deep_learning_object_detection.py

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 26 15:48:02 2017

@author: ziga
"""

# import the necessary packages
import numpy as np
import argparse
import cv2
 
# construct the argument parse and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required=True,
#	help="path to input image")
#ap.add_argument("-p", "--prototxt", required=True,
#	help="path to Caffe 'deploy' prototxt file")
#ap.add_argument("-m", "--model", required=True,
#	help="path to Caffe pre-trained model")
#ap.add_argument("-c", "--confidence", type=float, default=0.2,
#	help="minimum probability to filter weak detections")
#args = vars(ap.parse_args())




# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
#CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
#	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
#	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
#	"sofa", "train", "tvmonitor"]

cap = cv2.VideoCapture('TestShort.mp4')

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe("MobileNetSSD_deploy.prototxt.txt", "MobileNetSSD_deploy.caffemodel")
i = 0
d = 20
while(cap.isOpened()):
    ret, frame = cap.read()
    
    CLASSES = ["car"]
    COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
    
    
    
    # load the input image and construct an input blob for the image
    # by resizing to a fixed 300x300 pixels and then normalizing it
    # (note: normalization is done via the authors of the MobileNet SSD
    # implementation)
#    image = cv2.imread("snap_test.jpeg")
    if i % d == 0:
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843,
        	(300, 300), 127.5)
         
        # pass the blob through the network and obtain the detections and
        # predictions
        print("[INFO] computing object detections...")
        net.setInput(blob)
        detections = net.forward()
        
        # loop over the detections
        for i in np.arange(0, detections.shape[2]):
        	# extract the confidence (i.e., probability) associated with the
        	# prediction
        	confidence = detections[0, 0, i, 2]
         
        	# filter out weak detections by ensuring the `confidence` is
        	# greater than the minimum confidence
        	if confidence > 0.6:
        		# extract the index of the class label from the `detections`,
        		# then compute the (x, y)-coordinates of the bounding box for
        		# the object
        		idx = 0#int(detections[0, 0, i, 1])
        		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        		(startX, startY, endX, endY) = box.astype("int")
         
        		# display the prediction
        		label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
        		print("[INFO] {}".format(label))
        		cv2.rectangle(frame, (startX, startY), (endX, endY),
        			COLORS[idx], 2)
        		y = startY - 15 if startY - 15 > 15 else startY + 15
        		cv2.putText(frame, label, (startX, y),
        			cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) 
    i += 1
    cv2.imshow('frame',frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
#    if 0xFF == ord('q'):
#        break

cap.release()
cv2.destroyAllWindows()


#CLASSES = ["car"]
#COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
#
## load our serialized model from disk
#print("[INFO] loading model...")
#net = cv2.dnn.readNetFromCaffe("MobileNetSSD_deploy.prototxt.txt", "MobileNetSSD_deploy.caffemodel")
#
## load the input image and construct an input blob for the image
## by resizing to a fixed 300x300 pixels and then normalizing it
## (note: normalization is done via the authors of the MobileNet SSD
## implementation)
#image = cv2.imread("snap_test.jpeg")
#(h, w) = image.shape[:2]
#blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843,
#	(300, 300), 127.5)
# 
## pass the blob through the network and obtain the detections and
## predictions
#print("[INFO] computing object detections...")
#net.setInput(blob)
#detections = net.forward()
#
## loop over the detections
#for i in np.arange(0, detections.shape[2]):
#	# extract the confidence (i.e., probability) associated with the
#	# prediction
#	confidence = detections[0, 0, i, 2]
# 
#	# filter out weak detections by ensuring the `confidence` is
#	# greater than the minimum confidence
#	if confidence > 0.2:
#		# extract the index of the class label from the `detections`,
#		# then compute the (x, y)-coordinates of the bounding box for
#		# the object
#		idx = 0#int(detections[0, 0, i, 1])
#		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
#		(startX, startY, endX, endY) = box.astype("int")
# 
#		# display the prediction
#		label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
#		print("[INFO] {}".format(label))
#		cv2.rectangle(image, (startX, startY), (endX, endY),
#			COLORS[idx], 2)
#		y = startY - 15 if startY - 15 > 15 else startY + 15
#		cv2.putText(image, label, (startX, y),
#			cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) 
#
#	
## show the output image
#cv2.imshow("Output", image)
#cv2.waitKey(0)