 - 1]  ", float, lambda v: 0 < v <= 1)
+            finder.steps['selectShapes']['aggressive'].calibrate(results, .5)
+
+        except (AttributeError, KeyError):
+            finder.steps['selectShapes']['aggressive'] = shape_selectors.AggressiveLowFalsePos(0.2, 1, 0.1)
+            # finder.steps['selectShapes']['aggressive'].debug = True
+    # for t, tt in finder.steps.items():
+    #     for p, pp in tt.items():
+    #         pp.debug = True
+
+    # finder.find(imgs[2])
+    # ovw = cinput("Overwite? (y/n)", str, ynValidator) == 'y'
+    # test = cinput("Re-test the algorythm on the images? (y/n)", str, ynValidator) == 'y'
+    # validity = cinput("Input validity? (y/n)", str, ynValidator) == 'y'
+    # finder.accuracy(imgs, ovw, test, validity, finder.steps['selectShapes']['aggressive'].thresholds)
+    # finder.accuracy(imgs, False, True, False, finder.steps['selectShapes']['aggressive'].thresholds)
+    # finder.speedBenchmark(imgs, 50, (720, 1280))
+    # finder.speedBenchmark(imgs, 50, (480, 640))
+
+    ib2 = ImageBrowserBehavior(imgs, finder)
+
+
+    while True:
+        # ib.loop()
+        ib2.loop()
+