read_video.py

importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()
importcv2
importnumpyasnp

importtime
importsys

CONFIDENCE=0.5
SCORE_THRESHOLD=0.5
IOU_THRESHOLD=0.5
config_path="cfg/yolov3.cfg"
weights_path="weights/yolov3.weights"
font_scale=1
thickness=1
labels=open("data/coco.names").read().strip().split("\n")
colors=np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net=cv2.dnn.readNetFromDarknet(config_path, weights_path)

ln=net.getLayerNames()
ln= [ln[i[0] -1] foriinnet.getUnconnectedOutLayers()]
# read the file from the command line
video_file=sys.argv[1]
cap=cv2.VideoCapture(video_file)
_, image=cap.read()
h, w=image.shape[:2]
fourcc=cv2.VideoWriter_fourcc(*"XVID")
out=cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
whileTrue:
_, image=cap.read()

h, w=image.shape[:2]
blob=cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start=time.perf_counter()
layer_outputs=net.forward(ln)
time_took=time.perf_counter() -start
print("Time took:", time_took)
boxes, confidences, class_ids= [], [], []

# loop over each of the layer outputs
foroutputinlayer_outputs:
# loop over each of the object detections
fordetectioninoutput:
# extract the class id (label) and confidence (as a probability) of
# the current object detection
scores=detection[5:]
class_id=np.argmax(scores)
confidence=scores[class_id]
# discard weak predictions by ensuring the detected
# probability is greater than the minimum probability
ifconfidence>CONFIDENCE:
# 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[: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
# 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,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
class_ids.append(class_id)

# perform the non maximum suppression given the scores defined before
idxs=cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD)

font_scale=1
thickness=1

# ensure at least one detection exists
iflen(idxs) >0:
# loop over the indexes we are keeping
foriinidxs.flatten():
# 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
color= [int(c) forcincolors[class_ids[i]]]
cv2.rectangle(image, (x, y), (x+w, y+h), color=color, thickness=thickness)
text=f"{labels[class_ids[i]]}: {confidences[i]:.2f}"
# calculate text width & height to draw the transparent boxes as background of the text
 (text_width, text_height) =cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
text_offset_x=x
text_offset_y=y-5
box_coords= ((text_offset_x, text_offset_y), (text_offset_x+text_width+2, text_offset_y-text_height))
overlay=image.copy()
cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
# add opacity (transparency to the box)
image=cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
# now put the text (label: confidence %)
cv2.putText(image, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

out.write(image)
cv2.imshow("image", image)

iford("q") ==cv2.waitKey(1):
break


cap.release()
cv2.destroyAllWindows()