Add Slack Hook reference

messfinder/messfinder.py

@@ -1,39 +1,41 @@
 import argparse
 import os
 import time
+import json
 import numpy as np
 from pypylon import pylon
 import cv2
 
-# construct the argument parse and parse the arguments
+# Configuration
+slack_hook = "https://hooks.slack.com/services/String/String"
+
+# Parse the arguments.  argparse does the hard work for us.
 ap = argparse.ArgumentParser()
 ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory")
 ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
 ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression")
 args = vars(ap.parse_args())
 
-# load the COCO class labels our YOLO model was trained on
+# Load the COCO class labels our YOLO model was trained on
 labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
 LABELS = open(labelsPath).read().strip().split("\n")
 
-# initialize a list of colors to represent each possible class label
+# Initialize a list of colors to represent each possible class label
 np.random.seed(42)
 COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
 	dtype="uint8")
 
-# derive the paths to the YOLO weights and model configuration
+# Derive the paths to the YOLO weights and model configuration
 weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
 configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
 
-# load our YOLO object detector trained on COCO dataset (80 classes)
+# Load our YOLO object detector trained on COCO dataset (80 classes)
 print("[INFO] loading YOLO from disk...")
 net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
 
 
 camera = pylon.InstantCamera(pylon.TlFactory.GetInstance().CreateFirstDevice())
 
-numberOfImagesToGrab = 100
-#camera.StartGrabbingMax(numberOfImagesToGrab)
 camera.StartGrabbing(pylon.GrabStrategy_LatestImageOnly)
 converter = pylon.ImageFormatConverter()
 
@@ -49,69 +51,69 @@
         image = converter.Convert(grabResult)
         img = image.GetArray()
         (H, W) = img.shape[:2]
-        # determine only the *output* layer names that we need from YOLO
+        # Determine only the *output* layer names that we need from YOLO
         ln = net.getLayerNames()
         ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
-        # construct a blob from the input image and then perform a forward
+        # Construct a blob from the input image and then perform a forward
         # pass of the YOLO object detector, giving us our bounding boxes and
         # associated probabilities
         blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)
         net.setInput(blob)
         start = time.time()
         layerOutputs = net.forward(ln)
         end = time.time()
-        # show timing information on YOLO
+        # Show timing information on YOLO
         print("[INFO] YOLO took {:.6f} seconds".format(end - start))
 
-        # initialize our lists of detected bounding boxes, confidences, and
+        # Initialize our lists of detected bounding boxes, confidences, and
         # class IDs, respectively
         boxes = []
         confidences = []
         classIDs = []
 
         for output in layerOutputs:
-            # loop over each of the detections
+            # Loop over each of the detections
             for detection in output:
-                # extract the class ID and confidence (i.e., probability) of
+                # Extract the class ID and confidence (i.e., probability) of
                 # the current object detection
                 scores = detection[5:]
                 classID = np.argmax(scores)
                 confidence = scores[classID]
 
-                # filter out weak predictions by ensuring the detected
+                # Filter out weak predictions by ensuring the detected
                 # probability is greater than the minimum probability
                 if confidence > args["confidence"]:
-                    # scale the bounding box coordinates back relative to the
+                    # Scale the bounding box coordinates back relative to the
                     # size of the image, keeping in mind that YOLO actually
                     # returns the center (x, y)-coordinates of the bounding
                     # box followed by the boxes' width and height
                     box = detection[0:4] * np.array([W, H, W, H])
                     (centerX, centerY, width, height) = box.astype("int")
 
-                    # use the center (x, y)-coordinates to derive the top and
+                    # Use the center (x, y)-coordinates to derive the top and
                     # and left corner of the bounding box
                     x = int(centerX - (width / 2))
                     y = int(centerY - (height / 2))
 
-                    # update our list of bounding box coordinates, confidences,
+                    # Update our list of bounding box coordinates, confidences,
                     # and class IDs
                     boxes.append([x, y, int(width), int(height)])
                     confidences.append(float(confidence))
                     classIDs.append(classID)
 
-        # apply non-maxima suppression to suppress weak, overlapping bounding
+        # Apply non-maxima suppression to suppress weak, overlapping bounding
         # boxes
         idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])
 
-        # ensure at least one detection exists
+        # Ensure at least one detection exists
         if len(idxs) > 0:
-            # loop over the indexes we are keeping
+            # Loop over the indexes we are keeping
             for i in idxs.flatten():
-                # extract the bounding box coordinates
+                # Extract the bounding box coordinates
                 (x, y) = (boxes[i][0], boxes[i][1])
                 (w, h) = (boxes[i][2], boxes[i][3])
 
-                # draw a bounding box rectangle and label on the image
+                # Draw a bounding box rectangle and label on the image
                 color = [int(c) for c in COLORS[classIDs[i]]]
                 cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
                 text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])